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This entry-level guide, designed for database and data warehouse administrators and architects, will help you navigate the Snowflake interface and introduce you to some of our core capabilities. \u003Ca href=\"https://trial.snowflake.com\"\u003ESign up for a free 30-day trial of Snowflake\u003C/a\u003E and follow along with this lab. Once we cover the basics, you'll be ready to start processing your own data and diving into Snowflake's more advanced features!\u003C/p\u003E\n","\u003Ch3\u003EFree Virtual Hands-on Lab\u003C/h3\u003E\n","\u003Cp\u003EThis Snowflake Guide is available as a free, instructor-led Virtual Hands on Lab. \u003Ca href=\"/virtual-hands-on-lab/\"\u003ESign up for the VHOL today\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUse of the \u003Ca href=\"https://trial.snowflake.com\"\u003ESnowflake free 30-day trial environment\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL, database concepts, and objects\u003C/li\u003E\u003Cli\u003EFamiliarity with CSV (comma-delimited) files and JSON semi-structured data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create stages, databases, tables, views, and virtual warehouses.\u003C/li\u003E\u003Cli\u003EHow to load structured and semi-structured data.\u003C/li\u003E\u003Cli\u003EHow to consume Snowflake Public Data data from the \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free?search=Snowflake+Public+Data+%28Free%29\"\u003ESnowflake Data Marketplace\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EHow to perform analytical queries on data in Snowflake, including joins between tables.\u003C/li\u003E\u003Cli\u003EHow to clone objects.\u003C/li\u003E\u003Cli\u003EHow to undo user errors using Time Travel.\u003C/li\u003E\u003Cli\u003EHow to create roles and users, and grant them privileges.\u003C/li\u003E\u003Cli\u003EHow to securely and easily share data with other accounts.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EData You'll Use:\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Public Data\u003C/strong\u003E is a next generation data company creating a real-time view of the world's economy with analytics-ready data exclusively on Snowflake Marketplace. Initially focused on consumer insights, Snowflake Public Data enables you to access external data directly in your Snowflake instance — no ETL required.\u003C/p\u003E\n","\u003Cp\u003EThis lab will use the following Snowflake Public Data datasets:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Public Data (Free)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECheck out Snowflake Public Data's \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free?search=Snowflake+Public+Data+%28Free%29\"\u003ESnowflake Public Data (Free)\u003C/a\u003E product and \u003Ca href=\"https://app.snowflake.com/marketplace/providers/GZTSZAS2KCS/Snowflake%20Public%20Data%20Products\"\u003Eexplore all public sources\u003C/a\u003E Snowflake Public Data offers on the Snowflake Marketplace.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare Your Lab Environment\u003C/h2\u003E\n","\u003Cp\u003EIf you haven't already, register for a \u003Ca href=\"https://signup.snowflake.com/developers?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake free 30-day trial\u003C/a\u003E. This lab assumes you are using a new Snowflake account created by registering for a trial.\u003C/p\u003E\n","\u003Cp\u003EThe Snowflake edition (Standard, Enterprise, Business Critical, etc.), cloud provider (AWS, Azure, GCP), and Region (US East, EU, etc.) you use for this lab, do not matter. However, we suggest you select the region that is physically closest to you and Enterprise, our most popular offering, as your Snowflake edition.\u003C/p\u003E\n","\u003Cp\u003EAfter registering, you will receive an email with an activation link and URL for accessing your Snowflake account.\u003C/p\u003E\n","\u003Ch3\u003ELogging into the Snowflake User Interface (UI)\u003C/h3\u003E\n","\u003Cp\u003EOpen a browser window and enter the URL of your Snowflake 30-day trial environment that was sent with your registration email. Enter the username and password that you specified during the registration:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_1.png\" alt=\"login screen\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Snowflake ​User Interface\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAbout the screenshots, sample code, and environment\u003C/strong\u003E\nScreenshots in this lab depict examples; results may vary slightly from what you see when you complete the exercises.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ENavigating the Snowflake UI\u003C/h3\u003E\n","\u003Cp\u003ELet's get you acquainted with Snowflake! This section covers the basic components of the user interface.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_2.png\" alt=\"snowflake navbar\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EProjects > Worksheets\u003C/h3\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EProjects\u003C/strong\u003E on the left-hand panel, select the ​\u003Cstrong\u003EWorksheets​\u003C/strong\u003E tab or go directly by <a href="https://app.snowflake.com/_deeplink/worksheets?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">clicking here</a>. This provides an interface for submitting SQL queries, performing DDL and DML operations, and viewing results as your queries or operations complete. A new worksheet is created by clicking \u003Cstrong\u003E\u003Ccode\u003E+\u003C/code\u003E\u003C/strong\u003E on the top right.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_3.png\" alt=\"worksheets tab main\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWorksheet Structure\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_4.png\" alt=\"worksheets tab details\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe top left corner contains the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake\u003C/strong\u003E icon: Use this to get back to the main console/close the worksheet.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWorksheet_name\u003C/strong\u003E drop-down: The default name is the timestamp when the worksheet was created. Click the timestamp to edit the worksheet name. The drop-down also displays additional actions you can perform for the worksheet.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFilters\u003C/strong\u003E button: Custom filters are special keywords that resolve as a subquery or list of values.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe top right corner contains the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EContext\u003C/strong\u003E box: This lets Snowflake know which role and warehouse to use during this session. It can be changed via the UI or SQL commands.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EShare\u003C/strong\u003E button: Open the sharing menu to share to other users or copy the link to the worksheet.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPlay/Run\u003C/strong\u003E button: Run the SQL statement where the cursor currently is or multiple selected statements.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe middle pane contains the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDrop-down at the top for setting the database/schema/object context for the worksheet.\u003C/li\u003E\u003Cli\u003EGeneral working area where you enter and execute queries and other SQL statements.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe middle-left panel contains the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EWorksheets\u003C/strong\u003E tab: Use this tab to quickly select and jump between different worksheets\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabases\u003C/strong\u003E tab: Use this tab to view all of the database objects available to the current role\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESearch\u003C/strong\u003E bar: database objects browser which enables you to explore all databases, schemas, tables, and views accessible by the role currently in use for the worksheet.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe bottom pane displays the results of queries and other operations. Also includes 4 options (\u003Cstrong\u003EObject\u003C/strong\u003E, \u003Cstrong\u003EQuery\u003C/strong\u003E, \u003Cstrong\u003EResult\u003C/strong\u003E, \u003Cstrong\u003EChart\u003C/strong\u003E) that open/close their respective panels on the UI. \u003Cstrong\u003EChart\u003C/strong\u003E opens a visualization panel for the returned results. More on this later.\u003C/p\u003E\n","\u003Cp\u003EThe various panes on this page can be resized by adjusting their sliders. If you need more room in the worksheet, collapse the database objects browser in the left panel. Many of the screenshots in this guide keep this panel closed.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWorksheets vs the UI\u003C/strong\u003E\nMost of the exercises in this lab are executed using pre-written SQL within this worksheet to save time. These tasks can also be done via the UI, but would require navigating back-and-forth between multiple UI tabs.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EProjects > Dashboards\u003C/h3\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EProjects\u003C/strong\u003E on the left-hand panel, select the ​\u003Cstrong\u003EDashboards​\u003C/strong\u003E tab or <a href="https://app.snowflake.com/_deeplink/dashboards?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">click here</a>. This tab allows you to create flexible displays of one or more charts (in the form of tiles, which can be rearranged). Tiles and widgets are produced by executing SQL queries that return results in a worksheet. Dashboards work at a variety of sizes with minimal configuration.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_5.png\" alt=\"dashboards tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EData > Databases\u003C/h3\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EDiscover & Collaborate\u003C/strong\u003E, the \u003Cstrong\u003ECatalog > Database Explorer\u003C/strong\u003E​ tab shows information about the databases you have created or have permission to access. Click here to naviage to the <a href="https://app.snowflake.com/_deeplink/#/data/databases?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">Database Explorer</a> directly.  You can create, clone, drop, or transfer ownership of databases, as well as load data in the UI. Notice that a database already exists in your environment. However, we will not be using it in this lab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_6.png\" alt=\"databases tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EData Products > Marketplace\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EMarketplace\u003C/strong\u003E tab is where any Snowflake customer can browse and consume data sets made available by providers. Click here to navigate direclty to the <a href="https://app.snowflake.com/_deeplink/marketplace?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">Marketplace</a>.There are two types of shared data: Public and Personalized. Public data is free data sets available for querying instantaneously. Personalized data requires reaching out to the provider of data for approval of sharing data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_8.png\" alt=\"marketplace tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EData Products > Private Sharing\u003C/h3\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EData Products\u003C/strong\u003E, the \u003Cstrong\u003EPrivate Sharing\u003C/strong\u003E tab is where data sharing can be configured to easily and securely share Snowflake tables among separate Snowflake accounts or external users, without having to create a copy of the data.  Browse directly to Private Sharing by <a href="https://app.snowflake.com/_deeplink/#/data/shared/shared-with-you?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">clicking here</a>.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_7.png\" alt=\"shared data tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EMonitoring > Query History\u003C/h3\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EMonitoring\u003C/strong\u003E there are <a href="https://app.snowflake.com/_deeplink/#/compute/history/queries?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">multiple tabs</a> for tracking your usage of your Snowflake account:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EQuery History\u003C/strong\u003E is where previous queries are shown, along with filters that can be used to hone results (user, warehouse, status, query tag, etc.). View the details of all queries executed in the last 14 days from your Snowflake account. Click a query ID to drill into it for more information.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECopy History\u003C/strong\u003E shows the status of copy commands run to ingest data into Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETask History\u003C/strong\u003E allows you to see the execution history for tasks and tasks graphs. (Tasks let you schedule the execution of SQL code. It is associated with a specific database and schema.)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDynamic Tables\u003C/strong\u003E is where you can use Snowsight to monitor dynamic table refreshes and examine dynamic tables and dynamic table graphs.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGovernance\u003C/strong\u003E tracks row- and column-level security, object tagging, data classification, access history, and more.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_9.png\" alt=\"history tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdmin > Warehouses\u003C/h3\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EAdmin\u003C/strong\u003E, the \u003Cstrong\u003E​Warehouses​\u003C/strong\u003E tab is where you set up and manage compute resources known as virtual warehouses to load or query data in Snowflake. A warehouse called COMPUTE_WH already exists in your environment.  <a href="https://app.snowflake.com/_deeplink/#/compute/warehouses?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">Click here</a> to go directly to the Warehouses tab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_10.png\" alt=\"warehouses tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdmin > Cost Management\u003C/h3\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EAdmin\u003C/strong\u003E, the \u003Cstrong\u003ECost Management\u003C/strong\u003E tab shows an overview of account consumption and budgets. It also includes details on all the resource monitors that have been created to control the number of credits that virtual warehouses consume. For each resource monitor, it shows the credit quota, type of monitoring, schedule, and actions performed when the virtual warehouse reaches its credit limit.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_11.png\" alt=\"resource monitors tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdmin > Users & Roles\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ERoles\u003C/strong\u003E sub-tab of the \u003Cstrong\u003EUsers & Roles\u003C/strong\u003E tab shows a list of the roles and their hierarchies. Roles can be created, reorganized, and granted to users in this tab. The roles can also be displayed in tabular/list format by selecting the \u003Cstrong\u003ETable\u003C/strong\u003E sub-tab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_12.png\" alt=\"roles tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EUsers\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EUsers\u003C/strong\u003E sub-tab of the \u003Cstrong\u003EUsers & Roles\u003C/strong\u003E tab shows a list of users in the account, default roles, and owner of the users. For a new account, no records are shown because no additional roles have been created. Permissions granted through your current role determine the information shown for this tab. To see all the information available on the tab, switch your role to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_13.png\" alt=\"users tab\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClicking on your username in the bottom right of the UI allows you to change your password, roles, and preferences. Snowflake has several system defined roles. You are currently in the default role of \u003Ccode\u003ESYSADMIN\u003C/code\u003E and will stay in this role for the majority of the lab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_14.png\" alt=\"user preferences dropdown\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESYSADMIN\u003C/strong\u003E\nThe \u003Ccode\u003ESYSADMIN\u003C/code\u003E (aka System Administrator) role has privileges to create warehouses, databases, and other objects in an account.\nIn a real-world environment, you would use different roles for the tasks in this lab, and assign roles to your users. We will cover more on roles and Snowflake's access control model in Section 9 and you can find additional information in the \u003Ca href=\"https://docs.snowflake.com/user-guide/security-access-control-overview\"\u003ESnowflake documentation\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Lab: Stock Price & SEC Filings Data\u003C/h2\u003E\n","\u003Ch3\u003EThe Lab Story\u003C/h3\u003E\n","\u003Cp\u003EYou work at a grocery retailer. You want to understand the performance of major consumer goods (CPG) companies in the US that supply your store. This lab takes a look at daily stock price data and quarterly and annual Securities Exchange Commission (SEC) company filings to understand the performance of the CPG landscape. Public companies are required to submit a quarterly and annual report to the SEC detailing their financial data.\u003C/p\u003E\n","\u003Cp\u003EWe will start by collecting data from three different sources:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELoad company metadata \u003Ccode\u003E.csv\u003C/code\u003E file.\u003C/li\u003E\u003Cli\u003ELoad SEC filings from a semi-structured JSON format.\u003C/li\u003E\u003Cli\u003EUse the Snowflake Marketplace to find free stock price data in "Snowflake Public Data (Free)" from Snowflake Public Data.\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoading Structured Data into Snowflake: CSVs\u003C/h2\u003E\n","\u003Cp\u003ELet's start by preparing to load structured \u003Ccode\u003E.csv\u003C/code\u003E data into Snowflake.\u003C/p\u003E\n","\u003Cp\u003EWe are using company metadata developed from the Securities and Exchange Commission (SEC) that details the consumer packaged goods (CPG) companies we want to evaluate. The data has been exported and pre-staged for you in an Amazon AWS S3 bucket in the US-EAST region. It is in comma-delimited format with a single header line and double quotes enclosing all string values, including the field headings in the header line. This will be important when we configure the Snowflake table to store this data.\u003C/p\u003E\n<!-- victoria image of the CPG data -->\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFree Datasets from Snowflake Public Data direct to your Snowflake instance:\u003C/strong\u003E The full dataset is available \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free?search=Snowflake+Public+Data+%28Free%29\"\u003E\u003Cstrong\u003Efor free\u003C/strong\u003E\u003C/a\u003E in Snowflake Marketplace from Snowflake Public Data -- no ETL required. For the purposes of this demo, we will focus on working with a subset of the data, staged in a csv file to learn how to load structured data into Snowflake.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGetting Data into Snowflake\u003C/strong\u003E\nData can be ingested into Snowflake from many locations by using the \u003Ccode\u003ECOPY\u003C/code\u003E command, Snowpipe auto-ingestion, external connectors, or third-party ETL/ELT solutions. For more information on getting data into Snowflake, see the \u003Ca href=\"https://docs.snowflake.com/guides-overview-loading-data\"\u003ESnowflake documentation\u003C/a\u003E. For the purposes of this lab, we use the \u003Ccode\u003ECOPY\u003C/code\u003E command and AWS S3 storage to load data manually. In a real-world scenario, you would more likely use an ETL solution or grab data directly from the Snowflake Marketplace!\u003C/p\u003E\n","\u003Ch3\u003ECreate a Database and Table\u003C/h3\u003E\n","\u003Cp\u003EEnsure you are using the \u003Ccode\u003ESYSADMIN\u003C/code\u003E role by selecting your name at the top left, \u003Cstrong\u003ESwitch Role\u003C/strong\u003E > \u003Cstrong\u003ESYSADMIN\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003ENavigate to the <a href="https://app.snowflake.com/_deeplink/#/data/databases?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">\u003Cstrong\u003EDatabases\u003C/strong\u003E tab</a>. Click \u003Cstrong\u003ECreate\u003C/strong\u003E, name the database \u003Ccode\u003EPublic_Data\u003C/code\u003E, then click \u003Cstrong\u003ECREATE\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Create_New_DB.png\" alt=\"worksheet creation\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow navigate to the <a href="https://app.snowflake.com/_deeplink/worksheets?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">\u003Cstrong\u003EWorksheets\u003C/strong\u003E tab</a>. You should see the worksheet we created in step 3.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_3.png\" alt=\"new worksheet\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe need to set the context appropriately within the worksheet. In the upper right corner of the worksheet, click the box to the left of the \u003Cstrong\u003EShare\u003C/strong\u003E button to show the context menu. Here we control the elements you can see and run from each worksheet. We are using the UI here to set the context. Later in the lab, we will accomplish the same thing via SQL commands within the worksheet.\u003C/p\u003E\n","\u003Cp\u003ESelect the following context settings:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole:\u003C/strong\u003E \u003Ccode\u003ESYSADMIN\u003C/code\u003E\n\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_4.png\" alt=\"context role and warehouse settings\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, in the drop-down for the database, select the following context settings:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003EPublic_Data\u003C/code\u003E\n\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003EPUBLIC\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Choose_Schema.png\" alt=\"context database settings\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Definition Language (DDL) operations are free!\u003C/strong\u003E\nAll the DDL operations we have done so far do not require compute resources, so we can create all our objects for free.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ETo make working in the worksheet easier, let's rename it. In the top left corner, double-click the worksheet name, which is the timestamp when the worksheet was created, and change it to \u003Ccode\u003EZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ENext we create a table called \u003Ccode\u003ECOMPANY_METADATA\u003C/code\u003E to use for loading the comma-delimited data. Instead of using the UI, we use the worksheet to run the DDL that creates the table. Copy the following SQL text into your worksheet:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE TABLE company_metadata\n(cybersyn_company_id string,\ncompany_name string,\npermid_security_id string,\nprimary_ticker string,\nsecurity_name string,\nasset_class string,\nprimary_exchange_code string,\nprimary_exchange_name string,\nsecurity_status string,\nglobal_tickers variant,\nexchange_code variant,\npermid_quote_id variant);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMany Options to Run Commands.\u003C/strong\u003E\nSQL commands can be executed through the UI, via the \u003Cstrong\u003EWorksheets\u003C/strong\u003E tab, using our SnowSQL command line tool, with a SQL editor of your choice via ODBC/JDBC, or through our other connectors (Python, Spark, etc.).\nAs mentioned earlier, to save time, we are performing most of the operations in this lab via pre-written SQL executed in the worksheet as opposed to using the UI.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ERun the query by placing your cursor anywhere in the SQL text and clicking the blue \u003Cstrong\u003EPlay/Run\u003C/strong\u003E button in the top right of the worksheet. Or use the keyboard shortcut [Ctrl]/[Cmd]+[Enter].\u003C/p\u003E\n","\u003Cp\u003EVerify your \u003Ccode\u003ECOMPANY_METADATA\u003C/code\u003E table has been created. At the bottom of the worksheet, you should see a Results section displaying a \u003Ccode\u003E"Table COMPANY_METADATA successfully created"\u003C/code\u003E message.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_5_new.png\" alt=\"TRIPS confirmation message\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENavigate to the \u003Cstrong\u003EDatabases\u003C/strong\u003E tab by clicking the \u003Cstrong\u003EHOME\u003C/strong\u003E icon in the upper left corner of the worksheet. Then click \u003Cstrong\u003EData\u003C/strong\u003E > \u003Cstrong\u003EDatabases\u003C/strong\u003E. In the list of databases, click \u003Ccode\u003EPublic_Data\u003C/code\u003E > \u003Ccode\u003EPUBLIC\u003C/code\u003E > \u003Cstrong\u003ETABLES\u003C/strong\u003E to see your newly created \u003Ccode\u003ECOMPANY_METADATA\u003C/code\u003E table. If you don't see any databases on the left, expand your browser because they may be hidden.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_6_new.png\" alt=\"TRIPS table\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ECOMPANY_METADATA\u003C/code\u003E and the \u003Cstrong\u003EColumns\u003C/strong\u003E tab to see the table structure you just created.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_7_new.png\" alt=\"TRIPS table structure\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate an External Stage\u003C/h3\u003E\n","\u003Cp\u003EWe are working with structured, comma-delimited data that has already been staged in a public, external S3 bucket. Before we can use this data, we first need to create a \u003Cem\u003Estage\u003C/em\u003E that specifies the location of our external bucket.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFor this lab, we are using an AWS-East bucket. To prevent data egress/transfer costs in the future, you should select a staging location from the same cloud provider and region as your Snowflake account.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EFrom the \u003Cstrong\u003EDatabases\u003C/strong\u003E tab, click the \u003Ccode\u003EPublic_Data\u003C/code\u003E database and \u003Ccode\u003EPUBLIC\u003C/code\u003E schema. Click the \u003Cstrong\u003ECreate\u003C/strong\u003E button, then \u003Cstrong\u003EStage\u003C/strong\u003E > \u003Cstrong\u003EAmazon S3\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_8_new.png\" alt=\"stages create\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003ECreate Stage\u003C/code\u003E dialog that opens, replace the following values in the SQL statement:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStage Name\u003C/strong\u003E: \u003Ccode\u003EPublic_Data_company_metadata\u003C/code\u003E\n\u003Cstrong\u003EURL\u003C/strong\u003E: \u003Ccode\u003Es3://sfquickstarts/zero_to_snowflake/cybersyn-consumer-company-metadata-csv/\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Make sure to include the final forward slash (\u003Ccode\u003E/\u003C/code\u003E) at the end of the URL or you will encounter errors later when loading data from the bucket.\nAlso ensure you have removed 'credentials = (...)' statement which is not required. You can also comment it out like the picture below by using '--'. The create stage command should resemble the below picture or not include the 3rd line.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe S3 bucket for this lab is public so you can leave the credentials options in the statement empty. In a real-world scenario, the bucket used for an external stage would likely require key information.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_9_new.png\" alt=\"create stage settings\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let's take a look at the contents of the \u003Ccode\u003EPublic_Data_company_metadata\u003C/code\u003E stage. Navigate back to the \u003Cstrong\u003EWorksheets\u003C/strong\u003E tab and open the \u003Ccode\u003EZERO_TO_SNOWFLAKE_WITH_Public_Data\u003C/code\u003E worksheet we made.\u003C/p\u003E\n","\u003Cp\u003EAdd the following SQL statement below the previous code and then execute:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ELIST @Public_Data_company_metadata;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the results in the bottom pane, you should see the list of files in the stage:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_10_new.png\" alt=\"worksheet result\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate a File Format\u003C/h3\u003E\n","\u003Cp\u003EBefore we can load the data into Snowflake, we have to create a file format that matches the data structure. In the worksheet, again add the following command below the rest and execute to create the file format:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE FILE FORMAT csv\n    TYPE = 'CSV'\n    COMPRESSION = 'AUTO'  -- Automatically determines the compression of files\n    FIELD_DELIMITER = ','  -- Specifies comma as the field delimiter\n    RECORD_DELIMITER = '\\n'  -- Specifies newline as the record delimiter\n    SKIP_HEADER = 1  -- Skip the first line\n    FIELD_OPTIONALLY_ENCLOSED_BY = '\\042'  -- Fields are optionally enclosed by double quotes (ASCII code 34)\n    TRIM_SPACE = FALSE  -- Spaces are not trimmed from fields\n    ERROR_ON_COLUMN_COUNT_MISMATCH = FALSE  -- Does not raise an error if the number of fields in the data file varies\n    ESCAPE = 'NONE'  -- No escape character for special character escaping\n    ESCAPE_UNENCLOSED_FIELD = '\\134'  -- Backslash is the escape character for unenclosed fields\n    DATE_FORMAT = 'AUTO'  -- Automatically detects the date format\n    TIMESTAMP_FORMAT = 'AUTO'  -- Automatically detects the timestamp format\n    NULL_IF = ('')  -- Treats empty strings as NULL values\n    COMMENT = 'File format for ingesting data for zero to snowflake';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_11_new.png\" alt=\"create file format\"\u003E\u003C/p\u003E\n","\u003Cp\u003EVerify the file format has been created with the correct settings by executing the following command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESHOW FILE FORMATS IN DATABASE Public_Data;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe file format created should be listed in the result:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_12_new.png\" alt=\"create file format settings\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EResize and Use a Warehouse for Data Loading\u003C/h3\u003E\n","\u003Cp\u003EWe will now use a virtual warehouse and the \u003Ccode\u003ECOPY\u003C/code\u003E command to initiate bulk loading of structured data into the Snowflake table we created.\u003C/p\u003E\n","\u003Cp\u003ECompute resources are needed for loading data. Snowflake's compute nodes are called virtual warehouses and they can be dynamically sized up or out according to workload, whether you are loading data, running a query, or performing a DML operation. Each workload can have its own warehouse so there is no resource contention.\u003C/p\u003E\n","\u003Cp\u003ENavigate to the <a href="https://app.snowflake.com/_deeplink/#/compute/warehouses?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">\u003Cstrong\u003EWarehouses\u003C/strong\u003E tab</a> (under \u003Cstrong\u003EAdmin\u003C/strong\u003E). This is where you can view all of your existing warehouses, as well as analyze their usage trends.\u003C/p\u003E\n","\u003Cp\u003ENote the \u003Cstrong\u003E+ Warehouse\u003C/strong\u003E option in the upper right corner of the top. This is where you can quickly add a new warehouse. However, we want to use the existing warehouse \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E included in the 30-day trial environment.\u003C/p\u003E\n","\u003Cp\u003EClick the row of the \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E warehouse. Then click the \u003Cstrong\u003E[...]\u003C/strong\u003E in the upper right corner text above it to see the actions you can perform on the warehouse. We will use this warehouse to load the data from AWS S3.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_1.png\" alt=\"compute warehouse configure\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EEdit\u003C/strong\u003E to walk through the options of this warehouse and learn some of Snowflake's unique functionality.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf this account isn't using Snowflake Enterprise Edition (or higher), you will not see the \u003Cstrong\u003EMode\u003C/strong\u003E or \u003Cstrong\u003EClusters\u003C/strong\u003E options shown in the screenshot below. The multi-cluster warehouses feature is not used in this lab, but we will discuss it as a key capability of Snowflake.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_2.png\" alt=\"warehouse configure settings\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ESize\u003C/strong\u003E drop-down is where the capacity of the warehouse is selected. For larger data loading operations or more compute-intensive queries, a larger warehouse is recommended. The sizes translate to the underlying compute resources provisioned from the cloud provider (AWS, Azure, or GCP) where your Snowflake account is hosted. It also determines the number of credits consumed by the warehouse for each full hour it runs. The larger the size, the more compute resources from the cloud provider are allocated to the warehouse and the more credits it consumes. For example, the \u003Ccode\u003E4X-Large\u003C/code\u003E setting consumes 128 credits for each full hour. This sizing can be changed up or down at any time with a simple click.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you are using Snowflake Enterprise Edition (or higher) the \u003Cstrong\u003EQuery Acceleration\u003C/strong\u003E option is available. When it is enabled for a warehouse, it can improve overall warehouse performance by reducing the impact of outlier queries, which are queries that use more resources than the typical query. Leave this disabled\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you are using Snowflake Enterprise Edition (or higher) and the \u003Cstrong\u003EMulti-cluster Warehouse\u003C/strong\u003E option is enabled, you will see additional options. This is where you can set up a warehouse to use multiple clusters of compute resources, up to 10 clusters. For example, if a \u003Ccode\u003E4X-Large\u003C/code\u003E multi-cluster warehouse is assigned a maximum cluster size of 10, it can scale out to 10 times the compute resources powering that warehouse...and it can do this in seconds! However, note that this will increase the number of credits consumed by the warehouse to 1280 if all 10 clusters run for a full hour (128 credits/hour x 10 clusters). Multi-cluster is ideal for concurrency scenarios, such as many business analysts simultaneously running different queries using the same warehouse. In this use case, the various queries are allocated across multiple clusters to ensure they run quickly.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EAdvanced Warehouse Options\u003C/strong\u003E, the options allow you to automatically suspend the warehouse when not in use so no credits are needlessly consumed. There is also an option to automatically resume a suspended warehouse so when a new workload is sent to it, it automatically starts back up. This functionality enables Snowflake's efficient "pay only for what you use" billing model which allows you to scale your resources when necessary and automatically scale down or turn off when not needed, nearly eliminating idle resources. Additionally, there is an option to change the Warehouse type from Standard to Snowpark-optimized. Snowpark-optmized warehouses provide 16x memory per node and are recommended for workloads that have large memory requirements such as ML training use cases using a stored procedure on a single virtual warehouse node. Leave this type as Standard\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Compute vs Other Data Warehouses\u003C/strong\u003E\nMany of the virtual warehouse and compute capabilities we just covered, such as the ability to create, scale up, scale out, and auto-suspend/resume virtual warehouses are easy to use in Snowflake and can be done in seconds. For on-premise data warehouses, these capabilities are much more difficult, if not impossible, as they require significant physical hardware, over-provisioning of hardware for workload spikes, and significant configuration work, as well as additional challenges. Even other cloud-based data warehouses cannot scale up and out like Snowflake without significantly more configuration work and time.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWarning - Watch Your Spend!\u003C/strong\u003E\nDuring or after this lab, you should be careful about performing the following actions without good reason or you may burn through your $400 of free credits more quickly than desired:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDo not disable auto-suspend. If auto-suspend is disabled, your warehouses continues to run and consume credits even when not in use.\u003C/li\u003E\u003Cli\u003EDo not use a warehouse size that is excessive given the workload. The larger the warehouse, the more credits are consumed.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe are going to use this virtual warehouse to load the structured data in the CSV files (stored in the AWS S3 bucket) into Snowflake. However, we are first going to change the size of the warehouse to increase the compute resources it uses. After the load, note the time taken and then, in a later step in this section, we will re-do the same load operation with an even larger warehouse, observing its faster load time.\u003C/p\u003E\n","\u003Cp\u003EChange the \u003Cstrong\u003ESize\u003C/strong\u003E of this data warehouse from \u003Ccode\u003EX-Small\u003C/code\u003E to \u003Ccode\u003ESmall\u003C/code\u003E. then click the \u003Cstrong\u003ESave Warehouse\u003C/strong\u003E button:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_3.png\" alt=\"configure settings small\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELoad the Data\u003C/h3\u003E\n","\u003Cp\u003ENow we can run a COPY command to load the data into the \u003Ccode\u003ECOMPANY_METADATA\u003C/code\u003E table we created earlier.\u003C/p\u003E\n","\u003Cp\u003ENavigate back to the \u003Ccode\u003EZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA\u003C/code\u003E worksheet in the \u003Cstrong\u003EWorksheets\u003C/strong\u003E tab. Make sure the worksheet context is correctly set:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole:\u003C/strong\u003E \u003Ccode\u003ESYSADMIN\u003C/code\u003E\n\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E\n\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003EPublic_Data\u003C/code\u003E\n\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003EPUBLIC\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_4.png\" alt=\"worksheet context\"\u003E\u003C/p\u003E\n","\u003Cp\u003EExecute the following statements in the worksheet to load the staged data into the table. This may take up to 30 seconds.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECOPY INTO company_metadata FROM @Public_Data_company_metadata file_format=csv PATTERN = '.*csv.*' ON_ERROR = 'CONTINUE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the result pane, you should see the status of each file that was loaded. Once the load is done, in the \u003Cstrong\u003EQuery Details\u003C/strong\u003E pane on the bottom right, you can scroll through the various statuses, error statistics, and visualizations for the last statement executed.\u003C/p\u003E\n","\u003Cp\u003ENext, navigate to the \u003Cstrong\u003EQuery History\u003C/strong\u003E tab by clicking the \u003Cstrong\u003EHome\u003C/strong\u003E icon and then \u003Cstrong\u003EActivity\u003C/strong\u003E > \u003Cstrong\u003EQuery History\u003C/strong\u003E. Select the query at the top of the list, which should be the COPY INTO statement that was last executed. Select the \u003Cstrong\u003EQuery Profile\u003C/strong\u003E tab and note the steps taken by the query to execute, query details, most expensive nodes, and additional statistics.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_6.png\" alt=\"history and duration\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let's reload the \u003Ccode\u003ECOMPANY_METADATA\u003C/code\u003E table with a larger warehouse to see the impact the additional compute resources have on the loading time.\u003C/p\u003E\n","\u003Cp\u003EGo back to the worksheet and use the \u003Ccode\u003ETRUNCATE TABLE\u003C/code\u003E command to clear the table of all data and metadata:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ETRUNCATE TABLE company_metadata;\n\n-- Verify that the table is empty by running the following command:\nSELECT * FROM company_metadata LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe result should show \u003Ccode\u003EQuery produced no results\u003C/code\u003E. Change the warehouse size to \u003Ccode\u003ELARGE\u003C/code\u003E using the following \u003Ccode\u003EALTER WAREHOUSE\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EALTER WAREHOUSE compute_wh SET warehouse_size='large';\n\n-- Verify the change using the following SHOW WAREHOUSES:\nSHOW WAREHOUSES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_7.png\" alt=\"resize context to large in UI step 1\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe size can also be changed using the UI by clicking on the worksheet context box, then the \u003Cstrong\u003EConfigure\u003C/strong\u003E (3-line) icon on the right side of the context box, and changing \u003Ccode\u003ESmall\u003C/code\u003E to \u003Ccode\u003ELarge\u003C/code\u003E in the \u003Cstrong\u003ESize\u003C/strong\u003E drop-down:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_8.png\" alt=\"resize context to large in UI step 2\"\u003E\u003C/p\u003E\n","\u003Cp\u003EExecute the same \u003Ccode\u003ECOPY INTO\u003C/code\u003E statement as before to load the same data again:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECOPY INTO company_metadata FROM @Public_Data_company_metadata file_format=csv PATTERN = '.*csv.*' ON_ERROR = 'CONTINUE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce the load is done, navigate back to the \u003Cstrong\u003EQueries\u003C/strong\u003E page (\u003Cstrong\u003EHome\u003C/strong\u003E icon > \u003Cstrong\u003EActivity\u003C/strong\u003E > \u003Cstrong\u003EQuery History\u003C/strong\u003E). Compare the times of the two \u003Ccode\u003ECOPY INTO\u003C/code\u003E commands. The load using the \u003Ccode\u003ELarge\u003C/code\u003E warehouse was significantly faster.\u003C/p\u003E\n","\u003Ch3\u003ECreate a New Warehouse for Data Analytics\u003C/h3\u003E\n","\u003Cp\u003ELet's assume our internal analytics team wants to eliminate resource contention between their data loading/ETL workloads and the analytical end users using BI tools to query Snowflake. As mentioned earlier, Snowflake can easily do this by assigning different, appropriately-sized warehouses to various workloads. Since our company already has a warehouse for data loading, let's create a new warehouse for the end users running analytics. We will use this warehouse to perform analytics in the next section.\u003C/p\u003E\n","\u003Cp\u003ENavigate to the \u003Cstrong\u003EAdmin\u003C/strong\u003E > \u003Cstrong\u003EWarehouses\u003C/strong\u003E tab, click \u003Cstrong\u003E+ Warehouse\u003C/strong\u003E, and name the new warehouse and set the size to \u003Ccode\u003ELarge\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf you are using Snowflake Enterprise Edition (or higher) and \u003Cstrong\u003EMulti-cluster Warehouses\u003C/strong\u003E is enabled, you will see additional settings:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMake sure \u003Cstrong\u003EMax Clusters\u003C/strong\u003E is set to \u003Ccode\u003E1\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ELeave all the other settings at their defaults.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_10.png\" alt=\"warehouse settings\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick the \u003Cstrong\u003ECreate Warehouse\u003C/strong\u003E button to create the warehouse.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoading Semi-Structured Data into Snowflake: JSONs\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis section requires loading additional data and, therefore, provides a review of data loading while also introducing loading semi-structured data.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EGoing back to the lab's example, our company's analytics team wants to evaluate the performance of CPG companies through the lens of their reported metrics in SEC filings. To do this, in this section, we will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELoad SEC filing data in semi-structured JSON format held in a public S3 bucket.\u003C/li\u003E\u003Cli\u003ECreate a view and query the JSON data using SQL dot notation.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe JSON data consists of SEC filings provided by \u003Cem\u003ESnowflake Public Data\u003C/em\u003E, detailing the historical performance of consumer-packaged goods companies from 2019-2023. It is also staged on AWS S3. If viewed in a text editor, the raw JSON in the GZ files looks like:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_1_1.png\" alt=\"raw JSON sample\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003E(The full dataset available \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/\"\u003E\u003Cstrong\u003Efor free\u003C/strong\u003E\u003C/a\u003E in Snowflake Marketplace from Snowflake Public Data -- no ETL required. For the purposes of this demo, we will focus on working with the semi-structured JSON file to learn how to load structured data into Snowflake.)\u003C/em\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESEMI-STRUCTURED DATA\u003C/strong\u003E\nSnowflake can easily load and query semi-structured data such as JSON, Parquet, or Avro without transformation. This is a key Snowflake feature because an increasing amount of business-relevant data being generated today is semi-structured, and many traditional data warehouses cannot easily load and query such data. Snowflake makes it easy!\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate a New Database and Table for the Data\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExecuting Multiple Commands\u003C/strong\u003E Remember that you need to execute each command individually. However, you can execute them in sequence together by selecting all of the commands and then clicking the \u003Cstrong\u003EPlay/Run\u003C/strong\u003E button (or using the keyboard shortcut).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENext, let's create two tables, \u003Ccode\u003ESEC_FILINGS_INDEX\u003C/code\u003E and \u003Ccode\u003ESEC_FILINGS_ATTRIBUTES\u003C/code\u003E to use for loading JSON data. In the worksheet, execute the following \u003Ccode\u003ECREATE TABLE\u003C/code\u003E commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE TABLE sec_filings_index (v variant);\n\nCREATE TABLE sec_filings_attributes (v variant);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that Snowflake has a special data type called \u003Ccode\u003EVARIANT\u003C/code\u003E that allows storing the entire JSON object as a single row and querying the object directly.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemi-Structured Data Magic\u003C/strong\u003E\nThe \u003Ccode\u003EVARIANT\u003C/code\u003E data type allows Snowflake to ingest semi-structured data without having to predefine the schema.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn the results pane at the bottom of the worksheet, verify that your tables, \u003Ccode\u003ESEC_FILINGS_INDEX\u003C/code\u003E and \u003Ccode\u003ESEC_FILINGS_ATTRIBUTES\u003C/code\u003E, were created:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_2_1_new.png\" alt=\"success message\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate Another External Stage\u003C/h3\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003EZERO_TO_SNOWFLAKE_WITH_Public_Data\u003C/code\u003E worksheet, use the following command to create a stage that points to the bucket where the semi-structured JSON data is stored on AWS S3:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE STAGE Public_Data_sec_filings\nurl = 's3://sfquickstarts/zero_to_snowflake/cybersyn_cpg_sec_filings/';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's take a look at the contents of the \u003Ccode\u003EPublic_Data_sec_filings\u003C/code\u003E stage. Execute the following \u003Ccode\u003ELIST\u003C/code\u003E command to display the list of files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ELIST @Public_Data_sec_filings;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the results pane, you should see a list of \u003Ccode\u003E.gz\u003C/code\u003E files from S3:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_3_1_new.png\" alt=\"results output\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELoad and Verify the Semi-structured Data\u003C/h3\u003E\n","\u003Cp\u003EWe will now use a warehouse to load the data from an S3 bucket into the tables we created earlier. In the \u003Ccode\u003EZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA worksheet, execute the\u003C/code\u003ECOPY` command below to load the data.\u003C/p\u003E\n","\u003Cp\u003ENote that you can specify a \u003Ccode\u003EFILE FORMAT\u003C/code\u003E object inline in the command. In the previous section where we loaded structured data in CSV format, we had to define a file format to support the CSV structure. Because the JSON data here is well-formed, we are able to simply specify the JSON type and use all the default settings:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECOPY INTO sec_filings_index\nFROM @Public_Data_sec_filings/cybersyn_sec_report_index.json.gz\n    file_format = (type = json strip_outer_array = true);\n\nCOPY INTO sec_filings_attributes\nFROM @Public_Data_sec_filings/cybersyn_sec_report_attributes.json.gz\n    file_format = (type = json strip_outer_array = true);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify that each file has a status of \u003Ccode\u003ELOADED\u003C/code\u003E:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_4_1_new.png\" alt=\"query result\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, let's take a look at the data that was loaded:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT * FROM sec_filings_index LIMIT 10;\nSELECT * FROM sec_filings_attributes LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick any of the rows to display the formatted JSON in the right panel:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_5_1.png\" alt=\"JSON data snippet\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo close the display in the panel and display the query details again, click the \u003Cstrong\u003EX\u003C/strong\u003E (Close) button that appears when you hover your mouse in the right corner of the panel.\u003C/p\u003E\n","\u003Ch3\u003ECreate a View and Query Semi-Structured Data\u003C/h3\u003E\n","\u003Cp\u003ENext, let's look at how Snowflake allows us to create a view and also query the JSON data directly using SQL.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EViews & Materialized Views\u003C/strong\u003E\nA view allows the result of a query to be accessed as if it were a table. Views can help present data to end users in a cleaner manner, limit what end users can view in a source table, and write more modular SQL.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ESnowflake also supports materialized views in which the query results are stored as though the results are a table. This allows faster access, but requires storage space. Materialized views can be created and queried if you are using Snowflake Enterprise Edition (or higher).\u003C/p\u003E\n","\u003Cp\u003ERun the following command to create a columnar view of the semi-structured JSON SEC filing data, so it is easier for analysts to understand and query. The CIK corresponds to the Central Index Key, or unique identifier that SEC gives to each filing entity. The ADSH is the document number for any filing submitted to the SEC.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE OR REPLACE VIEW sec_filings_index_view AS\nSELECT\n    v:CIK::string                   AS cik,\n    v:COMPANY_NAME::string          AS company_name,\n    v:EIN::int                      AS ein,\n    v:ADSH::string                  AS adsh,\n    v:TIMESTAMP_ACCEPTED::timestamp AS timestamp_accepted,\n    v:FILED_DATE::date              AS filed_date,\n    v:FORM_TYPE::string             AS form_type,\n    v:FISCAL_PERIOD::string         AS fiscal_period,\n    v:FISCAL_YEAR::string           AS fiscal_year\nFROM sec_filings_index;\n\nCREATE OR REPLACE VIEW sec_filings_attributes_view AS\nSELECT\n    v:VARIABLE::string            AS variable,\n    v:CIK::string                 AS cik,\n    v:ADSH::string                AS adsh,\n    v:MEASURE_DESCRIPTION::string AS measure_description,\n    v:TAG::string                 AS tag,\n    v:TAG_VERSION::string         AS tag_version,\n    v:UNIT_OF_MEASURE::string     AS unit_of_measure,\n    v:VALUE::string               AS value,\n    v:REPORT::int                 AS report,\n    v:STATEMENT::string           AS statement,\n    v:PERIOD_START_DATE::date     AS period_start_date,\n    v:PERIOD_END_DATE::date       AS period_end_date,\n    v:COVERED_QTRS::int           AS covered_qtrs,\n    TRY_PARSE_JSON(v:METADATA)    AS metadata\nFROM sec_filings_attributes;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESQL dot notation \u003Ccode\u003Ev:VARIABLE\u003C/code\u003E is used in this command to pull out values at lower levels within the JSON object hierarchy. This allows us to treat each field as if it were a column in a relational table.\u003C/p\u003E\n","\u003Cp\u003EThe new view should appear as \u003Ccode\u003ESEC_FILINGS_INDEX_VIEW\u003C/code\u003E under \u003Ccode\u003EPublic_data\u003C/code\u003E > \u003Ccode\u003EPUBLIC\u003C/code\u003E > \u003Cstrong\u003EViews\u003C/strong\u003E in the object browser on the left. You may need to expand or refresh the objects browser in order to see it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_6_1.png\" alt=\"JSON_WEATHER_DATA _VIEW in dropdown\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENotice the results look just like a regular structured data source:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT *\nFROM sec_filings_index_view\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGetting Data from Snowflake Marketplace\u003C/h2\u003E\n","\u003Ch3\u003ESnowflake Data Marketplace\u003C/h3\u003E\n","\u003Cp\u003EMake sure you're using the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role and, <a href="https://app.snowflake.com/_deeplink/marketplace?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">navigate to the Marketplace</a> (\u003Cstrong\u003EData Products\u003C/strong\u003E > \u003Cstrong\u003EMarketplace\u003C/strong\u003E):\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_7.png\" alt=\"data marketplace tab\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EFind a listing\u003C/h4\u003E\n","\u003Cp\u003EType \u003Ccode\u003ESnowflake Public Data (Free)\u003C/code\u003E in the search box at the top, scroll through the results, and select \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/\"\u003E\u003Cstrong\u003ESnowflake Public Data (Free)\u003C/strong\u003E\u003C/a\u003E (provided by Snowflake Public Data).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Search_result.png\" alt=\"health tab\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003ESnowflake Public Data (Free)\u003C/strong\u003E page, you can learn more about the contents of the data listing, explore data dictionaries, and see some sample queries. You will also see links to documentation and the dataset's cloud region availability. When you're ready, click the \u003Cstrong\u003EGet\u003C/strong\u003E button to make this information available within your Snowflake account:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Snowflake_Public_Data_Free_Listing.png\" alt=\"get data fields\"\u003E\u003C/p\u003E\n","\u003Cp\u003EReview the information in the dialog and click \u003Cstrong\u003EGet\u003C/strong\u003E again:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/default_get.png\" alt=\"get data fields\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can now click \u003Cstrong\u003EDone\u003C/strong\u003E or choose to run the sample queries provided by Snowflake Public Data:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/ready.png\" alt=\"get data fields\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you chose \u003Cstrong\u003EQuery Data\u003C/strong\u003E, a new worksheet opens in a new browser tab/window:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESet your context\u003C/li\u003E\u003Cli\u003ESelect the query you want to run (or place your cursor in the query text).\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003ERun/Play\u003C/strong\u003E button (or use the keyboard shortcut).\u003C/li\u003E\u003Cli\u003EYou can view the data results in the bottom pane.\u003C/li\u003E\u003Cli\u003EWhen you are done running the sample queries, click the \u003Cstrong\u003EHome\u003C/strong\u003E icon in the upper left corner.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENext:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003EData\u003C/strong\u003E > \u003Cstrong\u003EDatabases\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EClick the \u003Ccode\u003ESNOWFLAKE_PUBLIC_DATA_FREE\u003C/code\u003E database.\u003C/li\u003E\u003Cli\u003EYou can see details about the schemas, tables, and views that are available to query.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/database_details.png\" alt=\"covid19 databases\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThat's it! You have now successfully subscribed to the Snowflake Public Data (Free) from Snowflake Public Data, which is updated regularly with global financial data. Notice we didn't have to create databases, tables, views, or an ETL process. We simply searched for and accessed shared data from the Snowflake Data Marketplace.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ETo learn more about how to use the new worksheet interface, go to the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#using-snowsight\"\u003ESnowsight Docs\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuerying, the Results Cache, & Cloning\u003C/h2\u003E\n","\u003Cp\u003EIn the previous exercises, we loaded data into two tables using Snowflake's \u003Ccode\u003ECOPY\u003C/code\u003E bulk loader command and the \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E virtual warehouse. Now we are going to take on the role of the analytics users at our company who need to query data in those tables using the worksheet and the second warehouse \u003Ccode\u003EANALYTICS_WH\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReal World Roles and Querying\u003C/strong\u003E\nWithin a real company, analytics users would likely have a different role than \u003Ccode\u003ESYSADMIN\u003C/code\u003E. To keep the lab simple, we are going to stay with the \u003Ccode\u003ESYSADMIN\u003C/code\u003E role for this section. Additionally, querying would typically be done with a business intelligence product like Tableau, Looker, PowerBI, etc. For more advanced analytics, data science tools like Datarobot, Dataiku, AWS Sagemaker or many others can query Snowflake. Any technology that leverages JDBC/ODBC, Spark, Python, or any of the other supported programmatic interfaces can run analytics on the data in Snowflake. To keep this lab simple, all queries are being executed via the Snowflake worksheet.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EExecute Some Queries\u003C/h3\u003E\n","\u003Cp\u003EGo to the \u003Cstrong\u003EZERO_TO_SNOWFLAKE_WITH_Public_Data\u003C/strong\u003E worksheet and change the warehouse to use the new warehouse you created in the last section. Your worksheet context should be the following:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole:\u003C/strong\u003E \u003Ccode\u003ESYSADMIN\u003C/code\u003E\n\u003Cstrong\u003EWarehouse:\u003C/strong\u003E \u003Ccode\u003EANALYTICS_WH (L)\u003C/code\u003E\n\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003EPublic_Data\u003C/code\u003E\n\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003EPUBLIC\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_1.png\" alt=\"sample data query results\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERun the following query to see a sample of the \u003Ccode\u003Ecompany_metadata\u003C/code\u003E data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT * FROM company_metadata;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_2_new.png\" alt=\"sample data query results\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, let's look at the performance of these companies in the stock market. Run the queries below in the worksheet.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClosing Price Statistics:\u003C/strong\u003E First, calculate the daily return of a stock (the percent change in the stock price from the close of the previous day to the close of the current day) and 5-day moving average from closing prices (which helps smooth out daily price fluctuations to identify trends).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT\n    meta.primary_ticker,\n    meta.company_name,\n    ts.date,\n    ts.value AS post_market_close,\n    (ts.value / LAG(ts.value, 1) OVER (PARTITION BY meta.primary_ticker ORDER BY ts.date))::DOUBLE AS daily_return,\n    AVG(ts.value) OVER (PARTITION BY meta.primary_ticker ORDER BY ts.date ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) AS five_day_moving_avg_price\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES ts\nINNER JOIN company_metadata meta\nON ts.ticker = meta.primary_ticker\nWHERE ts.variable_name = 'Post-Market Close';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_3.png\" alt=\"post-market close query results\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you have defined a particular database in the worksheet and want to use a table from a different database, you must fully qualify the reference to the other table by providing its database and schema name.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETrading Volume Statistics:\u003C/strong\u003E Then, calculate the trading volume change from one day to the next to see if there's an increase or decrease in trading activity. This can be a sign of increasing or decreasing interest in a stock.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT\n    meta.primary_ticker,\n    meta.company_name,\n    ts.date,\n    ts.value AS nasdaq_volume,\n    (ts.value / LAG(ts.value, 1) OVER (PARTITION BY meta.primary_ticker ORDER BY ts.date))::DOUBLE AS volume_change\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES ts\nINNER JOIN company_metadata meta\nON ts.ticker = meta.primary_ticker\nWHERE ts.variable_name = 'Nasdaq Volume';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_3b.png\" alt=\"volume query results\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EUse the Result Cache\u003C/h3\u003E\n","\u003Cp\u003ESnowflake has a result cache that holds the results of every query executed in the past 24 hours. These are available across warehouses, so query results returned to one user are available to any other user on the system who executes the same query, provided the underlying data has not changed. Not only do these repeated queries return extremely fast, but they also use no compute credits.\u003C/p\u003E\n","\u003Cp\u003ELet's see the result cache in action by running the exact same query again.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT\n    meta.primary_ticker,\n    meta.company_name,\n    ts.date,\n    ts.value AS post_market_close,\n    (ts.value / LAG(ts.value, 1) OVER (PARTITION BY primary_ticker ORDER BY ts.date))::DOUBLE AS daily_return,\n    AVG(ts.value) OVER (PARTITION BY primary_ticker ORDER BY date ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) AS five_day_moving_avg_price\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES ts\nINNER JOIN company_metadata meta\nON ts.ticker = meta.primary_ticker\nWHERE variable_name = 'Post-Market Close';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003EQuery Details\u003C/strong\u003E pane on the right, note that the query runs significantly faster because the results have been cached.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_4.png\" alt=\"cached query duration\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EClone a Table\u003C/h3\u003E\n","\u003Cp\u003ESnowflake allows you to create clones, also known as "zero-copy clones" of tables, schemas, and databases in seconds. When a clone is created, Snowflake takes a snapshot of data present in the source object and makes it available to the cloned object. The cloned object is writable and independent of the clone source. Therefore, changes made to either the source object or the clone object are not included in the other.\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003EA popular use case for zero-copy cloning is to clone a production environment for use by Development & Testing teams to test and experiment without adversely impacting the production environment and eliminating the need to set up and manage two separate environments.\u003C/em\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EZero-Copy Cloning\u003C/strong\u003E\nA massive benefit of zero-copy cloning is that the underlying data is not copied. Only the metadata and pointers to the underlying data change. Hence, clones are “zero-copy" and storage requirements are not doubled when the data is cloned. Most data warehouses cannot do this, but for Snowflake it is easy!\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ERun the following command in the worksheet to create a development (dev) table clone of the \u003Ccode\u003Ecompany_metadata\u003C/code\u003E table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE TABLE company_metadata_dev CLONE company_metadata;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick the three dots (\u003Cstrong\u003E...\u003C/strong\u003E) in the left pane and select \u003Cstrong\u003ERefresh\u003C/strong\u003E. Expand the object tree under the \u003Ccode\u003EPublic_Data\u003C/code\u003E database and verify that you see a new table named \u003Ccode\u003Ecompany_metadata_dev\u003C/code\u003E. Your Development team now can do whatever they want with this table, including updating or deleting it, without impacting the \u003Ccode\u003Ecompany_metadata\u003C/code\u003E table or any other object.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_6_new.png\" alt=\"trips_dev table\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EJoining Tables\u003C/h3\u003E\n","\u003Cp\u003EWe will now join the JSON SEC filing datasets together to investigate the revenue of one CPG company, Kraft Heinz. Run the query below to join \u003Ccode\u003ESEC_FILINGS_INDEX\u003C/code\u003E to \u003Ccode\u003ESEC_FILINGS_ATTRIBUTES\u003C/code\u003E to see how Kraft Heinz (KHC) business segments have performed over time:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EWITH data_prep AS (\n    SELECT \n        idx.cik,\n        idx.company_name,\n        idx.adsh,\n        idx.form_type,\n        att.measure_description,\n        CAST(att.value AS DOUBLE) AS value,\n        att.period_start_date,\n        att.period_end_date,\n        att.covered_qtrs,\n        TRIM(att.metadata:"ProductOrService"::STRING) AS product\n    FROM sec_filings_attributes_view att\n    JOIN sec_filings_index_view idx\n        ON idx.cik = att.cik AND idx.adsh = att.adsh\n    WHERE idx.cik = '0001637459'\n        AND idx.form_type IN ('10-K', '10-Q')\n        AND LOWER(att.measure_description) = 'net sales'\n        AND (att.metadata IS NULL OR OBJECT_KEYS(att.metadata) = ARRAY_CONSTRUCT('ProductOrService'))\n        AND att.covered_qtrs IN (1, 4)\n        AND value > 0\n    QUALIFY ROW_NUMBER() OVER (\n        PARTITION BY idx.cik, idx.company_name, att.measure_description, att.period_start_date, att.period_end_date, att.covered_qtrs, product\n        ORDER BY idx.filed_date DESC\n    ) = 1\n)\n\nSELECT\n    company_name,\n    measure_description,\n    product,\n    period_end_date,\n    CASE\n        WHEN covered_qtrs = 1 THEN value\n        WHEN covered_qtrs = 4 THEN value - SUM(value) OVER (\n            PARTITION BY cik, measure_description, product, YEAR(period_end_date)\n            ORDER BY period_end_date\n            ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING\n        )\n    END AS quarterly_value\nFROM data_prep\nORDER BY product, period_end_date;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_8_1.png\" alt=\"weather results\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUsing Time Travel\u003C/h2\u003E\n","\u003Cp\u003ESnowflake's powerful Time Travel feature enables accessing historical data, as well as the objects storing the data, at any point within a period of time. The default window is 24 hours and, if you are using Snowflake Enterprise Edition, can be increased up to 90 days. Most data warehouses cannot offer this functionality, but - you guessed it - Snowflake makes it easy!\u003C/p\u003E\n","\u003Cp\u003ESome useful applications include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERestoring data-related objects such as tables, schemas, and databases that may have been deleted.\u003C/li\u003E\u003Cli\u003EDuplicating and backing up data from key points in the past.\u003C/li\u003E\u003Cli\u003EAnalyzing data usage and manipulation over specified periods of time.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDrop and Undrop a Table\u003C/h3\u003E\n","\u003Cp\u003EFirst let's see how we can restore data objects that have been accidentally or intentionally deleted.\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003EZERO_TO_SNOWFLAKE_WITH_Public_Data\u003C/code\u003E worksheet, run the following DROP command to remove the \u003Ccode\u003ESEC_FILINGS_INDEX\u003C/code\u003E table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EDROP TABLE sec_filings_index;\n\n-- Run a query on the table:\nSELECT * FROM sec_filings_index LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the results pane at the bottom, you should see an error because the underlying table has been dropped:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_1.png\" alt=\"table dropped error\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, restore the table:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUNDROP TABLE sec_filings_index;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe SEC filing index table should be restored. Verify by running the following query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT * FROM sec_filings_index LIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_2.png\" alt=\"restored table result\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERoll Back a Table\u003C/h3\u003E\n","\u003Cp\u003ELet's roll back the \u003Ccode\u003ECOMPANY_METADATA\u003C/code\u003E table in the \u003Ccode\u003EPublic_Data\u003C/code\u003E database to a previous state to fix an unintentional DML error that replaces all the company names in the table with the word "oops".\u003C/p\u003E\n","\u003Cp\u003EFirst, run the following SQL statements to switch your worksheet to the proper context:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE sysadmin;\n\nUSE WAREHOUSE compute_wh;\n\nUSE DATABASE Public_Data;\n\nUSE SCHEMA public;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the following command to replace all of the company names in the table with the word "oops":\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUPDATE company_metadata SET company_name = 'oops';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, run a query that returns the companies. Notice that the company names are all the same:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT *\nFROM company_metadata;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_3.png\" alt=\"one row result\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENormally we would need to scramble and hope we have a backup lying around. In Snowflake, we can simply run a command to find the query ID of the last \u003Ccode\u003EUPDATE\u003C/code\u003E command and store it in a variable named \u003Ccode\u003E$QUERY_ID\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EUse Time Travel to recreate the table with the correct company names and verify the company names have been restored:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E-- Set the session variable for the query_id\nSET query_id = (\n  SELECT query_id\n  FROM TABLE(information_schema.query_history_by_session(result_limit=>5))\n  WHERE query_text LIKE 'UPDATE%'\n  ORDER BY start_time DESC\n  LIMIT 1\n);\n\n-- Use the session variable with the identifier syntax (e.g., $query_id)\nCREATE OR REPLACE TABLE company_metadata AS\nSELECT *\nFROM company_metadata\nBEFORE (STATEMENT => $query_id);\n\n-- Verify the company names have been restored\nSELECT *\nFROM company_metadata;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_4.png\" alt=\"restored names result\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWorking with Roles, Account Admin, & Account Usage\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we will explore aspects of Snowflake's access control security model, such as creating a role and granting it specific permissions. We will also explore other usage of the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E (Account Administrator) role.\u003C/p\u003E\n","\u003Cp\u003EContinuing with the lab story, let's assume a junior DBA has joined our internal analytics team, and we want to create a new role for them with less privileges than the system-defined, default role of \u003Ccode\u003ESYSADMIN\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERole-Based Access Control\u003C/strong\u003E\nSnowflake offers very powerful and granular access control that dictates the objects and functionality a user can access, as well as the level of access they have. For more details, check out the \u003Ca href=\"https://docs.snowflake.com/user-guide/security-access-control-overview\"\u003ESnowflake documentation\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate a New Role and Add a User\u003C/h3\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003EZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA\u003C/code\u003E worksheet, switch to the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role to create a new role. \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E encapsulates the \u003Ccode\u003ESYSADMIN\u003C/code\u003E and \u003Ccode\u003ESECURITYADMIN\u003C/code\u003E system-defined roles. It is the top-level role in the account and should be granted only to a limited number of users.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE accountadmin;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice that, in the top right of the worksheet, the context has changed to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_1.png\" alt=\"ACCOUNTADMIN context\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBefore a role can be used for access control, at least one user must be assigned to it. So let's create a new role named \u003Ccode\u003EJUNIOR_DBA\u003C/code\u003E and assign it to your Snowflake user. To complete this task, you need to know your username, which is the name you used to log in to the UI.\u003C/p\u003E\n","\u003Cp\u003EUse the following commands to create the role and assign it to you. Before you run the GRANT ROLE command, replace \u003Ccode\u003EYOUR_USERNAME_GOES_HERE\u003C/code\u003E with your username:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ECREATE ROLE junior_dba;\n\nGRANT ROLE junior_dba TO USER YOUR_USERNAME_GOES_HERE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you try to perform this operation while in a role such as \u003Ccode\u003ESYSADMIN\u003C/code\u003E, it would fail due to insufficient privileges. By default (and design), the \u003Ccode\u003ESYSADMIN\u003C/code\u003E role cannot create new roles or users.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EChange your worksheet context to the new \u003Ccode\u003EJUNIOR_DBA\u003C/code\u003E role:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE junior_dba;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the top right of the worksheet, notice that the context has changed to reflect the \u003Ccode\u003EJUNIOR_DBA\u003C/code\u003E role.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_2.png\" alt=\"JUNIOR_DBA context\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAlso, the warehouse is not selected because the newly created role does not have usage privileges on any warehouse. Let's fix it by switching back to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role and grant usage privileges to \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E warehouse.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE accountadmin;\n\nGRANT USAGE ON WAREHOUSE compute_wh TO ROLE junior_dba;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESwitch back to the \u003Ccode\u003EJUNIOR_DBA\u003C/code\u003E role. You should be able to use \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E now.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE junior_dba;\n\nUSE WAREHOUSE compute_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, you can notice that in the database object browser panel on the left, the \u003Ccode\u003EPublic_Data\u003C/code\u003E and \u003Ccode\u003ESNOWFLAKE_PUBLIC_DATA_FREE\u003C/code\u003E databases no longer appear. This is because the \u003Ccode\u003EJUNIOR_DBA\u003C/code\u003E role does not have privileges to access them.\u003C/p\u003E\n","\u003Cp\u003ESwitch back to the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role and grant the \u003Ccode\u003EJUNIOR_DBA\u003C/code\u003E the USAGE privilege required to view and use the \u003Ccode\u003EPublic_data\u003C/code\u003E and \u003Ccode\u003ESNOWFLAKE_PUBLIC_DATA_FREE\u003C/code\u003E databases. Note that the Snowflake Public Data database from the Marketplace uses \u003Ccode\u003EGRANT IMPORTED PRIVILEGES\u003C/code\u003E, instead of \u003Ccode\u003EGRANT USAGE\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE accountadmin;\n\nGRANT USAGE ON DATABASE Public_Data TO ROLE junior_dba;\n\nGRANT IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE_PUBLIC_DATA_FREE TO ROLE junior_dba;\n\nUSE ROLE junior_dba;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice that the \u003Ccode\u003EPublic_Data\u003C/code\u003E and \u003Ccode\u003ESNOWFLAKE_PUBLIC_DATA_FREE\u003C/code\u003E databases now appear in the database object browser panel on the left. If they don't appear, try clicking \u003Cstrong\u003E...\u003C/strong\u003E in the panel, then clicking \u003Cstrong\u003ERefresh\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_3.png\" alt=\"object browser panel with databases\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EView the Account Administrator UI\u003C/h3\u003E\n","\u003Cp\u003ELet's change our access control role back to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E to see other areas of the UI accessible only to this role. However, to perform this task, use the UI instead of the worksheet.\u003C/p\u003E\n","\u003Cp\u003EFirst, click the \u003Cstrong\u003EHome\u003C/strong\u003E icon in the top left corner of the worksheet. Then, in the bottom left corner of the UI, click your name to display the user preferences menu. In the menu, go to \u003Cstrong\u003ESwitch Role\u003C/strong\u003E and select \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_4.png\" alt=\"switch UI role\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERoles in User Preference vs Worksheet\u003C/strong\u003E\nWhy did we use the user preference menu to change the role instead of the worksheet? The UI session and each worksheet have their own separate roles. The UI session role controls the elements you can see and access in the UI, whereas the worksheet role controls only the objects and actions you can access within the role.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ENotice that once you switch the UI session to the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role, new tabs are available under \u003Cstrong\u003EAdmin\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch4\u003EAdmin > Cost Management\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_5.png\" alt=\"account usage\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe <a href="https://app.snowflake.com/_deeplink/#/account/usage?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">\u003Cstrong\u003ECost Management\u003C/strong\u003E tab</a> shows your usage of Snowflake credits, with filters by account and consumption types:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EOrganization\u003C/strong\u003E: Credit usage across all the accounts in your organization.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECompute\u003C/strong\u003E: Credits consumed by the virtual warehouses in the current account.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStorage\u003C/strong\u003E: Average amount of data stored in all databases, internal stages, and Snowflake Failsafe in the current account for the past month.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Transfer\u003C/strong\u003E: Average amount of data transferred out of the region (for the current account) into other regions for the past month.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EAdmin > Security\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_6.png\" alt=\"account usage\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ESecurity\u003C/strong\u003E tab contains network policies created for the Snowflake account. New network policies can be created by selecting “+ Network Policy” at the top right hand side of the page.\u003C/p\u003E\n","\u003Ch4\u003EAdmin > Billing & Terms\u003C/h4\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EBilling & Terms\u003C/strong\u003E tab contains the payment method for the account:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf you are a Snowflake contract customer, the tab shows the name associated with your contract information.\u003C/li\u003E\u003Cli\u003EIf you are an on-demand Snowflake customer, the tab shows the credit card used to pay month-to-month, if one has been entered. If no credit card is on file, you can add one to continue using Snowflake when your trial ends.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor the next section, stay in the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role for the UI session.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESharing Data Securely via Snowflake Marketplace\u003C/h2\u003E\n","\u003Cp\u003ESnowflake enables data access between accounts through the secure data sharing features. Shares are created by data providers and imported by data consumers, either through their own Snowflake account or a provisioned Snowflake Reader account. The consumer can be an external entity or a different internal business unit that is required to have its own unique Snowflake account.\u003C/p\u003E\n","\u003Cp\u003EWith secure data sharing:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThere is only one copy of the data that lives in the data provider's account.\u003C/li\u003E\u003Cli\u003EShared data is always live, real-time, and immediately available to consumers.\u003C/li\u003E\u003Cli\u003EProviders can establish revocable, fine-grained access to shares.\u003C/li\u003E\u003Cli\u003EData sharing is simple and safe, especially compared to older data sharing methods, which were often manual and insecure, such as transferring large \u003Ccode\u003E.csv\u003C/code\u003E files across the internet.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECross-region & cross-cloud data sharing\u003C/strong\u003E To share data across regions or cloud platforms, you must set up replication. This is outside the scope of this lab, but more information is available in \u003Ca href=\"/trending/what-is-data-replication\"\u003Ethis Snowflake article\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ESnowflake uses secure data sharing to provide account usage data and sample data sets to all Snowflake accounts. In this capacity, Snowflake acts as the data provider of the data and all other accounts.\u003C/p\u003E\n","\u003Cp\u003ESecure data sharing also powers the Snowflake Data Marketplace, which is available to all Snowflake customers and allows you to discover and access third-party datasets from numerous data providers and SaaS vendors. Again, in this data sharing model, the data doesn't leave the provider's account and you can use the datasets without any transformation.\u003C/p\u003E\n","\u003Ch3\u003EView Existing Shares\u003C/h3\u003E\n","\u003Cp\u003EIn the home page, navigate to \u003Cstrong\u003EData\u003C/strong\u003E > \u003Cstrong\u003EDatabases\u003C/strong\u003E. In the <a href="https://app.snowflake.com/_deeplink/#/data/databases?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink" class="_deeplink">list of databases</a>, look at the \u003Cstrong\u003ESOURCE\u003C/strong\u003E column. You should see one database with \u003Ccode\u003ELocal\u003C/code\u003E in the column, which we created previously in the lab. The other database, \u003Ccode\u003ESNOWFLAKE\u003C/code\u003E, shows \u003Ccode\u003EShare\u003C/code\u003E in the column, indicating it's shared from a provider.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_1.png\" alt=\"arrow over database icon\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate an Outbound Share\u003C/h3\u003E\n","\u003Cp\u003EAssume we are the Account Administrator for our Snowflake account. We have a trusted partner who wants to analyze our data on a near real-time basis. This partner also has their own Snowflake account in the same region as our account. We can use secure data sharing to allow them to access this information.\u003C/p\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EData Products\u003C/strong\u003E > \u003Cstrong\u003EPrivate Sharing\u003C/strong\u003E, then at the top of the tab click \u003Cstrong\u003EShared By Your Account\u003C/strong\u003E. Click the \u003Cstrong\u003EShare\u003C/strong\u003E button in the top right corner and select \u003Cstrong\u003ECreate a Direct Share\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_2.png\" alt=\"shares outbound button\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E+ Select Data\u003C/strong\u003E and navigate to the database and schema you want to share. Select the tables and click the \u003Cstrong\u003EDone\u003C/strong\u003E button:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_3.png\" alt=\"share fields\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe default name of the share is a generic name with a random numeric value appended. Edit the default name to a more descriptive value that will help identify the share in the future (e.g. \u003Ccode\u003EZERO_TO_SNOWFLAKE_SHARED_DATA\u003C/code\u003E). You can also add a comment.\u003C/p\u003E\n","\u003Cp\u003EIn a real-world scenario, the Account Administrator would next add one or more consumer accounts to the share, but we'll stop here for the purposes of this lab.\u003C/p\u003E\n","\u003Cp\u003EClick the \u003Cstrong\u003ECreate Share\u003C/strong\u003E button at the bottom of the dialog. The dialog closes and the page shows the secure share you created.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_5.png\" alt=\"TRIPS_SHARE share\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can add consumers, add/change the description, and edit the objects in the share at any time. In the page, click the \u003Cstrong\u003E<\u003C/strong\u003E button next to the share name to return to the \u003Cstrong\u003EShare with Other Accounts\u003C/strong\u003E page.\u003C/p\u003E\n","\u003Cp\u003EWe've demonstrated how it only takes seconds to give other accounts access to data in your Snowflake account in a secure manner with no copying or transferring of data required!\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides several ways to securely share data without compromising confidentiality. In addition to tables, you can share secure views, secure UDFs (user-defined functions), and other secure objects. For more details about using these methods to share data while preventing access to sensitive information, see the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-sharing-secure-views.html\"\u003ESnowflake documentation\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EResetting Your Snowflake Environment\u003C/h2\u003E\n","\u003Cp\u003EIf you would like to reset your environment by deleting all the objects created as part of this lab, ensure you are using the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role in the worksheet and run the following commands to drop the objects we created in the lab:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE accountadmin;\n\nDROP DATABASE IF EXISTS Public_Data;\n\nDROP WAREHOUSE IF EXISTS analytics_wh;\n\nDROP ROLE IF EXISTS junior_dba;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion & Next Steps\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on completing this introductory lab exercise! You've mastered the Snowflake basics and are ready to apply these fundamentals to your own data. Be sure to reference this guide if you ever need a refresher.\u003C/p\u003E\n","\u003Cp\u003EWe encourage you to continue with your free trial by loading your own sample or production data and by using some of the more advanced capabilities of Snowflake not covered in this lab.\u003C/p\u003E\n","\u003Ch3\u003EAdditional Resources:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELearn more about the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#using-snowsight\"\u003ESnowsight\u003C/a\u003E docs.\u003C/li\u003E\u003Cli\u003ERead the \u003Ca href=\"/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/\"\u003EDefinitive Guide to Maximizing Your Free Trial\u003C/a\u003E document.\u003C/li\u003E\u003Cli\u003EAttend a \u003Ca href=\"/about/events/\"\u003ESnowflake virtual or in-person event\u003C/a\u003E to learn more about our capabilities and customers.\u003C/li\u003E\u003Cli\u003EJoin the \u003Ca href=\"https://community.snowflake.com/s/topic/0TO0Z000000wmFQWAY/getting-started-with-snowflake\"\u003ESnowflake Community\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ESign up for \u003Ca href=\"https://community.snowflake.com/s/article/Getting-Access-to-Snowflake-University\"\u003ESnowflake University\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EContact our \u003Ca href=\"/free-trial-contact-sales/\"\u003ESales Team\u003C/a\u003E to learn more.\u003C/li\u003E\u003Cli\u003EAccess Snowflake Public Data's analytics-ready data on \u003Ca href=\"https://app.snowflake.com/marketplace/providers/GZTSZAS2KCS/Snowflake%20Public%20Data%20Products\"\u003ESnowflake Marketplace\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat we've covered:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to create stages, databases, tables, views, and virtual warehouses.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to load structured and semi-structured data.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to consume Snowflake Public data from the \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZAS2KF7/\"\u003ESnowflake Data Marketplace\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to perform analytical queries on data in Snowflake, including joins between tables.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to clone objects.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to undo user errors using Time Travel.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to create roles and users, and grant them privileges.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to securely and easily share data with other accounts.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Create%20and%20Manage%20Snowflake%20Objects%20like%20a%20Pro/Create%20and%20Manage%20Snowflake%20Objects%20like%20a%20Pro.ipynb\"\u003EFork repo on GitHub\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/content/dam/snowflake-site/developers/2024/05/Citibike-Data-Analysis_-Create-and-manage-Snowflake-Objects-like-a-pro.pdf\"\u003EDownload Reference Architecture\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://youtu.be/Dj8aAoEOfrw?list=TLGGDV2-mOUmeyYyMDA5MjAyNQ\"\u003EWatch the Demo\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"title":"Getting Started with Snowflake","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\n\nWelcome to Snowflake! This entry-level guide, designed for database and data warehouse administrators and architects, will help you navigate the Snowflake interface and introduce you to some of our core capabilities. [Sign up for a free 30-day trial of Snowflake](https://trial.snowflake.com) and follow along with this lab. Once we cover the basics, you'll be ready to start processing your own data and diving into Snowflake's more advanced features!\n\n### Free Virtual Hands-on Lab\nThis Snowflake Guide is available as a free, instructor-led Virtual Hands on Lab. [Sign up for the VHOL today](/virtual-hands-on-lab/).\n\n### Prerequisites:\n- Use of the [Snowflake free 30-day trial environment](https://trial.snowflake.com)\n- Basic knowledge of SQL, database concepts, and objects\n- Familiarity with CSV (comma-delimited) files and JSON semi-structured data\n\n### What You'll Learn:\n- How to create stages, databases, tables, views, and virtual warehouses.\n- How to load structured and semi-structured data.\n- How to consume Snowflake Public Data data from the [Snowflake Data Marketplace](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free?search=Snowflake+Public+Data+%28Free%29).\n- How to perform analytical queries on data in Snowflake, including joins between tables.\n- How to clone objects.\n- How to undo user errors using Time Travel.\n- How to create roles and users, and grant them privileges.\n- How to securely and easily share data with other accounts.\n\n### Data You'll Use:\n**Snowflake Public Data** is a next generation data company creating a real-time view of the world's economy with analytics-ready data exclusively on Snowflake Marketplace. Initially focused on consumer insights, Snowflake Public Data enables you to access external data directly in your Snowflake instance — no ETL required.\n\nThis lab will use the following Snowflake Public Data datasets:\n- Snowflake Public Data (Free)\n\nCheck out Snowflake Public Data's [Snowflake Public Data (Free)](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free?search=Snowflake+Public+Data+%28Free%29) product and [explore all public sources](https://app.snowflake.com/marketplace/providers/GZTSZAS2KCS/Snowflake%20Public%20Data%20Products) Snowflake Public Data offers on the Snowflake Marketplace.\n\u003C!-- ------------------------ --\u003E\n\n## Prepare Your Lab Environment\n\n\nIf you haven't already, register for a [Snowflake free 30-day trial](https://signup.snowflake.com/developers?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides). This lab assumes you are using a new Snowflake account created by registering for a trial.\n\nThe Snowflake edition (Standard, Enterprise, Business Critical, etc.), cloud provider (AWS, Azure, GCP), and Region (US East, EU, etc.) you use for this lab, do not matter. However, we suggest you select the region that is physically closest to you and Enterprise, our most popular offering, as your Snowflake edition.\n\nAfter registering, you will receive an email with an activation link and URL for accessing your Snowflake account.\n\n### Logging into the Snowflake User Interface (UI)\nOpen a browser window and enter the URL of your Snowflake 30-day trial environment that was sent with your registration email. Enter the username and password that you specified during the registration:\n\n![login screen](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_1.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## The Snowflake ​User Interface\n\n\n\u003E \n\u003E \n\u003E  **About the screenshots, sample code, and environment**\nScreenshots in this lab depict examples; results may vary slightly from what you see when you complete the exercises.\n\n### Navigating the Snowflake UI\n\nLet's get you acquainted with Snowflake! This section covers the basic components of the user interface.\n\n![snowflake navbar](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_2.png)\n\n### Projects \u003E Worksheets\n\nUnder **Projects** on the left-hand panel, select the ​**Worksheets​** tab or go directly by \u003Ca href=\"https://app.snowflake.com/_deeplink/worksheets?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003Eclicking here\u003C/a\u003E. This provides an interface for submitting SQL queries, performing DDL and DML operations, and viewing results as your queries or operations complete. A new worksheet is created by clicking **`+`** on the top right.\n\n![worksheets tab main](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_3.png)\n\n### Worksheet Structure \n\n![worksheets tab details](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_4.png)\n\nThe top left corner contains the following:\n- **Snowflake** icon: Use this to get back to the main console/close the worksheet.\n- **Worksheet_name** drop-down: The default name is the timestamp when the worksheet was created. Click the timestamp to edit the worksheet name. The drop-down also displays additional actions you can perform for the worksheet.\n- **Filters** button: Custom filters are special keywords that resolve as a subquery or list of values.\n\nThe top right corner contains the following:\n- **Context** box: This lets Snowflake know which role and warehouse to use during this session. It can be changed via the UI or SQL commands.\n- **Share** button: Open the sharing menu to share to other users or copy the link to the worksheet.\n- **Play/Run** button: Run the SQL statement where the cursor currently is or multiple selected statements.\n\nThe middle pane contains the following:\n- Drop-down at the top for setting the database/schema/object context for the worksheet.\n- General working area where you enter and execute queries and other SQL statements. \n\nThe middle-left panel contains the following:\n- **Worksheets** tab: Use this tab to quickly select and jump between different worksheets\n- **Databases** tab: Use this tab to view all of the database objects available to the current role\n- **Search** bar: database objects browser which enables you to explore all databases, schemas, tables, and views accessible by the role currently in use for the worksheet. \n\nThe bottom pane displays the results of queries and other operations. Also includes 4 options (**Object**, **Query**, **Result**, **Chart**) that open/close their respective panels on the UI. **Chart** opens a visualization panel for the returned results. More on this later.\n\nThe various panes on this page can be resized by adjusting their sliders. If you need more room in the worksheet, collapse the database objects browser in the left panel. Many of the screenshots in this guide keep this panel closed.\n\n\u003E \n\u003E \n\u003E  **Worksheets vs the UI**\nMost of the exercises in this lab are executed using pre-written SQL within this worksheet to save time. These tasks can also be done via the UI, but would require navigating back-and-forth between multiple UI tabs.\n\n### Projects \u003E Dashboards\n\nUnder **Projects** on the left-hand panel, select the ​**Dashboards​** tab or \u003Ca href=\"https://app.snowflake.com/_deeplink/dashboards?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003Eclick here\u003C/a\u003E. This tab allows you to create flexible displays of one or more charts (in the form of tiles, which can be rearranged). Tiles and widgets are produced by executing SQL queries that return results in a worksheet. Dashboards work at a variety of sizes with minimal configuration.\n\n![dashboards tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_5.png)\n\n### Data \u003E Databases\n\nUnder **Discover & Collaborate**, the **Catalog \u003E Database Explorer**​ tab shows information about the databases you have created or have permission to access. Click here to naviage to the \u003Ca href=\"https://app.snowflake.com/_deeplink/#/data/databases?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003EDatabase Explorer\u003C/a\u003E directly.  You can create, clone, drop, or transfer ownership of databases, as well as load data in the UI. Notice that a database already exists in your environment. However, we will not be using it in this lab.\n\n![databases tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_6.png)\n\n### Data Products \u003E Marketplace\n\nThe **Marketplace** tab is where any Snowflake customer can browse and consume data sets made available by providers. Click here to navigate direclty to the \u003Ca href=\"https://app.snowflake.com/_deeplink/marketplace?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003EMarketplace\u003C/a\u003E.There are two types of shared data: Public and Personalized. Public data is free data sets available for querying instantaneously. Personalized data requires reaching out to the provider of data for approval of sharing data.\n\n![marketplace tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_8.png)\n\n### Data Products \u003E Private Sharing\n\nUnder **Data Products**, the **Private Sharing** tab is where data sharing can be configured to easily and securely share Snowflake tables among separate Snowflake accounts or external users, without having to create a copy of the data.  Browse directly to Private Sharing by \u003Ca href=\"https://app.snowflake.com/_deeplink/#/data/shared/shared-with-you?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003Eclicking here\u003C/a\u003E.\n\n![shared data tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_7.png)\n\n### Monitoring \u003E Query History\n\nUnder **Monitoring** there are \u003Ca href=\"https://app.snowflake.com/_deeplink/#/compute/history/queries?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003Emultiple tabs\u003C/a\u003E for tracking your usage of your Snowflake account:\n\n- **Query History** is where previous queries are shown, along with filters that can be used to hone results (user, warehouse, status, query tag, etc.). View the details of all queries executed in the last 14 days from your Snowflake account. Click a query ID to drill into it for more information.\n- **Copy History** shows the status of copy commands run to ingest data into Snowflake.\n- **Task History** allows you to see the execution history for tasks and tasks graphs. (Tasks let you schedule the execution of SQL code. It is associated with a specific database and schema.)\n- **Dynamic Tables** is where you can use Snowsight to monitor dynamic table refreshes and examine dynamic tables and dynamic table graphs.\n- **Governance** tracks row- and column-level security, object tagging, data classification, access history, and more.\n\n![history tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_9.png)\n\n### Admin \u003E Warehouses\n\nUnder **Admin**, the **​Warehouses​** tab is where you set up and manage compute resources known as virtual warehouses to load or query data in Snowflake. A warehouse called COMPUTE_WH already exists in your environment.  \u003Ca href=\"https://app.snowflake.com/_deeplink/#/compute/warehouses?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003EClick here\u003C/a\u003E to go directly to the Warehouses tab.\n\n![warehouses tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_10.png)\n\n### Admin \u003E Cost Management\n\nUnder **Admin**, the **Cost Management** tab shows an overview of account consumption and budgets. It also includes details on all the resource monitors that have been created to control the number of credits that virtual warehouses consume. For each resource monitor, it shows the credit quota, type of monitoring, schedule, and actions performed when the virtual warehouse reaches its credit limit.\n\n![resource monitors tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_11.png)\n\n### Admin \u003E Users & Roles\n\nThe **Roles** sub-tab of the **Users & Roles** tab shows a list of the roles and their hierarchies. Roles can be created, reorganized, and granted to users in this tab. The roles can also be displayed in tabular/list format by selecting the **Table** sub-tab.\n\n![roles tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_12.png)\n\n### Users\n\nThe **Users** sub-tab of the **Users & Roles** tab shows a list of users in the account, default roles, and owner of the users. For a new account, no records are shown because no additional roles have been created. Permissions granted through your current role determine the information shown for this tab. To see all the information available on the tab, switch your role to `ACCOUNTADMIN`.\n\n![users tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_13.png)\n\nClicking on your username in the bottom right of the UI allows you to change your password, roles, and preferences. Snowflake has several system defined roles. You are currently in the default role of `SYSADMIN` and will stay in this role for the majority of the lab.\n\n![user preferences dropdown](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/3UIStory_14.png)\n\n\u003E \n\u003E \n\u003E  **SYSADMIN**\nThe `SYSADMIN` (aka System Administrator) role has privileges to create warehouses, databases, and other objects in an account.\nIn a real-world environment, you would use different roles for the tasks in this lab, and assign roles to your users. We will cover more on roles and Snowflake's access control model in Section 9 and you can find additional information in the [Snowflake documentation](https://docs.snowflake.com/user-guide/security-access-control-overview).\n\n\u003C!-- ------------------------ --\u003E\n\n## Data Lab: Stock Price & SEC Filings Data\n\n\n### The Lab Story\nYou work at a grocery retailer. You want to understand the performance of major consumer goods (CPG) companies in the US that supply your store. This lab takes a look at daily stock price data and quarterly and annual Securities Exchange Commission (SEC) company filings to understand the performance of the CPG landscape. Public companies are required to submit a quarterly and annual report to the SEC detailing their financial data.\n\nWe will start by collecting data from three different sources:\n1. Load company metadata `.csv` file.\n2. Load SEC filings from a semi-structured JSON format.\n3. Use the Snowflake Marketplace to find free stock price data in \"Snowflake Public Data (Free)\" from Snowflake Public Data.\n\n\u003C!-- ------------------------ --\u003E\n\n## Loading Structured Data into Snowflake: CSVs\n\n\nLet's start by preparing to load structured `.csv` data into Snowflake.\n\nWe are using company metadata developed from the Securities and Exchange Commission (SEC) that details the consumer packaged goods (CPG) companies we want to evaluate. The data has been exported and pre-staged for you in an Amazon AWS S3 bucket in the US-EAST region. It is in comma-delimited format with a single header line and double quotes enclosing all string values, including the field headings in the header line. This will be important when we configure the Snowflake table to store this data.\n\n\u003C!-- victoria image of the CPG data --\u003E\n\n\u003E \n\u003E \n\u003E **Free Datasets from Snowflake Public Data direct to your Snowflake instance:** The full dataset is available [**for free**](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free?search=Snowflake+Public+Data+%28Free%29) in Snowflake Marketplace from Snowflake Public Data -- no ETL required. For the purposes of this demo, we will focus on working with a subset of the data, staged in a csv file to learn how to load structured data into Snowflake.\n\n**Getting Data into Snowflake**\nData can be ingested into Snowflake from many locations by using the `COPY` command, Snowpipe auto-ingestion, external connectors, or third-party ETL/ELT solutions. For more information on getting data into Snowflake, see the [Snowflake documentation](https://docs.snowflake.com/guides-overview-loading-data). For the purposes of this lab, we use the `COPY` command and AWS S3 storage to load data manually. In a real-world scenario, you would more likely use an ETL solution or grab data directly from the Snowflake Marketplace!\n\n### Create a Database and Table\nEnsure you are using the `SYSADMIN` role by selecting your name at the top left, **Switch Role** \u003E **SYSADMIN**.\n\nNavigate to the \u003Ca href=\"https://app.snowflake.com/_deeplink/#/data/databases?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003E**Databases** tab\u003C/a\u003E. Click **Create**, name the database `Public_Data`, then click **CREATE**.\n\n![worksheet creation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Create_New_DB.png)\n\nNow navigate to the \u003Ca href=\"https://app.snowflake.com/_deeplink/worksheets?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003E**Worksheets** tab\u003C/a\u003E. You should see the worksheet we created in step 3.\n\n![new worksheet](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_3.png)\n\nWe need to set the context appropriately within the worksheet. In the upper right corner of the worksheet, click the box to the left of the **Share** button to show the context menu. Here we control the elements you can see and run from each worksheet. We are using the UI here to set the context. Later in the lab, we will accomplish the same thing via SQL commands within the worksheet.\n\nSelect the following context settings:\n\n**Role:** `SYSADMIN`\n**Warehouse:** `COMPUTE_WH`\n\n![context role and warehouse settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_4.png)\n\nNext, in the drop-down for the database, select the following context settings:\n\n**Database:** `Public_Data`\n**Schema:** `PUBLIC`\n\n![context database settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Choose_Schema.png)\n\n\u003E \n\u003E \n\u003E  **Data Definition Language (DDL) operations are free!**\nAll the DDL operations we have done so far do not require compute resources, so we can create all our objects for free.\n\nTo make working in the worksheet easier, let's rename it. In the top left corner, double-click the worksheet name, which is the timestamp when the worksheet was created, and change it to `ZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA`.\n\nNext we create a table called `COMPANY_METADATA` to use for loading the comma-delimited data. Instead of using the UI, we use the worksheet to run the DDL that creates the table. Copy the following SQL text into your worksheet:\n\n```SQL\nCREATE OR REPLACE TABLE company_metadata\n(cybersyn_company_id string,\ncompany_name string,\npermid_security_id string,\nprimary_ticker string,\nsecurity_name string,\nasset_class string,\nprimary_exchange_code string,\nprimary_exchange_name string,\nsecurity_status string,\nglobal_tickers variant,\nexchange_code variant,\npermid_quote_id variant);\n```\n\n\u003E \n\u003E \n\u003E  **Many Options to Run Commands.**\nSQL commands can be executed through the UI, via the **Worksheets** tab, using our SnowSQL command line tool, with a SQL editor of your choice via ODBC/JDBC, or through our other connectors (Python, Spark, etc.).\nAs mentioned earlier, to save time, we are performing most of the operations in this lab via pre-written SQL executed in the worksheet as opposed to using the UI.\n\nRun the query by placing your cursor anywhere in the SQL text and clicking the blue **Play/Run** button in the top right of the worksheet. Or use the keyboard shortcut [Ctrl]/[Cmd]+[Enter].\n\nVerify your `COMPANY_METADATA` table has been created. At the bottom of the worksheet, you should see a Results section displaying a `\"Table COMPANY_METADATA successfully created\"` message.\n\n![TRIPS confirmation message](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_5_new.png)\n\nNavigate to the **Databases** tab by clicking the **HOME** icon in the upper left corner of the worksheet. Then click **Data** \u003E **Databases**. In the list of databases, click `Public_Data` \u003E `PUBLIC` \u003E **TABLES** to see your newly created `COMPANY_METADATA` table. If you don't see any databases on the left, expand your browser because they may be hidden.\n\n![TRIPS table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_6_new.png)\n\nClick `COMPANY_METADATA` and the **Columns** tab to see the table structure you just created.\n\n![TRIPS table structure](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_7_new.png)\n\n### Create an External Stage\n\nWe are working with structured, comma-delimited data that has already been staged in a public, external S3 bucket. Before we can use this data, we first need to create a _stage_ that specifies the location of our external bucket.\n\n\u003E \n\u003E \n\u003E  For this lab, we are using an AWS-East bucket. To prevent data egress/transfer costs in the future, you should select a staging location from the same cloud provider and region as your Snowflake account.\n\nFrom the **Databases** tab, click the `Public_Data` database and `PUBLIC` schema. Click the **Create** button, then **Stage** \u003E **Amazon S3**.\n\n![stages create](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_8_new.png)\n\nIn the `Create Stage` dialog that opens, replace the following values in the SQL statement:\n\n**Stage Name**: `Public_Data_company_metadata`\n**URL**: `s3://sfquickstarts/zero_to_snowflake/cybersyn-consumer-company-metadata-csv/`\n\n**Note:** Make sure to include the final forward slash (`/`) at the end of the URL or you will encounter errors later when loading data from the bucket.\nAlso ensure you have removed 'credentials = (...)' statement which is not required. You can also comment it out like the picture below by using '--'. The create stage command should resemble the below picture or not include the 3rd line.\n\n\u003E \n\u003E \n\u003E  The S3 bucket for this lab is public so you can leave the credentials options in the statement empty. In a real-world scenario, the bucket used for an external stage would likely require key information.\n\n![create stage settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_9_new.png)\n\nNow let's take a look at the contents of the `Public_Data_company_metadata` stage. Navigate back to the **Worksheets** tab and open the `ZERO_TO_SNOWFLAKE_WITH_Public_Data` worksheet we made.\n\nAdd the following SQL statement below the previous code and then execute:\n\n```SQL\nLIST @Public_Data_company_metadata;\n```\n\nIn the results in the bottom pane, you should see the list of files in the stage:\n\n![worksheet result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_10_new.png)\n\n### Create a File Format\n\nBefore we can load the data into Snowflake, we have to create a file format that matches the data structure. In the worksheet, again add the following command below the rest and execute to create the file format:\n\n```SQL\nCREATE OR REPLACE FILE FORMAT csv\n    TYPE = 'CSV'\n    COMPRESSION = 'AUTO'  -- Automatically determines the compression of files\n    FIELD_DELIMITER = ','  -- Specifies comma as the field delimiter\n    RECORD_DELIMITER = '\\n'  -- Specifies newline as the record delimiter\n    SKIP_HEADER = 1  -- Skip the first line\n    FIELD_OPTIONALLY_ENCLOSED_BY = '\\042'  -- Fields are optionally enclosed by double quotes (ASCII code 34)\n    TRIM_SPACE = FALSE  -- Spaces are not trimmed from fields\n    ERROR_ON_COLUMN_COUNT_MISMATCH = FALSE  -- Does not raise an error if the number of fields in the data file varies\n    ESCAPE = 'NONE'  -- No escape character for special character escaping\n    ESCAPE_UNENCLOSED_FIELD = '\\134'  -- Backslash is the escape character for unenclosed fields\n    DATE_FORMAT = 'AUTO'  -- Automatically detects the date format\n    TIMESTAMP_FORMAT = 'AUTO'  -- Automatically detects the timestamp format\n    NULL_IF = ('')  -- Treats empty strings as NULL values\n    COMMENT = 'File format for ingesting data for zero to snowflake';\n```\n\n![create file format](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_11_new.png)\n\nVerify the file format has been created with the correct settings by executing the following command:\n\n```SQL\nSHOW FILE FORMATS IN DATABASE Public_Data;\n```\n\nThe file format created should be listed in the result:\n![create file format settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/4PreLoad_12_new.png)\n\n### Resize and Use a Warehouse for Data Loading\n\nWe will now use a virtual warehouse and the `COPY` command to initiate bulk loading of structured data into the Snowflake table we created.\n\nCompute resources are needed for loading data. Snowflake's compute nodes are called virtual warehouses and they can be dynamically sized up or out according to workload, whether you are loading data, running a query, or performing a DML operation. Each workload can have its own warehouse so there is no resource contention.\n\nNavigate to the \u003Ca href=\"https://app.snowflake.com/_deeplink/#/compute/warehouses?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003E**Warehouses** tab\u003C/a\u003E (under **Admin**). This is where you can view all of your existing warehouses, as well as analyze their usage trends.\n\nNote the **+ Warehouse** option in the upper right corner of the top. This is where you can quickly add a new warehouse. However, we want to use the existing warehouse `COMPUTE_WH` included in the 30-day trial environment.\n\nClick the row of the `COMPUTE_WH` warehouse. Then click the **[...]** in the upper right corner text above it to see the actions you can perform on the warehouse. We will use this warehouse to load the data from AWS S3.\n\n![compute warehouse configure](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_1.png)\n\nClick **Edit** to walk through the options of this warehouse and learn some of Snowflake's unique functionality.\n\n\u003E \n\u003E \n\u003E  If this account isn't using Snowflake Enterprise Edition (or higher), you will not see the **Mode** or **Clusters** options shown in the screenshot below. The multi-cluster warehouses feature is not used in this lab, but we will discuss it as a key capability of Snowflake.\n\n![warehouse configure settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_2.png)\n\n- The **Size** drop-down is where the capacity of the warehouse is selected. For larger data loading operations or more compute-intensive queries, a larger warehouse is recommended. The sizes translate to the underlying compute resources provisioned from the cloud provider (AWS, Azure, or GCP) where your Snowflake account is hosted. It also determines the number of credits consumed by the warehouse for each full hour it runs. The larger the size, the more compute resources from the cloud provider are allocated to the warehouse and the more credits it consumes. For example, the `4X-Large` setting consumes 128 credits for each full hour. This sizing can be changed up or down at any time with a simple click.\n\n- If you are using Snowflake Enterprise Edition (or higher) the **Query Acceleration** option is available. When it is enabled for a warehouse, it can improve overall warehouse performance by reducing the impact of outlier queries, which are queries that use more resources than the typical query. Leave this disabled \n\n- If you are using Snowflake Enterprise Edition (or higher) and the **Multi-cluster Warehouse** option is enabled, you will see additional options. This is where you can set up a warehouse to use multiple clusters of compute resources, up to 10 clusters. For example, if a `4X-Large` multi-cluster warehouse is assigned a maximum cluster size of 10, it can scale out to 10 times the compute resources powering that warehouse...and it can do this in seconds! However, note that this will increase the number of credits consumed by the warehouse to 1280 if all 10 clusters run for a full hour (128 credits/hour x 10 clusters). Multi-cluster is ideal for concurrency scenarios, such as many business analysts simultaneously running different queries using the same warehouse. In this use case, the various queries are allocated across multiple clusters to ensure they run quickly.\n\n- Under **Advanced Warehouse Options**, the options allow you to automatically suspend the warehouse when not in use so no credits are needlessly consumed. There is also an option to automatically resume a suspended warehouse so when a new workload is sent to it, it automatically starts back up. This functionality enables Snowflake's efficient \"pay only for what you use\" billing model which allows you to scale your resources when necessary and automatically scale down or turn off when not needed, nearly eliminating idle resources. Additionally, there is an option to change the Warehouse type from Standard to Snowpark-optimized. Snowpark-optmized warehouses provide 16x memory per node and are recommended for workloads that have large memory requirements such as ML training use cases using a stored procedure on a single virtual warehouse node. Leave this type as Standard\n\n\u003E \n\u003E \n\u003E  **Snowflake Compute vs Other Data Warehouses**\nMany of the virtual warehouse and compute capabilities we just covered, such as the ability to create, scale up, scale out, and auto-suspend/resume virtual warehouses are easy to use in Snowflake and can be done in seconds. For on-premise data warehouses, these capabilities are much more difficult, if not impossible, as they require significant physical hardware, over-provisioning of hardware for workload spikes, and significant configuration work, as well as additional challenges. Even other cloud-based data warehouses cannot scale up and out like Snowflake without significantly more configuration work and time.\n\n**Warning - Watch Your Spend!**\nDuring or after this lab, you should be careful about performing the following actions without good reason or you may burn through your $400 of free credits more quickly than desired:\n\n- Do not disable auto-suspend. If auto-suspend is disabled, your warehouses continues to run and consume credits even when not in use.\n- Do not use a warehouse size that is excessive given the workload. The larger the warehouse, the more credits are consumed.\n\nWe are going to use this virtual warehouse to load the structured data in the CSV files (stored in the AWS S3 bucket) into Snowflake. However, we are first going to change the size of the warehouse to increase the compute resources it uses. After the load, note the time taken and then, in a later step in this section, we will re-do the same load operation with an even larger warehouse, observing its faster load time.\n\nChange the **Size** of this data warehouse from `X-Small` to `Small`. then click the **Save Warehouse** button:\n\n![configure settings small](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_3.png)\n\n### Load the Data\n\nNow we can run a COPY command to load the data into the `COMPANY_METADATA` table we created earlier.\n\nNavigate back to the `ZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA` worksheet in the **Worksheets** tab. Make sure the worksheet context is correctly set:\n\n**Role:** `SYSADMIN`\n**Warehouse:** `COMPUTE_WH`\n**Database:** `Public_Data`\n**Schema:** `PUBLIC`\n\n![worksheet context](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_4.png)\n\nExecute the following statements in the worksheet to load the staged data into the table. This may take up to 30 seconds.\n\n```SQL\nCOPY INTO company_metadata FROM @Public_Data_company_metadata file_format=csv PATTERN = '.*csv.*' ON_ERROR = 'CONTINUE';\n```\n\nIn the result pane, you should see the status of each file that was loaded. Once the load is done, in the **Query Details** pane on the bottom right, you can scroll through the various statuses, error statistics, and visualizations for the last statement executed.\n\nNext, navigate to the **Query History** tab by clicking the **Home** icon and then **Activity** \u003E **Query History**. Select the query at the top of the list, which should be the COPY INTO statement that was last executed. Select the **Query Profile** tab and note the steps taken by the query to execute, query details, most expensive nodes, and additional statistics.\n\n![history and duration](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_6.png)\n\nNow let's reload the `COMPANY_METADATA` table with a larger warehouse to see the impact the additional compute resources have on the loading time.\n\nGo back to the worksheet and use the `TRUNCATE TABLE` command to clear the table of all data and metadata:\n```SQL\nTRUNCATE TABLE company_metadata;\n\n-- Verify that the table is empty by running the following command:\nSELECT * FROM company_metadata LIMIT 10;\n```\n\nThe result should show `Query produced no results`. Change the warehouse size to `LARGE` using the following `ALTER WAREHOUSE`:\n```SQL\nALTER WAREHOUSE compute_wh SET warehouse_size='large';\n\n-- Verify the change using the following SHOW WAREHOUSES:\nSHOW WAREHOUSES;\n```\n\n![resize context to large in UI step 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_7.png)\n\nThe size can also be changed using the UI by clicking on the worksheet context box, then the **Configure** (3-line) icon on the right side of the context box, and changing `Small` to `Large` in the **Size** drop-down:\n\n![resize context to large in UI step 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_8.png)\n\nExecute the same `COPY INTO` statement as before to load the same data again:\n\n```SQL\nCOPY INTO company_metadata FROM @Public_Data_company_metadata file_format=csv PATTERN = '.*csv.*' ON_ERROR = 'CONTINUE';\n```\n\nOnce the load is done, navigate back to the **Queries** page (**Home** icon \u003E **Activity** \u003E **Query History**). Compare the times of the two `COPY INTO` commands. The load using the `Large` warehouse was significantly faster.\n\n### Create a New Warehouse for Data Analytics\n\nLet's assume our internal analytics team wants to eliminate resource contention between their data loading/ETL workloads and the analytical end users using BI tools to query Snowflake. As mentioned earlier, Snowflake can easily do this by assigning different, appropriately-sized warehouses to various workloads. Since our company already has a warehouse for data loading, let's create a new warehouse for the end users running analytics. We will use this warehouse to perform analytics in the next section.\n\nNavigate to the **Admin** \u003E **Warehouses** tab, click **+ Warehouse**, and name the new warehouse and set the size to `Large`.\n\nIf you are using Snowflake Enterprise Edition (or higher) and **Multi-cluster Warehouses** is enabled, you will see additional settings:\n- Make sure **Max Clusters** is set to `1`.\n- Leave all the other settings at their defaults.\n\n![warehouse settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/5Load_10.png)\n\nClick the **Create Warehouse** button to create the warehouse.\n\n\u003C!-- ------------------------ --\u003E\n\n## Loading Semi-Structured Data into Snowflake: JSONs\n\n\n\u003E \n\u003E \n\u003E  This section requires loading additional data and, therefore, provides a review of data loading while also introducing loading semi-structured data.\n\nGoing back to the lab's example, our company's analytics team wants to evaluate the performance of CPG companies through the lens of their reported metrics in SEC filings. To do this, in this section, we will:\n\n- Load SEC filing data in semi-structured JSON format held in a public S3 bucket.\n- Create a view and query the JSON data using SQL dot notation.\n\nThe JSON data consists of SEC filings provided by *Snowflake Public Data*, detailing the historical performance of consumer-packaged goods companies from 2019-2023. It is also staged on AWS S3. If viewed in a text editor, the raw JSON in the GZ files looks like:\n\n![raw JSON sample](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_1_1.png)\n\n_(The full dataset available [**for free**](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/) in Snowflake Marketplace from Snowflake Public Data -- no ETL required. For the purposes of this demo, we will focus on working with the semi-structured JSON file to learn how to load structured data into Snowflake.)_\n\n\u003E \n\u003E \n\u003E  **SEMI-STRUCTURED DATA**\nSnowflake can easily load and query semi-structured data such as JSON, Parquet, or Avro without transformation. This is a key Snowflake feature because an increasing amount of business-relevant data being generated today is semi-structured, and many traditional data warehouses cannot easily load and query such data. Snowflake makes it easy!\n\n### Create a New Database and Table for the Data\n\n\u003E \n\u003E \n\u003E  **Executing Multiple Commands** Remember that you need to execute each command individually. However, you can execute them in sequence together by selecting all of the commands and then clicking the **Play/Run** button (or using the keyboard shortcut).\n\nNext, let's create two tables, `SEC_FILINGS_INDEX` and `SEC_FILINGS_ATTRIBUTES` to use for loading JSON data. In the worksheet, execute the following `CREATE TABLE` commands:\n\n```SQL\nCREATE TABLE sec_filings_index (v variant);\n\nCREATE TABLE sec_filings_attributes (v variant);\n```\n\nNote that Snowflake has a special data type called `VARIANT` that allows storing the entire JSON object as a single row and querying the object directly.\n\n\u003E \n\u003E \n\u003E  **Semi-Structured Data Magic**\nThe `VARIANT` data type allows Snowflake to ingest semi-structured data without having to predefine the schema.\n\nIn the results pane at the bottom of the worksheet, verify that your tables, `SEC_FILINGS_INDEX` and `SEC_FILINGS_ATTRIBUTES`, were created:\n\n![success message](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_2_1_new.png)\n\n### Create Another External Stage\n\nIn the `ZERO_TO_SNOWFLAKE_WITH_Public_Data` worksheet, use the following command to create a stage that points to the bucket where the semi-structured JSON data is stored on AWS S3:\n\n```SQL\nCREATE STAGE Public_Data_sec_filings\nurl = 's3://sfquickstarts/zero_to_snowflake/cybersyn_cpg_sec_filings/';\n```\n\nNow let's take a look at the contents of the `Public_Data_sec_filings` stage. Execute the following `LIST` command to display the list of files:\n\n```SQL\nLIST @Public_Data_sec_filings;\n```\n\nIn the results pane, you should see a list of `.gz` files from S3:\n![results output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_3_1_new.png)\n\n### Load and Verify the Semi-structured Data\n\nWe will now use a warehouse to load the data from an S3 bucket into the tables we created earlier. In the `ZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA worksheet, execute the `COPY` command below to load the data.\n\nNote that you can specify a `FILE FORMAT` object inline in the command. In the previous section where we loaded structured data in CSV format, we had to define a file format to support the CSV structure. Because the JSON data here is well-formed, we are able to simply specify the JSON type and use all the default settings:\n\n```SQL\nCOPY INTO sec_filings_index\nFROM @Public_Data_sec_filings/cybersyn_sec_report_index.json.gz\n    file_format = (type = json strip_outer_array = true);\n\nCOPY INTO sec_filings_attributes\nFROM @Public_Data_sec_filings/cybersyn_sec_report_attributes.json.gz\n    file_format = (type = json strip_outer_array = true);\n```\n\nVerify that each file has a status of `LOADED`:\n![query result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_4_1_new.png)\n\nNow, let's take a look at the data that was loaded:\n```SQL\nSELECT * FROM sec_filings_index LIMIT 10;\nSELECT * FROM sec_filings_attributes LIMIT 10;\n```\n\nClick any of the rows to display the formatted JSON in the right panel:\n\n![JSON data snippet](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_5_1.png)\n\nTo close the display in the panel and display the query details again, click the **X** (Close) button that appears when you hover your mouse in the right corner of the panel.\n\n### Create a View and Query Semi-Structured Data\n\nNext, let's look at how Snowflake allows us to create a view and also query the JSON data directly using SQL.\n\n\u003E \n\u003E \n\u003E  **Views & Materialized Views**\nA view allows the result of a query to be accessed as if it were a table. Views can help present data to end users in a cleaner manner, limit what end users can view in a source table, and write more modular SQL.\n\nSnowflake also supports materialized views in which the query results are stored as though the results are a table. This allows faster access, but requires storage space. Materialized views can be created and queried if you are using Snowflake Enterprise Edition (or higher).\n\nRun the following command to create a columnar view of the semi-structured JSON SEC filing data, so it is easier for analysts to understand and query. The CIK corresponds to the Central Index Key, or unique identifier that SEC gives to each filing entity. The ADSH is the document number for any filing submitted to the SEC.\n\n```SQL\nCREATE OR REPLACE VIEW sec_filings_index_view AS\nSELECT\n    v:CIK::string                   AS cik,\n    v:COMPANY_NAME::string          AS company_name,\n    v:EIN::int                      AS ein,\n    v:ADSH::string                  AS adsh,\n    v:TIMESTAMP_ACCEPTED::timestamp AS timestamp_accepted,\n    v:FILED_DATE::date              AS filed_date,\n    v:FORM_TYPE::string             AS form_type,\n    v:FISCAL_PERIOD::string         AS fiscal_period,\n    v:FISCAL_YEAR::string           AS fiscal_year\nFROM sec_filings_index;\n\nCREATE OR REPLACE VIEW sec_filings_attributes_view AS\nSELECT\n    v:VARIABLE::string            AS variable,\n    v:CIK::string                 AS cik,\n    v:ADSH::string                AS adsh,\n    v:MEASURE_DESCRIPTION::string AS measure_description,\n    v:TAG::string                 AS tag,\n    v:TAG_VERSION::string         AS tag_version,\n    v:UNIT_OF_MEASURE::string     AS unit_of_measure,\n    v:VALUE::string               AS value,\n    v:REPORT::int                 AS report,\n    v:STATEMENT::string           AS statement,\n    v:PERIOD_START_DATE::date     AS period_start_date,\n    v:PERIOD_END_DATE::date       AS period_end_date,\n    v:COVERED_QTRS::int           AS covered_qtrs,\n    TRY_PARSE_JSON(v:METADATA)    AS metadata\nFROM sec_filings_attributes;\n```\n\nSQL dot notation `v:VARIABLE` is used in this command to pull out values at lower levels within the JSON object hierarchy. This allows us to treat each field as if it were a column in a relational table.\n\nThe new view should appear as `SEC_FILINGS_INDEX_VIEW` under `Public_data` \u003E `PUBLIC` \u003E **Views** in the object browser on the left. You may need to expand or refresh the objects browser in order to see it.\n\n![JSON_WEATHER_DATA _VIEW in dropdown](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_6_1.png)\n\nNotice the results look just like a regular structured data source: \n\n```SQL\nSELECT *\nFROM sec_filings_index_view\nLIMIT 20;\n```\n\u003C!-- ------------------------ --\u003E\n\n## Getting Data from Snowflake Marketplace\n\n\n### Snowflake Data Marketplace\n\nMake sure you're using the `ACCOUNTADMIN` role and, \u003Ca href=\"https://app.snowflake.com/_deeplink/marketplace?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003Enavigate to the Marketplace\u003C/a\u003E (**Data Products** \u003E **Marketplace**):\n\n![data marketplace tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_7.png)\n\n#### Find a listing\n\nType `Snowflake Public Data (Free)` in the search box at the top, scroll through the results, and select [**Snowflake Public Data (Free)**](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/) (provided by Snowflake Public Data).\n\n![health tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Search_result.png)  \n\nIn the **Snowflake Public Data (Free)** page, you can learn more about the contents of the data listing, explore data dictionaries, and see some sample queries. You will also see links to documentation and the dataset's cloud region availability. When you're ready, click the **Get** button to make this information available within your Snowflake account:\n\n![get data fields](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/Snowflake_Public_Data_Free_Listing.png)\n\nReview the information in the dialog and click **Get** again:\n\n![get data fields](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/default_get.png)\n\nYou can now click **Done** or choose to run the sample queries provided by Snowflake Public Data:\n\n![get data fields](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/ready.png)\n\nIf you chose **Query Data**, a new worksheet opens in a new browser tab/window:\n1. Set your context \n2. Select the query you want to run (or place your cursor in the query text).\n3. Click the **Run/Play** button (or use the keyboard shortcut).\n4. You can view the data results in the bottom pane.\n5. When you are done running the sample queries, click the **Home** icon in the upper left corner.\n\nNext:\n1. Click **Data** \u003E **Databases**.\n2. Click the `SNOWFLAKE_PUBLIC_DATA_FREE` database.\n3. You can see details about the schemas, tables, and views that are available to query.\n\n![covid19 databases](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/database_details.png)\n\nThat's it! You have now successfully subscribed to the Snowflake Public Data (Free) from Snowflake Public Data, which is updated regularly with global financial data. Notice we didn't have to create databases, tables, views, or an ETL process. We simply searched for and accessed shared data from the Snowflake Data Marketplace.\n\n\u003E \n\u003E \n\u003E To learn more about how to use the new worksheet interface, go to the [Snowsight Docs](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#using-snowsight).\n\n\u003C!-- ------------------------ --\u003E\n\n## Querying, the Results Cache, & Cloning\n\n\nIn the previous exercises, we loaded data into two tables using Snowflake's `COPY` bulk loader command and the `COMPUTE_WH` virtual warehouse. Now we are going to take on the role of the analytics users at our company who need to query data in those tables using the worksheet and the second warehouse `ANALYTICS_WH`.\n\n\u003E \n\u003E \n\u003E  **Real World Roles and Querying**\nWithin a real company, analytics users would likely have a different role than `SYSADMIN`. To keep the lab simple, we are going to stay with the `SYSADMIN` role for this section. Additionally, querying would typically be done with a business intelligence product like Tableau, Looker, PowerBI, etc. For more advanced analytics, data science tools like Datarobot, Dataiku, AWS Sagemaker or many others can query Snowflake. Any technology that leverages JDBC/ODBC, Spark, Python, or any of the other supported programmatic interfaces can run analytics on the data in Snowflake. To keep this lab simple, all queries are being executed via the Snowflake worksheet.\n\n### Execute Some Queries\n\nGo to the **ZERO_TO_SNOWFLAKE_WITH_Public_Data** worksheet and change the warehouse to use the new warehouse you created in the last section. Your worksheet context should be the following:\n\n**Role:** `SYSADMIN`\n**Warehouse:** `ANALYTICS_WH (L)`\n**Database:** `Public_Data`\n**Schema:** `PUBLIC`\n\n![sample data query results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_1.png)\n\nRun the following query to see a sample of the `company_metadata` data:\n\n```SQL\nSELECT * FROM company_metadata;\n```\n\n![sample data query results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_2_new.png)\n\nNow, let's look at the performance of these companies in the stock market. Run the queries below in the worksheet. \n\n**Closing Price Statistics:** First, calculate the daily return of a stock (the percent change in the stock price from the close of the previous day to the close of the current day) and 5-day moving average from closing prices (which helps smooth out daily price fluctuations to identify trends).\n\n```SQL\nSELECT\n    meta.primary_ticker,\n    meta.company_name,\n    ts.date,\n    ts.value AS post_market_close,\n    (ts.value / LAG(ts.value, 1) OVER (PARTITION BY meta.primary_ticker ORDER BY ts.date))::DOUBLE AS daily_return,\n    AVG(ts.value) OVER (PARTITION BY meta.primary_ticker ORDER BY ts.date ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) AS five_day_moving_avg_price\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES ts\nINNER JOIN company_metadata meta\nON ts.ticker = meta.primary_ticker\nWHERE ts.variable_name = 'Post-Market Close';\n```\n\n![post-market close query results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_3.png)\n\n\u003E \n\u003E \n\u003E  If you have defined a particular database in the worksheet and want to use a table from a different database, you must fully qualify the reference to the other table by providing its database and schema name.\n\n**Trading Volume Statistics:** Then, calculate the trading volume change from one day to the next to see if there's an increase or decrease in trading activity. This can be a sign of increasing or decreasing interest in a stock.\n\n```SQL\nSELECT\n    meta.primary_ticker,\n    meta.company_name,\n    ts.date,\n    ts.value AS nasdaq_volume,\n    (ts.value / LAG(ts.value, 1) OVER (PARTITION BY meta.primary_ticker ORDER BY ts.date))::DOUBLE AS volume_change\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES ts\nINNER JOIN company_metadata meta\nON ts.ticker = meta.primary_ticker\nWHERE ts.variable_name = 'Nasdaq Volume';\n```\n\n![volume query results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_3b.png)\n\n### Use the Result Cache\n\nSnowflake has a result cache that holds the results of every query executed in the past 24 hours. These are available across warehouses, so query results returned to one user are available to any other user on the system who executes the same query, provided the underlying data has not changed. Not only do these repeated queries return extremely fast, but they also use no compute credits.\n\nLet's see the result cache in action by running the exact same query again.\n\n```SQL\nSELECT\n    meta.primary_ticker,\n    meta.company_name,\n    ts.date,\n    ts.value AS post_market_close,\n    (ts.value / LAG(ts.value, 1) OVER (PARTITION BY primary_ticker ORDER BY ts.date))::DOUBLE AS daily_return,\n    AVG(ts.value) OVER (PARTITION BY primary_ticker ORDER BY date ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) AS five_day_moving_avg_price\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES ts\nINNER JOIN company_metadata meta\nON ts.ticker = meta.primary_ticker\nWHERE variable_name = 'Post-Market Close';\n```\n\nIn the **Query Details** pane on the right, note that the query runs significantly faster because the results have been cached.\n![cached query duration](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_4.png)\n\n### Clone a Table\n\nSnowflake allows you to create clones, also known as \"zero-copy clones\" of tables, schemas, and databases in seconds. When a clone is created, Snowflake takes a snapshot of data present in the source object and makes it available to the cloned object. The cloned object is writable and independent of the clone source. Therefore, changes made to either the source object or the clone object are not included in the other.\n\n_A popular use case for zero-copy cloning is to clone a production environment for use by Development & Testing teams to test and experiment without adversely impacting the production environment and eliminating the need to set up and manage two separate environments._\n\n\u003E \n\u003E \n\u003E  **Zero-Copy Cloning**\nA massive benefit of zero-copy cloning is that the underlying data is not copied. Only the metadata and pointers to the underlying data change. Hence, clones are “zero-copy\" and storage requirements are not doubled when the data is cloned. Most data warehouses cannot do this, but for Snowflake it is easy!\n\nRun the following command in the worksheet to create a development (dev) table clone of the `company_metadata` table:\n\n```SQL\nCREATE TABLE company_metadata_dev CLONE company_metadata;\n```\n\nClick the three dots (**...**) in the left pane and select **Refresh**. Expand the object tree under the `Public_Data` database and verify that you see a new table named `company_metadata_dev`. Your Development team now can do whatever they want with this table, including updating or deleting it, without impacting the `company_metadata` table or any other object.\n\n![trips_dev table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/6Query_6_new.png)\n\n### Joining Tables\n\nWe will now join the JSON SEC filing datasets together to investigate the revenue of one CPG company, Kraft Heinz. Run the query below to join `SEC_FILINGS_INDEX` to `SEC_FILINGS_ATTRIBUTES` to see how Kraft Heinz (KHC) business segments have performed over time:\n\n```SQL\nWITH data_prep AS (\n    SELECT \n        idx.cik,\n        idx.company_name,\n        idx.adsh,\n        idx.form_type,\n        att.measure_description,\n        CAST(att.value AS DOUBLE) AS value,\n        att.period_start_date,\n        att.period_end_date,\n        att.covered_qtrs,\n        TRIM(att.metadata:\"ProductOrService\"::STRING) AS product\n    FROM sec_filings_attributes_view att\n    JOIN sec_filings_index_view idx\n        ON idx.cik = att.cik AND idx.adsh = att.adsh\n    WHERE idx.cik = '0001637459'\n        AND idx.form_type IN ('10-K', '10-Q')\n        AND LOWER(att.measure_description) = 'net sales'\n        AND (att.metadata IS NULL OR OBJECT_KEYS(att.metadata) = ARRAY_CONSTRUCT('ProductOrService'))\n        AND att.covered_qtrs IN (1, 4)\n        AND value \u003E 0\n    QUALIFY ROW_NUMBER() OVER (\n        PARTITION BY idx.cik, idx.company_name, att.measure_description, att.period_start_date, att.period_end_date, att.covered_qtrs, product\n        ORDER BY idx.filed_date DESC\n    ) = 1\n)\n\nSELECT\n    company_name,\n    measure_description,\n    product,\n    period_end_date,\n    CASE\n        WHEN covered_qtrs = 1 THEN value\n        WHEN covered_qtrs = 4 THEN value - SUM(value) OVER (\n            PARTITION BY cik, measure_description, product, YEAR(period_end_date)\n            ORDER BY period_end_date\n            ROWS BETWEEN 4 PRECEDING AND 1 PRECEDING\n        )\n    END AS quarterly_value\nFROM data_prep\nORDER BY product, period_end_date;\n```\n![weather results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/7SemiStruct_8_1.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Using Time Travel\n\n\nSnowflake's powerful Time Travel feature enables accessing historical data, as well as the objects storing the data, at any point within a period of time. The default window is 24 hours and, if you are using Snowflake Enterprise Edition, can be increased up to 90 days. Most data warehouses cannot offer this functionality, but - you guessed it - Snowflake makes it easy!\n\nSome useful applications include:\n\n- Restoring data-related objects such as tables, schemas, and databases that may have been deleted.\n- Duplicating and backing up data from key points in the past.\n- Analyzing data usage and manipulation over specified periods of time.\n\n### Drop and Undrop a Table\n\nFirst let's see how we can restore data objects that have been accidentally or intentionally deleted.\n\nIn the `ZERO_TO_SNOWFLAKE_WITH_Public_Data` worksheet, run the following DROP command to remove the `SEC_FILINGS_INDEX` table:\n\n```SQL\nDROP TABLE sec_filings_index;\n\n-- Run a query on the table:\nSELECT * FROM sec_filings_index LIMIT 10;\n```\n\nIn the results pane at the bottom, you should see an error because the underlying table has been dropped:\n![table dropped error](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_1.png)\n\nNow, restore the table:\n```SQL\nUNDROP TABLE sec_filings_index;\n```\n\nThe SEC filing index table should be restored. Verify by running the following query:\n\n```SQL \nSELECT * FROM sec_filings_index LIMIT 10;\n```\n\n![restored table result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_2.png)\n\n### Roll Back a Table\n\nLet's roll back the `COMPANY_METADATA` table in the `Public_Data` database to a previous state to fix an unintentional DML error that replaces all the company names in the table with the word \"oops\".\n\nFirst, run the following SQL statements to switch your worksheet to the proper context:\n\n```SQL\nUSE ROLE sysadmin;\n\nUSE WAREHOUSE compute_wh;\n\nUSE DATABASE Public_Data;\n\nUSE SCHEMA public;\n```\n\nRun the following command to replace all of the company names in the table with the word \"oops\":\n\n```SQL\nUPDATE company_metadata SET company_name = 'oops';\n```\n\nNow, run a query that returns the companies. Notice that the company names are all the same:\n\n```SQL\nSELECT *\nFROM company_metadata;\n```\n\n![one row result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_3.png)\n\nNormally we would need to scramble and hope we have a backup lying around. In Snowflake, we can simply run a command to find the query ID of the last `UPDATE` command and store it in a variable named `$QUERY_ID`.\n\nUse Time Travel to recreate the table with the correct company names and verify the company names have been restored:\n```SQL\n-- Set the session variable for the query_id\nSET query_id = (\n  SELECT query_id\n  FROM TABLE(information_schema.query_history_by_session(result_limit=\u003E5))\n  WHERE query_text LIKE 'UPDATE%'\n  ORDER BY start_time DESC\n  LIMIT 1\n);\n\n-- Use the session variable with the identifier syntax (e.g., $query_id)\nCREATE OR REPLACE TABLE company_metadata AS\nSELECT *\nFROM company_metadata\nBEFORE (STATEMENT =\u003E $query_id);\n\n-- Verify the company names have been restored\nSELECT *\nFROM company_metadata;\n```\n\n![restored names result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/8Time_4.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Working with Roles, Account Admin, & Account Usage\n\n\nIn this section, we will explore aspects of Snowflake's access control security model, such as creating a role and granting it specific permissions. We will also explore other usage of the `ACCOUNTADMIN` (Account Administrator) role.\n\nContinuing with the lab story, let's assume a junior DBA has joined our internal analytics team, and we want to create a new role for them with less privileges than the system-defined, default role of `SYSADMIN`.\n\n\u003E \n\u003E \n\u003E  **Role-Based Access Control**\nSnowflake offers very powerful and granular access control that dictates the objects and functionality a user can access, as well as the level of access they have. For more details, check out the [Snowflake documentation](https://docs.snowflake.com/user-guide/security-access-control-overview).\n\n### Create a New Role and Add a User\n\nIn the `ZERO_TO_SNOWFLAKE_WITH_PUBLIC_DATA` worksheet, switch to the `ACCOUNTADMIN` role to create a new role. `ACCOUNTADMIN` encapsulates the `SYSADMIN` and `SECURITYADMIN` system-defined roles. It is the top-level role in the account and should be granted only to a limited number of users.\n\n```SQL\nUSE ROLE accountadmin;\n```\n\nNotice that, in the top right of the worksheet, the context has changed to `ACCOUNTADMIN`:\n\n![ACCOUNTADMIN context](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_1.png)\n\nBefore a role can be used for access control, at least one user must be assigned to it. So let's create a new role named `JUNIOR_DBA` and assign it to your Snowflake user. To complete this task, you need to know your username, which is the name you used to log in to the UI.\n\nUse the following commands to create the role and assign it to you. Before you run the GRANT ROLE command, replace `YOUR_USERNAME_GOES_HERE` with your username:\n\n```SQL\nCREATE ROLE junior_dba;\n\nGRANT ROLE junior_dba TO USER YOUR_USERNAME_GOES_HERE;\n```\n\n\u003E \n\u003E \n\u003E  If you try to perform this operation while in a role such as `SYSADMIN`, it would fail due to insufficient privileges. By default (and design), the `SYSADMIN` role cannot create new roles or users.\n\nChange your worksheet context to the new `JUNIOR_DBA` role:\n\n```SQL\nUSE ROLE junior_dba;\n```\n\nIn the top right of the worksheet, notice that the context has changed to reflect the `JUNIOR_DBA` role. \n\n![JUNIOR_DBA context](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_2.png)\n\nAlso, the warehouse is not selected because the newly created role does not have usage privileges on any warehouse. Let's fix it by switching back to `ACCOUNTADMIN` role and grant usage privileges to `COMPUTE_WH` warehouse.\n\n```SQL\nUSE ROLE accountadmin;\n\nGRANT USAGE ON WAREHOUSE compute_wh TO ROLE junior_dba;\n```\n\nSwitch back to the `JUNIOR_DBA` role. You should be able to use `COMPUTE_WH` now.\n```SQL\nUSE ROLE junior_dba;\n\nUSE WAREHOUSE compute_wh;\n```\n\nFinally, you can notice that in the database object browser panel on the left, the `Public_Data` and `SNOWFLAKE_PUBLIC_DATA_FREE` databases no longer appear. This is because the `JUNIOR_DBA` role does not have privileges to access them.\n\nSwitch back to the `ACCOUNTADMIN` role and grant the `JUNIOR_DBA` the USAGE privilege required to view and use the `Public_data` and `SNOWFLAKE_PUBLIC_DATA_FREE` databases. Note that the Snowflake Public Data database from the Marketplace uses `GRANT IMPORTED PRIVILEGES`, instead of `GRANT USAGE`.\n\n```SQL\nUSE ROLE accountadmin;\n\nGRANT USAGE ON DATABASE Public_Data TO ROLE junior_dba;\n\nGRANT IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE_PUBLIC_DATA_FREE TO ROLE junior_dba;\n\nUSE ROLE junior_dba;\n```\n\nNotice that the `Public_Data` and `SNOWFLAKE_PUBLIC_DATA_FREE` databases now appear in the database object browser panel on the left. If they don't appear, try clicking **...** in the panel, then clicking **Refresh**.\n\n![object browser panel with databases](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_3.png)\n\n### View the Account Administrator UI\n\nLet's change our access control role back to `ACCOUNTADMIN` to see other areas of the UI accessible only to this role. However, to perform this task, use the UI instead of the worksheet.\n\nFirst, click the **Home** icon in the top left corner of the worksheet. Then, in the bottom left corner of the UI, click your name to display the user preferences menu. In the menu, go to **Switch Role** and select `ACCOUNTADMIN`.\n\n![switch UI role](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_4.png)\n\n\u003E \n\u003E \n\u003E  **Roles in User Preference vs Worksheet**\nWhy did we use the user preference menu to change the role instead of the worksheet? The UI session and each worksheet have their own separate roles. The UI session role controls the elements you can see and access in the UI, whereas the worksheet role controls only the objects and actions you can access within the role.\n\nNotice that once you switch the UI session to the `ACCOUNTADMIN` role, new tabs are available under **Admin**.\n\n#### Admin \u003E Cost Management\n\n![account usage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_5.png)\n\nThe \u003Ca href=\"https://app.snowflake.com/_deeplink/#/account/usage?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_content=getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003E**Cost Management** tab\u003C/a\u003E shows your usage of Snowflake credits, with filters by account and consumption types:\n- **Organization**: Credit usage across all the accounts in your organization.\n- **Compute**: Credits consumed by the virtual warehouses in the current account.\n- **Storage**: Average amount of data stored in all databases, internal stages, and Snowflake Failsafe in the current account for the past month.\n- **Data Transfer**: Average amount of data transferred out of the region (for the current account) into other regions for the past month.\n\n#### Admin \u003E Security\n\n![account usage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/9Role_6.png)\n\nThe **Security** tab contains network policies created for the Snowflake account. New network policies can be created by selecting “+ Network Policy” at the top right hand side of the page.\n\n#### Admin \u003E Billing & Terms\n\nThe **Billing & Terms** tab contains the payment method for the account:\n- If you are a Snowflake contract customer, the tab shows the name associated with your contract information.\n- If you are an on-demand Snowflake customer, the tab shows the credit card used to pay month-to-month, if one has been entered. If no credit card is on file, you can add one to continue using Snowflake when your trial ends.\n\nFor the next section, stay in the `ACCOUNTADMIN` role for the UI session.\n\n\u003C!-- ------------------------ --\u003E\n\n## Sharing Data Securely via Snowflake Marketplace\n\n\nSnowflake enables data access between accounts through the secure data sharing features. Shares are created by data providers and imported by data consumers, either through their own Snowflake account or a provisioned Snowflake Reader account. The consumer can be an external entity or a different internal business unit that is required to have its own unique Snowflake account.\n\nWith secure data sharing:\n\n- There is only one copy of the data that lives in the data provider's account.\n- Shared data is always live, real-time, and immediately available to consumers.\n- Providers can establish revocable, fine-grained access to shares.\n- Data sharing is simple and safe, especially compared to older data sharing methods, which were often manual and insecure, such as transferring large `.csv` files across the internet.\n\n\u003E \n\u003E \n\u003E  **Cross-region & cross-cloud data sharing** To share data across regions or cloud platforms, you must set up replication. This is outside the scope of this lab, but more information is available in [this Snowflake article](/trending/what-is-data-replication).\n\nSnowflake uses secure data sharing to provide account usage data and sample data sets to all Snowflake accounts. In this capacity, Snowflake acts as the data provider of the data and all other accounts.\n\nSecure data sharing also powers the Snowflake Data Marketplace, which is available to all Snowflake customers and allows you to discover and access third-party datasets from numerous data providers and SaaS vendors. Again, in this data sharing model, the data doesn't leave the provider's account and you can use the datasets without any transformation.\n\n### View Existing Shares\n\nIn the home page, navigate to **Data** \u003E **Databases**. In the \u003Ca href=\"https://app.snowflake.com/_deeplink/#/data/databases?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_campaign=-us-en-all&utm_content=app-getting-started-with-snowflake&utm_cta=developer-guides-deeplink\" class=\"_deeplink\"\u003Elist of databases\u003C/a\u003E, look at the **SOURCE** column. You should see one database with `Local` in the column, which we created previously in the lab. The other database, `SNOWFLAKE`, shows `Share` in the column, indicating it's shared from a provider.\n\n![arrow over database icon](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_1.png)\n\n### Create an Outbound Share\n\nAssume we are the Account Administrator for our Snowflake account. We have a trusted partner who wants to analyze our data on a near real-time basis. This partner also has their own Snowflake account in the same region as our account. We can use secure data sharing to allow them to access this information.\n\nNavigate to **Data Products** \u003E **Private Sharing**, then at the top of the tab click **Shared By Your Account**. Click the **Share** button in the top right corner and select **Create a Direct Share**:\n\n![shares outbound button](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_2.png)\n\nClick **+ Select Data** and navigate to the database and schema you want to share. Select the tables and click the **Done** button:\n\n![share fields](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_3.png)\n\nThe default name of the share is a generic name with a random numeric value appended. Edit the default name to a more descriptive value that will help identify the share in the future (e.g. `ZERO_TO_SNOWFLAKE_SHARED_DATA`). You can also add a comment.\n\nIn a real-world scenario, the Account Administrator would next add one or more consumer accounts to the share, but we'll stop here for the purposes of this lab.\n\nClick the **Create Share** button at the bottom of the dialog. The dialog closes and the page shows the secure share you created.\n\n![TRIPS_SHARE share](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake/10Share_5.png)\n\nYou can add consumers, add/change the description, and edit the objects in the share at any time. In the page, click the **\u003C** button next to the share name to return to the **Share with Other Accounts** page.\n\nWe've demonstrated how it only takes seconds to give other accounts access to data in your Snowflake account in a secure manner with no copying or transferring of data required!\n\nSnowflake provides several ways to securely share data without compromising confidentiality. In addition to tables, you can share secure views, secure UDFs (user-defined functions), and other secure objects. For more details about using these methods to share data while preventing access to sensitive information, see the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/data-sharing-secure-views.html).\n\n\u003C!-- ------------------------ --\u003E\n\n## Resetting Your Snowflake Environment\n\n\nIf you would like to reset your environment by deleting all the objects created as part of this lab, ensure you are using the `ACCOUNTADMIN` role in the worksheet and run the following commands to drop the objects we created in the lab:\n\n```SQL\nUSE ROLE accountadmin;\n\nDROP DATABASE IF EXISTS Public_Data;\n\nDROP WAREHOUSE IF EXISTS analytics_wh;\n\nDROP ROLE IF EXISTS junior_dba;\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusion & Next Steps\n\n\nCongratulations on completing this introductory lab exercise! You've mastered the Snowflake basics and are ready to apply these fundamentals to your own data. Be sure to reference this guide if you ever need a refresher.\n\nWe encourage you to continue with your free trial by loading your own sample or production data and by using some of the more advanced capabilities of Snowflake not covered in this lab.\n\n### Additional Resources:\n\n- Learn more about the [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#using-snowsight) docs.\n- Read the [Definitive Guide to Maximizing Your Free Trial](/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/) document.\n- Attend a [Snowflake virtual or in-person event](/about/events/) to learn more about our capabilities and customers.\n- Join the [Snowflake Community](https://community.snowflake.com/s/topic/0TO0Z000000wmFQWAY/getting-started-with-snowflake).\n- Sign up for [Snowflake University](https://community.snowflake.com/s/article/Getting-Access-to-Snowflake-University).\n- Contact our [Sales Team](/free-trial-contact-sales/) to learn more.\n- Access Snowflake Public Data's analytics-ready data on [Snowflake Marketplace](https://app.snowflake.com/marketplace/providers/GZTSZAS2KCS/Snowflake%20Public%20Data%20Products).\n\n### What we've covered:\n\n- How to create stages, databases, tables, views, and virtual warehouses.\n- How to load structured and semi-structured data.\n- How to consume Snowflake Public data from the [Snowflake Data Marketplace](https://app.snowflake.com/marketplace/listing/GZTSZAS2KF7/).\n- How to perform analytical queries on data in Snowflake, including joins between tables.\n- How to clone objects.\n- How to undo user errors using Time Travel.\n- How to create roles and users, and grant them privileges.\n- How to securely and easily share data with other accounts.\n\n- [Fork repo on GitHub](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Create%20and%20Manage%20Snowflake%20Objects%20like%20a%20Pro/Create%20and%20Manage%20Snowflake%20Objects%20like%20a%20Pro.ipynb)\n- [Download Reference Architecture](/content/dam/snowflake-site/developers/2024/05/Citibike-Data-Analysis_-Create-and-manage-Snowflake-Objects-like-a-pro.pdf)\n- [Watch the Demo](https://youtu.be/Dj8aAoEOfrw?list=TLGGDV2-mOUmeyYyMDA5MjAyNQ)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-fac5f21baa","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-63f729c191",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-177cbbb0c0","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-2b81a8e226","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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Your Lab Environment\u003C/h2\u003E","\u003Ch2\u003EThe Snowflake ​User Interface\u003C/h2\u003E","\u003Ch2\u003EData Lab: Stock Price & SEC Filings Data\u003C/h2\u003E","\u003Ch2\u003ELoading Structured Data into Snowflake: CSVs\u003C/h2\u003E","\u003Ch2\u003ELoading Semi-Structured Data into Snowflake: JSONs\u003C/h2\u003E","\u003Ch2\u003EGetting Data from Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003EQuerying, the Results Cache, & Cloning\u003C/h2\u003E","\u003Ch2\u003EUsing Time Travel\u003C/h2\u003E","\u003Ch2\u003EWorking with Roles, Account Admin, & Account Usage\u003C/h2\u003E","\u003Ch2\u003ESharing Data Securely via Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003EResetting Your Snowflake Environment\u003C/h2\u003E","\u003Ch2\u003EConclusion & Next 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