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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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By completing this guide, you will be able to build a data pipeline to process data from different sources, and periodically run the pipeline to update your data tables in Snowflake.\u003C/p\u003E\n","\u003Cp\u003EHere is a summary of what you will be able to learn in each step by following this quickstart:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESetup Environment\u003C/strong\u003E: Use stages and tables to ingest and organize raw data from S3 into Snowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Marketplace\u003C/strong\u003E: Download the data you need from Snowflake Marketplace and use it for your analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Engineering\u003C/strong\u003E: Leverage Snowpark for Python DataFrames to perform data transformations such as group by, aggregate, and join to prep for the data for downstream applications\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrchestrating Pipelines\u003C/strong\u003E: Use Snowflake Python Tasks API to turn your data pipeline code into operational pipelines with integrated monitoring\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn case you are new to some of the technologies mentioned above, here is a quick summary with the links to documentation.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowpark?\u003C/h3\u003E\n","\u003Cp\u003ESnowpark is the set of libraries and code execution environments that run Python and other programming languages next to your data in Snowflake. Snowpark can be used to build data pipelines, ML models, apps, and other data processing tasks.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClient Side Libraries\u003C/strong\u003E - Snowpark libraries can be installed and downloaded from any client-side notebook or IDE and are used for code development and deployment. Libraries include the Snowpark API for data pipelines and apps and the Snowpark ML API for end to end machine learning.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECode Execution Environments\u003C/strong\u003E - Snowpark provides elastic compute environments for secure execution of your code in Snowflake. These server-side capabilities allow users to bring in and run custom logic in Python with user-defined functions and stored procedures.\u003C/p\u003E\n","\u003Cp\u003ELearn more about \u003Ca href=\"/snowpark/\"\u003ESnowpark\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/snowpark.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWorking with Snowflake Marketplace\u003C/h3\u003E\n","\u003Cp\u003ESnowflake Marketplace provides visibility to a wide variety of datasets from third-party data stewards which broaden access to data points used to transform business processes. Snowflake Marketplace also removes the need to integrate and model data by providing secure access to data sets fully maintained by the data provider.\u003C/p\u003E\n","\u003Ch3\u003EWhat you will learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to ingest data from an external stage such as an S3 bucket into a Snowflake table\u003C/li\u003E\u003Cli\u003EHow to access data from Snowflake Marketplace and use it for your analysis\u003C/li\u003E\u003Cli\u003EHow to analyze data and perform data engineering tasks using Snowpark DataFrame API, Python Stored Procedures and more\u003C/li\u003E\u003Cli\u003EHow to use open-source Python libraries from curated Snowflake Anaconda channel\u003C/li\u003E\u003Cli\u003EHow to create Snowflake Tasks and use the Python Tasks API to schedule data pipelines\u003C/li\u003E\u003Cli\u003EHow to use VS Code extension for Snowflake to perform standard snowflake operations from VS Code and Snowsigt UI\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png\" alt=\"assets/data_pipeline_overview.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Python\u003C/li\u003E\u003Cli\u003EFamiliarity with the DataFrame API\u003C/li\u003E\u003Cli\u003EFamiliarity with Snowflake\u003C/li\u003E\u003Cli\u003EFamiliarity with Git repositories and GitHub\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n","\u003Cp\u003EYou will need the following things before beginning:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EA Snowflake Account\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA Snowflake user created with ACCOUNTADMIN permissions\u003C/strong\u003E. This user will be used to get things setup in Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnaconda Terms & Conditions accepted\u003C/strong\u003E. See Getting Started section in \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#getting-started\"\u003EThird-Party Packages\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EGitHub account\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EA GitHub account\u003C/strong\u003E. If you don't already have a GitHub account you can create one for free. Visit the \u003Ca href=\"https://github.com/signup\"\u003EJoin GitHub\u003C/a\u003E page to get started.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuickstart Setup\u003C/h2\u003E\n","\u003Ch3\u003EFork the GitHub Repository\u003C/h3\u003E\n","\u003Cp\u003EThe very first step is to fork the GitHub repository \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-data-engineering-with-snowpark-python-intro\"\u003EIntro to Data Engineering with Snowpark Python associated GitHub Repository\u003C/a\u003E. This repository contains all the code you need to successfully complete this Quickstart guide.  Click on the "Fork" button near the top right. Complete any required fields and click "Create Fork".\u003C/p\u003E\n","\u003Ch3\u003ECreate GitHub Codespace\u003C/h3\u003E\n","\u003Cp\u003EFor this Quickstart we will be using \u003Ca href=\"https://docs.github.com/en/codespaces/overview\"\u003EGitHub Codespaces\u003C/a\u003E for our development environment. Codespaces offer a hosted development environment with a hosted, web-based VS Code environment. GitHub currently offers \u003Ca href=\"https://github.com/features/codespaces\"\u003E60 hours for free each month\u003C/a\u003E when using a 2 node environment, which should be enough to work through this lab.\u003C/p\u003E\n","\u003Cp\u003ETo create a GitHub Codespace, click on the green \u003Ccode\u003E<> Code\u003C/code\u003E button from the GitHub repository homepage. In the Code popup, click on the \u003Ccode\u003ECodespaces\u003C/code\u003E tab and then on the green \u003Ccode\u003ECreate codespace on main\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/codespace_setup.png\" alt=\"assets/codespace_setup.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will open a new tab and begin setting up your codespace. It will take a few minutes as it sets up the entire environment for this Quickstart. Here is what is being done for you:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreating a container for your environment\u003C/li\u003E\u003Cli\u003EInstalling Anaconda (miniconda)\u003C/li\u003E\u003Cli\u003ESnowSQL setup\n\u003Cul\u003E\u003Cli\u003EInstalling SnowSQL\u003C/li\u003E\u003Cli\u003ECreating a directory and default config file for SnowSQL\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EAnaconda setup\n\u003Cul\u003E\u003Cli\u003ECreating the Anaconda environment\u003C/li\u003E\u003Cli\u003EInstalling the Snowpark Python library\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EVS Code setup\n\u003Cul\u003E\u003Cli\u003EInstalling VS Code\u003C/li\u003E\u003Cli\u003EInstalling the Snowflake VS Code extension\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EStarting a hosted, web-based VS Code editor\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOnce the codepsace has been created and started you should see a hosted web-based version of VS Code with your forked repository set up! Just a couple more things and we're ready to start.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE\u003C/strong\u003E:\nGoing forward references to \u003Ccode\u003EVS Code\u003C/code\u003E means the VS Code editor on the Codespaces\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EConfigure Snowflake Credentials\u003C/h3\u003E\n","\u003Cp\u003EWe will not be directly using \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql.html\"\u003Ethe SnowSQL command line client\u003C/a\u003E for this Quickstart, but we will be storing our Snowflake connection details in the SnowSQL config file located at \u003Ccode\u003E~/.snowsql/config\u003C/code\u003E. A default config file was created for you during the codespace setup.\u003C/p\u003E\n","\u003Cp\u003EThe easiest way to edit the default \u003Ccode\u003E~/.snowsql/config\u003C/code\u003E file is directly from VS Code in your codespace. Type \u003Ccode\u003ECommand-P\u003C/code\u003E, type (or paste) \u003Ccode\u003E~/.snowsql/config\u003C/code\u003E and hit return. The SnowSQL config file should now be open. You just need to edit the file and replace the \u003Ccode\u003Eaccountname\u003C/code\u003E, \u003Ccode\u003Eusername\u003C/code\u003E, and \u003Ccode\u003Epassword\u003C/code\u003E with your values. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier#finding-the-organization-and-account-name-for-an-account\"\u003Ehere\u003C/a\u003E on how to find the  account identifier, and make sure to replace the dot between the organization name and the account name with a hyphen. Then save and close the file.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The SnowCLI tool (and by extension this Quickstart) currently does not work with Key Pair authentication. It simply grabs your username and password details from the shared SnowSQL config file.\u003C/p\u003E\n","\u003Ch3\u003EVerify Your Anaconda Environment is Activated\u003C/h3\u003E\n","\u003Cp\u003EDuring the codespace setup we created an Anaconda environment named \u003Ccode\u003Esnowflake-demo\u003C/code\u003E. And when VS Code started up it should have automatically activated the environment in your terminal. You should see something like this in the terminal, and in particular you should see \u003Ccode\u003E(snowflake-demo)\u003C/code\u003E before your bash prompt.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Snowflake Objects\u003C/h2\u003E\n","\u003Ch3\u003ESnowflake Extensions for VS Code\u003C/h3\u003E\n","\u003Cp\u003EYou can run SQL queries and Python APIs against Snowflake in many different ways through the Snowsight UI, SnowCLI, etc. But for this Quickstart we'll be using the Snowflake extension for VS Code. For a brief overview of Snowflake's native extension for VS Code, please check out our \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc\"\u003EVS Code Marketplace Snowflake extension page\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ETo put this in context, we are on step \u003Cstrong\u003E#3\u003C/strong\u003E in our data flow overview:\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png\" alt=\"Quickstart Pipeline Overview\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ECreate Roles, Databases, Tables, Schema and Stages\u003C/h3\u003E\n","\u003Cp\u003EYou can log into \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E or VS Code to create all the snowflake objects needed to work through this guide.\u003C/p\u003E\n","\u003Cp\u003EFor the purpose of this quickstart, we will use VS Code to run the SQL commands and create the Snowflake objects. To do that, open the Snowflake extension tab on the left and use the account identifier and other credentials you previously entered in the SnowSQL config to sign in.\u003C/p\u003E\n","\u003Cp\u003EYou can now open the sql file \u003Ccode\u003Esteps/03_setup_snowflake.sql\u003C/code\u003E in VS Code. You can click on the \u003Ccode\u003EExecute\u003C/code\u003E option above every SQL command to run each command separately or click on \u003Ccode\u003EExecute All\u003C/code\u003E to run all the commands sequentially.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/vscode_execute.png\" alt=\"Execute in VS Code\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you use different names for objects created in this section, be sure to update scripts and code in the following sections accordingly.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFor each SQL script block below, select all the statements in the block and execute them top to bottom.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ELet's run through the commands individually and understand what each command does and finally create Snowflake Objects required for the lab.\u003C/p\u003E\n","\u003Ch3\u003ECreating Account Level Objects\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EIn this step, we create the role \u003Ccode\u003EHOL_ROLE\u003C/code\u003E and assign this role to \u003Ccode\u003ECURRENT_USER()\u003C/code\u003E within Snowflake. This role will have access permissions to create all the Snowflake objects needed for the quickstart. First, grant the \u003Ccode\u003EHOL_ROLE\u003C/code\u003E the same permissions as \u003Ccode\u003ESYSADMIN\u003C/code\u003E role. Then, grant permissions to run tasks, monitor the execution of tasks and to import privileges on the database to \u003Ccode\u003EHOL_ROLE\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET MY_USER = CURRENT_USER();\nCREATE OR REPLACE ROLE HOL_ROLE;\nGRANT ROLE HOL_ROLE TO ROLE SYSADMIN;\nGRANT ROLE HOL_ROLE TO USER IDENTIFIER($MY_USER);\n\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE HOL_ROLE;\nGRANT MONITOR EXECUTION ON ACCOUNT TO ROLE HOL_ROLE;\nGRANT IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE TO ROLE HOL_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a new database \u003Ccode\u003EHOL_DB\u003C/code\u003E and assign ownership of the database to \u003Ccode\u003EHOL_ROLE\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE HOL_DB;\nGRANT OWNERSHIP ON DATABASE HOL_DB TO ROLE HOL_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a new warehouse \u003Ccode\u003EHOL_WH\u003C/code\u003E to serve the compute requirements for the quickstart. I have used a XSMALL warehouse, and configured it to auto suspend after 300 seconds (5 mins), and enabled auto resume on it. I have assigned ownership of the warehouse to the \u003Ccode\u003EHOL_ROLE\u003C/code\u003E too.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE WAREHOUSE HOL_WH WAREHOUSE_SIZE = XSMALL, AUTO_SUSPEND = 300, AUTO_RESUME= TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HOL_WH TO ROLE HOL_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EDatabase Level Objects\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003ESet the right Database Context such as role, warehouse and database,\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE HOL_ROLE;\nUSE WAREHOUSE HOL_WH;\nUSE DATABASE HOL_DB;\n\nUSE SCHEMA HOL_SCHEMA;\nCREATE OR REPLACE STAGE FROSTBYTE_RAW_STAGE\n    URL = 's3://sfquickstarts/data-engineering-with-snowpark-python/'\n;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf you use different names for objects created in this section, be sure to update scripts and code in the following sections accordingly.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Weather\u003C/h2\u003E\n","\u003Cp\u003EDuring this step we will be "loading" the raw weather data to Snowflake. But "loading" is really the wrong word here. Because we're using Snowflake's unique data sharing capability we don't actually need to copy the data to our Snowflake account with a custom ETL process. Instead we can directly access the weather data shared by Weather Source in the Snowflake Marketplace.\u003C/p\u003E\n","\u003Cp\u003ETo put this in context, we are on step \u003Cstrong\u003E#4\u003C/strong\u003E in our data flow overview:\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png\" alt=\"Quickstart Pipeline Overview\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESnowflake Marketplace\u003C/h3\u003E\n","\u003Cp\u003ESnowflake Marketplace provides visibility to a wide variety of datasets from third-party data stewards which broaden access to data points used to transform business processes. Snowflake Marketplace also removes the need to integrate and model data by providing secure access to data sets fully maintained by the data provider.\u003C/p\u003E\n","\u003Cp\u003EBut what about data that needs constant updating - like the WEATHER data? We would need to build a pipeline process to constantly update that data to keep it fresh. Perhaps a better way to get this external data would be to source it from a trusted data supplier. Let them manage the data, keeping it accurate and up to date.\u003C/p\u003E\n","\u003Ch3\u003EWeather data from Snowflake Marketplace\u003C/h3\u003E\n","\u003Cp\u003EWeather Source is a leading provider of global weather and climate data and their OnPoint Product Suite provides businesses with the necessary weather and climate data to quickly generate meaningful and actionable insights for a wide range of use cases across industries. Let's connect to the \u003Ccode\u003EWeather Source LLC: frostbyte\u003C/code\u003E feed from Weather Source in the Snowflake Marketplace by following these steps:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELogin to Snowsight\u003C/li\u003E\u003Cli\u003EClick on the \u003Ccode\u003EMarketplace\u003C/code\u003E link in the left navigation bar\u003C/li\u003E\u003Cli\u003EEnter "Weather Source LLC: frostbyte" in the search box and click return\u003C/li\u003E\u003Cli\u003EClick on the "Weather Source LLC: frostbyte" listing tile\u003C/li\u003E\u003Cli\u003EClick the blue "Get" button\n\u003Cul\u003E\u003Cli\u003EExpand the "Options" dialog\u003C/li\u003E\u003Cli\u003EChange the "Database name" to read "FROSTBYTE_WEATHERSOURCE" (all capital letters)\u003C/li\u003E\u003Cli\u003ESelect the "HOL_ROLE" role to have access to the new database\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick on the blue "Get" button\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/snowflake_marketplace.png\" alt=\"Snowflake Marketplace\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003EThat's it... we don't have to do anything from here to keep this data updated. The provider will do that for us and data sharing means we are always seeing whatever they have published. How amazing is that? Just think of all the things you didn't have to do here to get access to an always up-to-date, third-party dataset!\u003C/p\u003E\n","\u003Ch3\u003EQuery the Weather data\u003C/h3\u003E\n","\u003Cp\u003EOpen the \u003Ccode\u003Esteps/04_load_weather.sql\u003C/code\u003E script in VS Code from the file Explorer in the left navigation bar, and select \u003Ccode\u003EExecute All\u003C/code\u003E from the top right corner. Notice how easy it is to query data shared through the Snowflake Marketplace! You access it just like any other table or view in Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM FROSTBYTE_WEATHERSOURCE.ONPOINT_ID.POSTAL_CODES LIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also view the shared database \u003Ccode\u003EFROSTBYTE_WEATHERSOURCE.ONPOINT_ID.POSTAL_CODE\u003C/code\u003E by navigating to the Snowsight UI -> Data -> Databases.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf you used a different name for the database while getting weather data from the marketplace, be sure to update scripts and code in the following sections accordingly.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Location and Order Detail\u003C/h2\u003E\n","\u003Cp\u003EDuring this step we will be "loading" the raw excel files containing location and order details data from an external stage (s3 bucket) to Snowflake using the Dynamic File Access feature. If you would like to peek at the raw excel files, you can check out the \u003Ccode\u003Edata\u003C/code\u003E folder in the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-data-engineering-with-snowpark-python-intro/tree/main/data\"\u003EGit repo\u003C/a\u003E. We use a Python stored procedure to load this data from an S3 bucket into a Snowflake table.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will run through the commands in the SQL file \u003Ccode\u003Esteps/05_load_excel_files.sql\u003C/code\u003E. You can open the sql file \u003Ccode\u003Esteps/05_load_excel_files.sql\u003C/code\u003E in VS Code. You can click on the \u003Ccode\u003EExecute\u003C/code\u003E option above every SQL command to run each command separately or click on \u003Ccode\u003EExecute All\u003C/code\u003E to run all the commands sequentially.\u003C/p\u003E\n","\u003Cp\u003ETo put this in context, we are on step \u003Cstrong\u003E#5\u003C/strong\u003E in our data flow overview:\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png\" alt=\"Quickstart Pipeline Overview\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EDynamic File Access\u003C/h3\u003E\n","\u003Cp\u003EYou can read a file from an internal or external stage using the \u003Ccode\u003ESnowflakeFile\u003C/code\u003E class in the Snowpark \u003Ccode\u003Esnowflake.snowpark.files\u003C/code\u003E module. The \u003Ccode\u003ESnowflakeFile\u003C/code\u003E class provides dynamic file access, which lets you stream files of any size.\u003C/p\u003E\n","\u003Ch3\u003ELoading Excel files from an External Stage\u003C/h3\u003E\n","\u003Cp\u003EIn this quickstart, we will use dynamic file access to load the excel files from an s3 bucket (an external stage we created before \u003Ccode\u003EFROSTBYTE_RAW_STAGE\u003C/code\u003E).\u003C/p\u003E\n","\u003Cp\u003EFirst let's check if the two excel files are present in the S3 bucket by calling \u003Ccode\u003ELIST\u003C/code\u003E on the stage.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ELIST @FROSTBYTE_RAW_STAGE/intro;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreating the Stored Procedure to load Excel files\u003C/h3\u003E\n","\u003Cp\u003EDuring this step we will be creating our first Snowpark Python stored procedure (or SPROC) to Snowflake. This SPROC \u003Ccode\u003ELOAD_EXCEL_WORKSHEET_TO_TABLE_SP\u003C/code\u003E will load the excel data files into snowflake tables for further analysis.\u003C/p\u003E\n","\u003Cp\u003EBelow is the SQL query to create the SPROC:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWe use the python runtime 3.10 for the SPROC\u003C/li\u003E\u003Cli\u003EWe use the python packages \u003Ccode\u003Esnowflake-snowpark-python\u003C/code\u003E, \u003Ccode\u003Epandas\u003C/code\u003E and \u003Ccode\u003Eopenpyxl\u003C/code\u003E to load excel files\u003C/li\u003E\u003Cli\u003EThen import \u003Ccode\u003Esnowflake.snowpark.files.SnowflakeFile\u003C/code\u003E for the dynamic file access to load file from an external stage\u003C/li\u003E\u003Cli\u003EWe use \u003Ccode\u003ESnowflake.open()\u003C/code\u003E to open the excel file, read the contents of the file into a pandas DataFrame, and save the DataFrame as a Snowflake table using \u003Ccode\u003Esave_as_table()\u003C/code\u003E API.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PROCEDURE LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(file_path string, worksheet_name string, target_table string)\nRETURNS VARIANT\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.10'\nPACKAGES = ('snowflake-snowpark-python', 'pandas', 'openpyxl')\nHANDLER = 'main'\nAS\n$$\nfrom snowflake.snowpark.files import SnowflakeFile\nfrom openpyxl import load_workbook\nimport pandas as pd\n \ndef main(session, file_path, worksheet_name, target_table):\n with SnowflakeFile.open(file_path, 'rb') as f:\n     workbook = load_workbook(f)\n     sheet = workbook.get_sheet_by_name(worksheet_name)\n     data = sheet.values\n \n     # Get the first line in file as a header line\n     columns = next(data)[0:]\n     # Create a DataFrame based on the second and subsequent lines of data\n     df = pd.DataFrame(data, columns=columns)\n \n     df2 = session.create_dataframe(df)\n     df2.write.mode("overwrite").save_as_table(target_table)\n \n return True\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERunning the Sproc in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EIn the above step, we only created the Stored procedure. However, we need to invoke \u003Ccode\u003ECALL\u003C/code\u003E on the SPROC to actually load the excel files into a Snowflake target table.\u003C/p\u003E\n","\u003Cp\u003ETo load \u003Ccode\u003EORDER_DETAIL.xlsx\u003C/code\u003E file, you can run the following SQL command in the SQL file \u003Ccode\u003Esteps/05_load_excel_files.sql\u003C/code\u003E from VS Code.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/order_detail.xlsx'), 'order_detail', 'ORDER_DETAIL');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo laod the \u003Ccode\u003ELOCATION.xlsx\u003C/code\u003E file, you can run the following SQL command in the SQL file \u003Ccode\u003Esteps/05_load_excel_files.sql\u003C/code\u003E from VS Code.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/location.xlsx'), 'location', 'LOCATION');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext up, to verify if the excel files are loaded successfully into respective Snowflake tables, you can run the following \u003Ccode\u003EDESC\u003C/code\u003E and \u003Ccode\u003ESELECT\u003C/code\u003E statements from VS Code.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDESCRIBE TABLE ORDER_DETAIL;\nSELECT * FROM ORDER_DETAIL;\n\nDESCRIBE TABLE LOCATION;\nSELECT * FROM LOCATION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFantastic. We now have all the raw data needed to build our data pipeline and further analyze them.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDaily City Metrics Update Sproc\u003C/h2\u003E\n","\u003Cp\u003EDuring this step we will be creating our second Snowpark Python sproc to Snowflake. This sproc will join the \u003Ccode\u003EORDER_DETAIL\u003C/code\u003E table with the \u003Ccode\u003ELOCATION\u003C/code\u003E table and \u003Ccode\u003EHISTORY_DAY\u003C/code\u003E table to create a final, aggregated table for analysis named \u003Ccode\u003EDAILY_CITY_METRICS\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will run through the commands in the SQL file \u003Ccode\u003Esteps/06_load_daily_city_metrics.sql\u003C/code\u003E. You can open the sql file \u003Ccode\u003Esteps/06_load_daily_city_metrics.sql\u003C/code\u003E in VS Code. You can click on the \u003Ccode\u003EExecute\u003C/code\u003E option above every SQL command to run each command separately or click on \u003Ccode\u003EExecute All\u003C/code\u003E to run all the commands sequentially.\u003C/p\u003E\n","\u003Cp\u003ETo put this in context, we are on step \u003Cstrong\u003E#6\u003C/strong\u003E in our data flow overview:\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png\" alt=\"Quickstart Pipeline Overview\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ECreating Sproc to Calculate Daily City Metrics\u003C/h3\u003E\n","\u003Cp\u003EBelow is the SQL query to create the SPROC:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWe use the python runtime 3.10 for the SPROC, and use the python package \u003Ccode\u003Esnowflake-snowpark-python\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFirst part of the Sproc contains \u003Ccode\u003Etable_exists()\u003C/code\u003E function which we use to verify if the raw tables exist in the defined database and schema.\u003C/li\u003E\u003Cli\u003ESecond part of the Sproc contains the  \u003Ccode\u003Emain()\u003C/code\u003E function that reads the raw tables \u003Ccode\u003EORDER_DETAIL\u003C/code\u003E, \u003Ccode\u003ELOCATION\u003C/code\u003E, and \u003Ccode\u003EFROSTBYTE_WEATHERSOURCE.ONPOINT_ID.HISTORY_DAY\u003C/code\u003E. Remember the Frostbyte Weather Source data is from Snowflake Marketplace but we can use it like any other standard snowflake table.\u003C/li\u003E\u003Cli\u003EAfter loading all 3 raw tables into their respective Snowpark Python Dataframes, we prepare to join the Dataframes.\u003C/li\u003E\u003Cli\u003EWe join all three Dataframes into a final DataFrame \u003Ccode\u003Eorder_detail\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ENext step is data aggregation based on date, country and city. So we use group_by() on \u003Ccode\u003EDATE_VALID_STD\u003C/code\u003E, \u003Ccode\u003EISO_COUNTRY_CODE\u003C/code\u003E and \u003Ccode\u003ECITY_NAME\u003C/code\u003E columns.\u003C/li\u003E\u003Cli\u003EAfter grouping by these 3 columns, we calculate the aggregate daily sales, daily average temperature in Farenheit, and daily average precipitation in inches.\u003C/li\u003E\u003Cli\u003EThe resulting DataFrame called \u003Ccode\u003Efinal_agg\u003C/code\u003E is saved. If the table \u003Ccode\u003EDAILY_CITY_METRICS\u003C/code\u003E does not exist, we create a table and save the \u003Ccode\u003Efinal_agg\u003C/code\u003E DataFrame as a table. If the table already exists, we append it to the existing table using Snowpark \u003Ccode\u003Emerge()\u003C/code\u003E function.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PROCEDURE LOAD_DAILY_CITY_METRICS_SP()\nRETURNS VARIANT\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.10'\nPACKAGES = ('snowflake-snowpark-python')\nHANDLER = 'main'\nAS\n$$\nfrom snowflake.snowpark import Session\nimport snowflake.snowpark.functions as F\n\ndef table_exists(session, schema='', name=''):\n    exists = session.sql("SELECT EXISTS (SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = '{}' AND TABLE_NAME = '{}') AS TABLE_EXISTS".format(schema, name)).collect()[0]['TABLE_EXISTS']\n    return exists\n\ndef main(session: Session) -> str:\n    schema_name = "HOL_SCHEMA"\n    table_name = "DAILY_CITY_METRICS"\n\n    # Define the tables\n    order_detail = session.table("ORDER_DETAIL")\n    history_day = session.table("FROSTBYTE_WEATHERSOURCE.ONPOINT_ID.HISTORY_DAY")\n    location = session.table("LOCATION")\n\n    # Join the tables\n    order_detail = order_detail.join(location, order_detail['LOCATION_ID'] == location['LOCATION_ID'])\n    order_detail = order_detail.join(history_day, (F.builtin("DATE")(order_detail['ORDER_TS']) == history_day['DATE_VALID_STD']) & (location['ISO_COUNTRY_CODE'] == history_day['COUNTRY']) & (location['CITY'] == history_day['CITY_NAME']))\n\n    # Aggregate the data\n    final_agg = order_detail.group_by(F.col('DATE_VALID_STD'), F.col('CITY_NAME'), F.col('ISO_COUNTRY_CODE')) \\\n                        .agg( \\\n                            F.sum('PRICE').alias('DAILY_SALES_SUM'), \\\n                            F.avg('AVG_TEMPERATURE_AIR_2M_F').alias("AVG_TEMPERATURE_F"), \\\n                            F.avg("TOT_PRECIPITATION_IN").alias("AVG_PRECIPITATION_IN"), \\\n                        ) \\\n                        .select(F.col("DATE_VALID_STD").alias("DATE"), F.col("CITY_NAME"), F.col("ISO_COUNTRY_CODE").alias("COUNTRY_DESC"), \\\n                            F.builtin("ZEROIFNULL")(F.col("DAILY_SALES_SUM")).alias("DAILY_SALES"), \\\n                            F.round(F.col("AVG_TEMPERATURE_F"), 2).alias("AVG_TEMPERATURE_FAHRENHEIT"), \\\n                            F.round(F.col("AVG_PRECIPITATION_IN"), 2).alias("AVG_PRECIPITATION_INCHES"), \\\n                        )\n\n    # If the table doesn't exist then create it\n    if not table_exists(session, schema=schema_name, name=table_name):\n        final_agg.write.mode("overwrite").save_as_table(table_name)\n\n        return f"Successfully created {table_name}"\n    # Otherwise update it\n    else:\n        cols_to_update = {c: final_agg[c] for c in final_agg.schema.names}\n\n        dcm = session.table(f"{schema_name}.{table_name}")\n        dcm.merge(final_agg, (dcm['DATE'] == final_agg['DATE']) & (dcm['CITY_NAME'] == final_agg['CITY_NAME']) & (dcm['COUNTRY_DESC'] == final_agg['COUNTRY_DESC']), \\\n                            [F.when_matched().update(cols_to_update), F.when_not_matched().insert(cols_to_update)])\n\n        return f"Successfully updated {table_name}"\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERunning the Sproc\u003C/h3\u003E\n","\u003Cp\u003EIn the above step, we only created a Stored procedure. However, we need to invoke \u003Ccode\u003ECALL\u003C/code\u003E on the SPROC to actually perform those aggregations and calculations.\u003C/p\u003E\n","\u003Cp\u003ETo calculate \u003Ccode\u003Efinal_agg\u003C/code\u003E and update the \u003Ccode\u003EDAILY_CITY_METRICS\u003C/code\u003E table, you can run the following SQL command in the SQL file \u003Ccode\u003Esteps/06_load_daily_city_metrics.sql\u003C/code\u003E from VS Code.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL LOAD_DAILY_CITY_METRICS_SP();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also simply run the entire script from Snowsight UI or VS Code. Execute the \u003Ccode\u003Esteps/06_load_daily_city_metrics.sql\u003C/code\u003E script by selecting Execute All from VS Code.\u003C/p\u003E\n","\u003Ch3\u003EViewing What Happened in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-activity.html#query-history\"\u003EQuery History\u003C/a\u003E in Snowflake is a very powerful feature, that logs every query run against your Snowflake account, no matter which tool or process initiated it. And this is especially helpful when working with client tools and APIs.\u003C/p\u003E\n","\u003Cp\u003EThe stored procedure we invoked in the previous step \u003Ccode\u003ELOAD_DAILY_CITY_METRICS_SP\u003C/code\u003E would be logged in the Query History tab in the Snowsight UI.\u003C/p\u003E\n","\u003Ch3\u003EMore on the Snowpark API\u003C/h3\u003E\n","\u003Cp\u003EIn this step we're starting to really use the Snowpark DataFrame API for data transformations. The Snowpark API provides the same functionality as the \u003Ca href=\"https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/index.html\"\u003ESpark SQL API\u003C/a\u003E. To begin with you need to create a Snowpark session object. Like PySpark, this is accomplished with the \u003Ccode\u003ESession.builder.configs().create()\u003C/code\u003E methods.\u003C/p\u003E\n","\u003Cp\u003EWhen building a Snowpark Python sproc the contract is that the first argument to the entry point (or handler) function is a Snowpark session.\u003C/p\u003E\n","\u003Cp\u003EThe first thing you'll notice in the \u003Ccode\u003Esteps/06_load_daily_city_metrics.sql\u003C/code\u003E script is that we have some functions which use SQL to create objects in Snowflake and to check object status. To issue a SQL statement to Snowflake with the Snowpark API you use the \u003Ccode\u003Esession.sql()\u003C/code\u003E function, like you'd expect. Here's one example:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef table_exists(session, schema='', name=''):\n    exists = session.sql("SELECT EXISTS (SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = '{}' AND TABLE_NAME = '{}') AS TABLE_EXISTS".format(schema, name)).collect()[0]['TABLE_EXISTS']\n    return exists\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe second thing to point out is how we're using DataFrames to join the data from different data sources into an \u003Ccode\u003EORDER_DETAIL\u003C/code\u003E df using the \u003Ccode\u003Ejoin()\u003C/code\u003E API.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eorder_detail = order_detail.join(location, order_detail['LOCATION_ID'] == location['LOCATION_ID'])\n    order_detail = order_detail.join(history_day, (F.builtin("DATE")(order_detail['ORDER_TS']) == history_day['DATE_VALID_STD']) & (location['ISO_COUNTRY_CODE'] == history_day['COUNTRY']) & (location['CITY'] == history_day['CITY_NAME']))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe last thing to point out is how we are using the Snowpark Python Dataframe APIs to aggregate DataFrames using APIs such as \u003Ccode\u003Eagg()\u003C/code\u003E, \u003Ccode\u003Egroup_by()\u003C/code\u003E, and \u003Ccode\u003Eselect()\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efinal_agg = order_detail.group_by(F.col('DATE_VALID_STD'), F.col('CITY_NAME'), F.col('ISO_COUNTRY_CODE')) \\\n                        .agg( \\\n                            F.sum('PRICE').alias('DAILY_SALES_SUM'), \\\n                            F.avg('AVG_TEMPERATURE_AIR_2M_F').alias("AVG_TEMPERATURE_F"), \\\n                            F.avg("TOT_PRECIPITATION_IN").alias("AVG_PRECIPITATION_IN"), \\\n                        ) \\\n                        .select(F.col("DATE_VALID_STD").alias("DATE"), F.col("CITY_NAME"), F.col("ISO_COUNTRY_CODE").alias("COUNTRY_DESC"), \\\n                            F.builtin("ZEROIFNULL")(F.col("DAILY_SALES_SUM")).alias("DAILY_SALES"), \\\n                            F.round(F.col("AVG_TEMPERATURE_F"), 2).alias("AVG_TEMPERATURE_FAHRENHEIT"), \\\n                            F.round(F.col("AVG_PRECIPITATION_IN"), 2).alias("AVG_PRECIPITATION_INCHES"), \\\n                        )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAgain, for more details about the Snowpark Python DataFrame API, please check out our \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes.html\"\u003EWorking with Dataframe in Snowpark Python\u003C/a\u003E page.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOrchestrate Jobs\u003C/h2\u003E\n","\u003Cp\u003EDuring this step we will be orchestrating our new Snowpark pipelines with Snowflake's native orchestration feature named Tasks. You can create and deploy Snowflake Task objects using SQL as well as Python Task APIs. For the scope of this quickstart, we will use Snowflake Python Task APIs to create and run Tasks.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will create two tasks, one for each stored procedure, and chain them together. We will then deploy or run the tasks to operationalize the data pipeline.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will run through the commands in the Python file \u003Ccode\u003Esteps/07_deploy_task_dag.py\u003C/code\u003E from VS Code. To execute the file, you can open the terminal in VS Code and run \u003Ccode\u003Ecd steps && python 07_deploy_task_dag.py\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ETo put this in context, we are on step \u003Cstrong\u003E#7\u003C/strong\u003E in our data flow overview:\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png\" alt=\"Quickstart Pipeline Overview\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EExploring the different terminologies associated with Snowflake Tasks\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETasks\u003C/strong\u003E: Task is a basic, smallest unit of execution. A task can execute a single SQL statement, a call to a stored procedure, or any procedural logic using \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-scripting/index\"\u003ESnowflake Scripting\u003C/a\u003E. One or more tasks make up a Dag.  A root task is a special type of task which is the first task in a dag with no parent tasks before it.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDags\u003C/strong\u003E: A Directed Acyclic Graph (DAG) is a series of tasks composed of a single root task and additional tasks, organized by their dependencies. DAGs flow in a single direction, meaning a task runs only after all of its predecessor tasks have run successfully to completion.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchedule Interval\u003C/strong\u003E: Schedule interval refers to the interval of time between successive scheduled executions of a standalone task or the root task in a DAG.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDeploying a Dag\u003C/strong\u003E: When you create a dag, you define the schedule interval, transformation logic in the tasks and task dependencies. However, you need to deploy a dag to actually run those transformations on the schedule. If you don't deploy the dag, the dag will not run on the schedule.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERunning a dag\u003C/strong\u003E: When a dag is deployed, it is run on the defined schedule. Each instance of this scheduled run is called a Task Run.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreating Tasks, and DAGs\u003C/h3\u003E\n","\u003Cp\u003ELet us create a dag and configure the schedule and transformations we want to run as part of the dag.\u003C/p\u003E\n","\u003Cp\u003EIn this example, we have two tasks \u003Ccode\u003Edag_task1\u003C/code\u003E, \u003Ccode\u003Edag_task2\u003C/code\u003E and \u003Ccode\u003Edag_task3\u003C/code\u003E as part of \u003Ccode\u003EHOL_DAG\u003C/code\u003E. The dag is scheduled daily.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Edag_task1\u003C/code\u003E loads the  \u003Ccode\u003Eorder_detail\u003C/code\u003E data by calling the stored procedure \u003Ccode\u003ELOAD_EXCEL_WORKSHEET_TO_TABLE_SP\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Edag_task2\u003C/code\u003E loads the \u003Ccode\u003Elocation\u003C/code\u003E data by calling the stored procedure \u003Ccode\u003ELOAD_EXCEL_WORKSHEET_TO_TABLE_SP\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Edag_task3\u003C/code\u003E updates the \u003Ccode\u003EDAILY_CITY_METRICS\u003C/code\u003E table in snowflake by calling the stored procedure \u003Ccode\u003ELOAD_DAILY_CITY_METRICS_SP\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere is the code snippet for dag and task creation:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edag_name = "HOL_DAG"\n    dag = DAG(dag_name, schedule=timedelta(days=1), warehouse=warehouse_name)\n    with dag:\n        dag_task1 = DAGTask("LOAD_ORDER_DETAIL_TASK", definition="CALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/order_detail.xlsx'), 'order_detail', 'ORDER_DETAIL')", warehouse=warehouse_name)\n        dag_task2 = DAGTask("LOAD_LOCATION_TASK", definition="CALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/location.xlsx'), 'location', 'LOCATION')", warehouse=warehouse_name)\n        dag_task3 = DAGTask("LOAD_DAILY_CITY_METRICS_TASK", definition="CALL LOAD_DAILY_CITY_METRICS_SP()", warehouse=warehouse_name)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGreat, we have the tasks and the dag created. But how do we define the task dependencies? Which tasks should run first?\u003C/p\u003E\n","\u003Cp\u003EIf you are familiar with Apache Airflow, you might know how we can use \u003Ccode\u003E>>\u003C/code\u003E operator to define task dependency. If not, let's understand the flow.\u003C/p\u003E\n","\u003Cp\u003EHere is how we define the order of execution of the tasks in our dag:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edag_task1 >> dag_task3\ndag_task2 >> dag_task3\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe above definition \u003Ccode\u003Edag_task1 >> dag_task3\u003C/code\u003E means that \u003Ccode\u003Edag_task3\u003C/code\u003E is dependant on \u003Ccode\u003Edag_task1\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ESimilarly, \u003Ccode\u003Edag_task2 >> dag_task3\u003C/code\u003E means that \u003Ccode\u003Edag_task3\u003C/code\u003E is dependant on \u003Ccode\u003Edag_task2\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EDeploying the DAG\u003C/h3\u003E\n","\u003Cp\u003ESo far, we have only created the dag and defined the order of execution of tasks within the dag. To enable the dag to run on the defined schedule, we have to \u003Ccode\u003Edeploy()\u003C/code\u003E it first.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edag_op.deploy(dag, mode="orreplace")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter we deploy the dag, the tasks in the dag will run periodically as per the schedule interval in the dag definition.\u003C/p\u003E\n","\u003Ch3\u003ERunning a DAG\u003C/h3\u003E\n","\u003Cp\u003EIn addition to running the dags periodically, suppose if you need to run the dag on demand for debugging or troubleshooting, you can use the \u003Ccode\u003Erun()\u003C/code\u003E function.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edag_op.run(dag)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd for more details on Tasks see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro.html\"\u003EIntroduction to Tasks\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ETask Metadata\u003C/h3\u003E\n","\u003Cp\u003ESnowflake keeps metadata for almost everything you do, and makes that metadata available for you to query (and to create any type of process around). Tasks are no different. Snowflake maintains rich metadata to help you monitor your task runs. Here are a few sample SQL queries you can use to monitor your tasks runs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Get a list of tasks\nSHOW TASKS;\n\n-- Task execution history in the past day\nSELECT *\nFROM TABLE(INFORMATION_SCHEMA.TASK_HISTORY(\n    SCHEDULED_TIME_RANGE_START=>DATEADD('DAY',-1,CURRENT_TIMESTAMP()),\n    RESULT_LIMIT => 100))\nORDER BY SCHEDULED_TIME DESC\n;\n\n-- Scheduled task runs\nSELECT\n    TIMESTAMPDIFF(SECOND, CURRENT_TIMESTAMP, SCHEDULED_TIME) NEXT_RUN,\n    SCHEDULED_TIME,\n    NAME,\n    STATE\nFROM TABLE(INFORMATION_SCHEMA.TASK_HISTORY())\nWHERE STATE = 'SCHEDULED'\nORDER BY COMPLETED_TIME DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMonitoring Tasks\u003C/h3\u003E\n","\u003Cp\u003ENow, after your dag is deployed, all the tasks in your dag will be running as per the defined schedule. However, as a Snowflake user, how can you peek into the success and failure of each run, look at the task run time, and more?\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides some rich task observability features in the Snowsight UI. Try it out for yourself by following these steps:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn the Snowsight navigation menu, click \u003Cstrong\u003EData\u003C/strong\u003E » \u003Cstrong\u003EDatabases\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EIn the right pane, using the object explorer, navigate to a database and schema.\u003C/li\u003E\u003Cli\u003EFor the selected schema, select and expand \u003Cstrong\u003ETasks\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect a task. Task information is displayed, including \u003Cstrong\u003ETask Details\u003C/strong\u003E, \u003Cstrong\u003EGraph\u003C/strong\u003E, and \u003Cstrong\u003ERun History\u003C/strong\u003E sub-tabs.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EGraph\u003C/strong\u003E tab. The task graph appears, displaying a hierarchy of child tasks.\u003C/li\u003E\u003Cli\u003ESelect a task to view its details.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/tasks_and_dags.png\" alt=\"Tasks\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo monitor all the tasks at an account level, you can use the Task History tab as well.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn the Snowsight navigation menu, click \u003Cstrong\u003EActivity\u003C/strong\u003E » \u003Cstrong\u003ETask History\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EIn the right pane, select the \u003Cstrong\u003ETask Runs\u003C/strong\u003E tab. You can scroll through all the task runs in the account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/task_history_runs.png\" alt=\"Task Runs\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor more details, and to learn about viewing account level task history, please check out our \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks.html\"\u003EViewing Task History\u003C/a\u003E documentation.\u003C/p\u003E\n","\u003Ch3\u003EQuery History for Tasks\u003C/h3\u003E\n","\u003Cp\u003EOne important thing to understand about tasks, is that the queries which get executed by the task won't show up with the default Query History UI settings. In order to see the queries that just ran you need to do the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERemove filters at the top of this table, including your username, as later scheduled tasks will run as "System":\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/query_history_remove_filter1.png\" alt=\"remove filter\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick "Filter", and add filter option 'Queries executed by user tasks' and click "Apply Filters":\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/query_history_remove_filter2.png\" alt=\"remove filter\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should now see all the queries run by your tasks!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETeardown\u003C/h2\u003E\n","\u003Cp\u003EOnce you're finished with the Quickstart and want to clean things up, you can simply run the \u003Ccode\u003Esteps/08_teardown.sql\u003C/code\u003E. Since this is a SQL script we will be using our native VS Code extension to execute it. So simply open the \u003Ccode\u003Esteps/08_teardown.sql\u003C/code\u003E script in VS Code and run the whole thing using the "Execute All Statements" button in the upper right corner of the editor window.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You have successfully built a data engineering pipeline using Snowpark Python, loaded data from an external stage using Snowflake Dynamic File Access, used data from Snowflake Marketplace and orchestrated the data pipeline using Snowflake Tasks as well.\u003C/p\u003E\n","\u003Cp\u003EWe would love your feedback on this QuickStart Guide! Please submit your feedback using this \u003Ca href=\"https://forms.gle/M7C8EC3sYKide1ro9\"\u003EFeedback Form\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EHere is the overview of what we built:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHow to ingest data from an external stage such as an S3 bucket into a Snowflake table\u003C/li\u003E\u003Cli\u003EHow to access data from Snowflake Marketplace and use it for your analysis\u003C/li\u003E\u003Cli\u003EHow to analyze data and perform data engineering tasks using Snowpark DataFrame API, Python Stored Procedures and more\u003C/li\u003E\u003Cli\u003EHow to use open-source Python libraries from curated Snowflake Anaconda channel\u003C/li\u003E\u003Cli\u003EHow to create Snowflake Tasks and use the Python Tasks API to schedule data pipelines\u003C/li\u003E\u003Cli\u003EHow to use VS Code extension for Snowflake to perform standard snowflake operations from VS Code and Snowsigt UI\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png\" alt=\"Quickstart Pipeline Overview\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-data-engineering-with-snowpark-python-intro\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EAdvanced: Data Engineering Pipelines with Snowpark Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python/\"\u003EAdvanced: Getting Started with Data Engineering and ML using Snowpark for Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html\"\u003ESnowpark for Python Developer Guide\u003C/a\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/stored-procedures-python.html\"\u003EWriting Stored Procedures in Snowpark (Python)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes.html\"\u003EWorking with DataFrame in Snowpark Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc\"\u003ESnowflake Visual Studio Code Extension\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"Introduction to data engineering with Snowpark Python covering DataFrames, UDFs, transformations, pipeline patterns, and best practices.","title":"Intro to Data Engineering with Snowpark Python",":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\n\nThis Quickstart will cover the basics of data engineering with Snowpark for Python. By completing this guide, you will be able to build a data pipeline to process data from different sources, and periodically run the pipeline to update your data tables in Snowflake.\n\nHere is a summary of what you will be able to learn in each step by following this quickstart:\n\n- **Setup Environment**: Use stages and tables to ingest and organize raw data from S3 into Snowflake\n- **Snowflake Marketplace**: Download the data you need from Snowflake Marketplace and use it for your analysis\n- **Data Engineering**: Leverage Snowpark for Python DataFrames to perform data transformations such as group by, aggregate, and join to prep for the data for downstream applications\n- **Orchestrating Pipelines**: Use Snowflake Python Tasks API to turn your data pipeline code into operational pipelines with integrated monitoring\n\nIn case you are new to some of the technologies mentioned above, here is a quick summary with the links to documentation.\n\n### What is Snowpark?\n\nSnowpark is the set of libraries and code execution environments that run Python and other programming languages next to your data in Snowflake. Snowpark can be used to build data pipelines, ML models, apps, and other data processing tasks.\n\n**Client Side Libraries** - Snowpark libraries can be installed and downloaded from any client-side notebook or IDE and are used for code development and deployment. Libraries include the Snowpark API for data pipelines and apps and the Snowpark ML API for end to end machine learning.\n\n**Code Execution Environments** - Snowpark provides elastic compute environments for secure execution of your code in Snowflake. These server-side capabilities allow users to bring in and run custom logic in Python with user-defined functions and stored procedures. \n\nLearn more about [Snowpark](/snowpark/).\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/snowpark.png)\n\n### Working with Snowflake Marketplace\n\nSnowflake Marketplace provides visibility to a wide variety of datasets from third-party data stewards which broaden access to data points used to transform business processes. Snowflake Marketplace also removes the need to integrate and model data by providing secure access to data sets fully maintained by the data provider.\n\n### What you will learn\n\n- How to ingest data from an external stage such as an S3 bucket into a Snowflake table\n- How to access data from Snowflake Marketplace and use it for your analysis\n- How to analyze data and perform data engineering tasks using Snowpark DataFrame API, Python Stored Procedures and more\n- How to use open-source Python libraries from curated Snowflake Anaconda channel\n- How to create Snowflake Tasks and use the Python Tasks API to schedule data pipelines\n- How to use VS Code extension for Snowflake to perform standard snowflake operations from VS Code and Snowsigt UI\n\n![assets/data_pipeline_overview.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png)\n\n### Prerequisites\n\n- Familiarity with Python\n- Familiarity with the DataFrame API\n- Familiarity with Snowflake\n- Familiarity with Git repositories and GitHub\n\n### What You’ll Need\n\nYou will need the following things before beginning:\n\n- Snowflake account\n  - **A Snowflake Account**\n  - **A Snowflake user created with ACCOUNTADMIN permissions**. This user will be used to get things setup in Snowflake.\n  - **Anaconda Terms & Conditions accepted**. See Getting Started section in [Third-Party Packages](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#getting-started).\n- GitHub account\n  - **A GitHub account**. If you don't already have a GitHub account you can create one for free. Visit the [Join GitHub](https://github.com/signup) page to get started.\n\n\u003C!-- ------------------------ --\u003E\n## Quickstart Setup\n\n\n### Fork the GitHub Repository\n\nThe very first step is to fork the GitHub repository [Intro to Data Engineering with Snowpark Python associated GitHub Repository](https://github.com/Snowflake-Labs/sfguide-data-engineering-with-snowpark-python-intro). This repository contains all the code you need to successfully complete this Quickstart guide.  Click on the \"Fork\" button near the top right. Complete any required fields and click \"Create Fork\".\n\n### Create GitHub Codespace\n\nFor this Quickstart we will be using [GitHub Codespaces](https://docs.github.com/en/codespaces/overview) for our development environment. Codespaces offer a hosted development environment with a hosted, web-based VS Code environment. GitHub currently offers [60 hours for free each month](https://github.com/features/codespaces) when using a 2 node environment, which should be enough to work through this lab.\n\nTo create a GitHub Codespace, click on the green `\u003C\u003E Code` button from the GitHub repository homepage. In the Code popup, click on the `Codespaces` tab and then on the green `Create codespace on main`.\n\n![assets/codespace_setup.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/codespace_setup.png)\n\nThis will open a new tab and begin setting up your codespace. It will take a few minutes as it sets up the entire environment for this Quickstart. Here is what is being done for you:\n\n- Creating a container for your environment\n- Installing Anaconda (miniconda)\n- SnowSQL setup\n  - Installing SnowSQL\n  - Creating a directory and default config file for SnowSQL\n- Anaconda setup\n  - Creating the Anaconda environment\n  - Installing the Snowpark Python library\n- VS Code setup\n  - Installing VS Code\n  - Installing the Snowflake VS Code extension\n- Starting a hosted, web-based VS Code editor\n\nOnce the codepsace has been created and started you should see a hosted web-based version of VS Code with your forked repository set up! Just a couple more things and we're ready to start.\n\n\u003E \n\u003E **NOTE**:\n\u003E Going forward references to `VS Code` means the VS Code editor on the Codespaces\n\n### Configure Snowflake Credentials\nWe will not be directly using [the SnowSQL command line client](https://docs.snowflake.com/en/user-guide/snowsql.html) for this Quickstart, but we will be storing our Snowflake connection details in the SnowSQL config file located at `~/.snowsql/config`. A default config file was created for you during the codespace setup.\n\nThe easiest way to edit the default `~/.snowsql/config` file is directly from VS Code in your codespace. Type `Command-P`, type (or paste) `~/.snowsql/config` and hit return. The SnowSQL config file should now be open. You just need to edit the file and replace the `accountname`, `username`, and `password` with your values. See [here](https://docs.snowflake.com/en/user-guide/admin-account-identifier#finding-the-organization-and-account-name-for-an-account) on how to find the  account identifier, and make sure to replace the dot between the organization name and the account name with a hyphen. Then save and close the file.\n\n**Note:** The SnowCLI tool (and by extension this Quickstart) currently does not work with Key Pair authentication. It simply grabs your username and password details from the shared SnowSQL config file.\n\n### Verify Your Anaconda Environment is Activated\n\nDuring the codespace setup we created an Anaconda environment named `snowflake-demo`. And when VS Code started up it should have automatically activated the environment in your terminal. You should see something like this in the terminal, and in particular you should see `(snowflake-demo)` before your bash prompt.\n\n\u003C!-- ------------------------ --\u003E\n## Setup Snowflake Objects\n\n\n### Snowflake Extensions for VS Code\n\nYou can run SQL queries and Python APIs against Snowflake in many different ways through the Snowsight UI, SnowCLI, etc. But for this Quickstart we'll be using the Snowflake extension for VS Code. For a brief overview of Snowflake's native extension for VS Code, please check out our [VS Code Marketplace Snowflake extension page](https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc).\n\nTo put this in context, we are on step **#3** in our data flow overview:\n\n---\n\n![Quickstart Pipeline Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png)\n\n---\n\n### Create Roles, Databases, Tables, Schema and Stages\n\nYou can log into [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#) or VS Code to create all the snowflake objects needed to work through this guide.\n\nFor the purpose of this quickstart, we will use VS Code to run the SQL commands and create the Snowflake objects. To do that, open the Snowflake extension tab on the left and use the account identifier and other credentials you previously entered in the SnowSQL config to sign in.\n\nYou can now open the sql file `steps/03_setup_snowflake.sql` in VS Code. You can click on the `Execute` option above every SQL command to run each command separately or click on `Execute All` to run all the commands sequentially.\n\n---\n\n![Execute in VS Code](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/vscode_execute.png)\n\n---\n\n\u003E \n\u003E IMPORTANT:\n\u003E\n\u003E - If you use different names for objects created in this section, be sure to update scripts and code in the following sections accordingly.\n\u003E\n\u003E - For each SQL script block below, select all the statements in the block and execute them top to bottom.\n\nLet's run through the commands individually and understand what each command does and finally create Snowflake Objects required for the lab.\n\n### Creating Account Level Objects\n\n- In this step, we create the role `HOL_ROLE` and assign this role to `CURRENT_USER()` within Snowflake. This role will have access permissions to create all the Snowflake objects needed for the quickstart. First, grant the `HOL_ROLE` the same permissions as `SYSADMIN` role. Then, grant permissions to run tasks, monitor the execution of tasks and to import privileges on the database to `HOL_ROLE`. \n\n```sql\nSET MY_USER = CURRENT_USER();\nCREATE OR REPLACE ROLE HOL_ROLE;\nGRANT ROLE HOL_ROLE TO ROLE SYSADMIN;\nGRANT ROLE HOL_ROLE TO USER IDENTIFIER($MY_USER);\n\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE HOL_ROLE;\nGRANT MONITOR EXECUTION ON ACCOUNT TO ROLE HOL_ROLE;\nGRANT IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE TO ROLE HOL_ROLE;\n```\n\n- Create a new database `HOL_DB` and assign ownership of the database to `HOL_ROLE`.\n\n```sql\nCREATE OR REPLACE DATABASE HOL_DB;\nGRANT OWNERSHIP ON DATABASE HOL_DB TO ROLE HOL_ROLE;\n```\n\n- Create a new warehouse `HOL_WH` to serve the compute requirements for the quickstart. I have used a XSMALL warehouse, and configured it to auto suspend after 300 seconds (5 mins), and enabled auto resume on it. I have assigned ownership of the warehouse to the `HOL_ROLE` too.\n\n```sql\nCREATE OR REPLACE WAREHOUSE HOL_WH WAREHOUSE_SIZE = XSMALL, AUTO_SUSPEND = 300, AUTO_RESUME= TRUE;\nGRANT OWNERSHIP ON WAREHOUSE HOL_WH TO ROLE HOL_ROLE;\n```\n\n####  Database Level Objects\n\n- Set the right Database Context such as role, warehouse and database,\n\n```sql\nUSE ROLE HOL_ROLE;\nUSE WAREHOUSE HOL_WH;\nUSE DATABASE HOL_DB;\n\nUSE SCHEMA HOL_SCHEMA;\nCREATE OR REPLACE STAGE FROSTBYTE_RAW_STAGE\n    URL = 's3://sfquickstarts/data-engineering-with-snowpark-python/'\n;\n```\n\n\u003E \n\u003E IMPORTANT:\n\u003E\n\u003E - If you use different names for objects created in this section, be sure to update scripts and code in the following sections accordingly.\n\n\u003C!-- ------------------------ --\u003E\n## Load Weather\n\n\n\nDuring this step we will be \"loading\" the raw weather data to Snowflake. But \"loading\" is really the wrong word here. Because we're using Snowflake's unique data sharing capability we don't actually need to copy the data to our Snowflake account with a custom ETL process. Instead we can directly access the weather data shared by Weather Source in the Snowflake Marketplace.\n\nTo put this in context, we are on step **#4** in our data flow overview:\n\n---\n\n![Quickstart Pipeline Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png)\n\n---\n\n### Snowflake Marketplace\n\nSnowflake Marketplace provides visibility to a wide variety of datasets from third-party data stewards which broaden access to data points used to transform business processes. Snowflake Marketplace also removes the need to integrate and model data by providing secure access to data sets fully maintained by the data provider.\n\nBut what about data that needs constant updating - like the WEATHER data? We would need to build a pipeline process to constantly update that data to keep it fresh. Perhaps a better way to get this external data would be to source it from a trusted data supplier. Let them manage the data, keeping it accurate and up to date.\n\n### Weather data from Snowflake Marketplace\n\nWeather Source is a leading provider of global weather and climate data and their OnPoint Product Suite provides businesses with the necessary weather and climate data to quickly generate meaningful and actionable insights for a wide range of use cases across industries. Let's connect to the `Weather Source LLC: frostbyte` feed from Weather Source in the Snowflake Marketplace by following these steps:\n\n- Login to Snowsight\n- Click on the `Marketplace` link in the left navigation bar\n- Enter \"Weather Source LLC: frostbyte\" in the search box and click return\n- Click on the \"Weather Source LLC: frostbyte\" listing tile\n- Click the blue \"Get\" button\n  - Expand the \"Options\" dialog\n  - Change the \"Database name\" to read \"FROSTBYTE_WEATHERSOURCE\" (all capital letters)\n  - Select the \"HOL_ROLE\" role to have access to the new database\n- Click on the blue \"Get\" button\n\n---\n\n![Snowflake Marketplace](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/snowflake_marketplace.png)\n\n---\n\nThat's it... we don't have to do anything from here to keep this data updated. The provider will do that for us and data sharing means we are always seeing whatever they have published. How amazing is that? Just think of all the things you didn't have to do here to get access to an always up-to-date, third-party dataset!\n\n### Query the Weather data\n\nOpen the `steps/04_load_weather.sql` script in VS Code from the file Explorer in the left navigation bar, and select `Execute All` from the top right corner. Notice how easy it is to query data shared through the Snowflake Marketplace! You access it just like any other table or view in Snowflake:\n\n```sql\nSELECT * FROM FROSTBYTE_WEATHERSOURCE.ONPOINT_ID.POSTAL_CODES LIMIT 100;\n```\n\nYou can also view the shared database `FROSTBYTE_WEATHERSOURCE.ONPOINT_ID.POSTAL_CODE` by navigating to the Snowsight UI -\u003E Data -\u003E Databases.  \n\n\u003E \n\u003E IMPORTANT:\n\u003E\n\u003E - If you used a different name for the database while getting weather data from the marketplace, be sure to update scripts and code in the following sections accordingly.\n\n\u003C!-- ------------------------ --\u003E\n## Load Location and Order Detail\n\n\nDuring this step we will be \"loading\" the raw excel files containing location and order details data from an external stage (s3 bucket) to Snowflake using the Dynamic File Access feature. If you would like to peek at the raw excel files, you can check out the `data` folder in the [Git repo](https://github.com/Snowflake-Labs/sfguide-data-engineering-with-snowpark-python-intro/tree/main/data). We use a Python stored procedure to load this data from an S3 bucket into a Snowflake table.\n\nIn this step, we will run through the commands in the SQL file `steps/05_load_excel_files.sql`. You can open the sql file `steps/05_load_excel_files.sql` in VS Code. You can click on the `Execute` option above every SQL command to run each command separately or click on `Execute All` to run all the commands sequentially.\n\nTo put this in context, we are on step **#5** in our data flow overview:\n\n---\n\n![Quickstart Pipeline Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png)\n\n---\n\n### Dynamic File Access\n\nYou can read a file from an internal or external stage using the `SnowflakeFile` class in the Snowpark `snowflake.snowpark.files` module. The `SnowflakeFile` class provides dynamic file access, which lets you stream files of any size.\n\n### Loading Excel files from an External Stage\n\nIn this quickstart, we will use dynamic file access to load the excel files from an s3 bucket (an external stage we created before `FROSTBYTE_RAW_STAGE`).\n\nFirst let's check if the two excel files are present in the S3 bucket by calling `LIST` on the stage.\n\n```sql\nLIST @FROSTBYTE_RAW_STAGE/intro;\n```\n\n### Creating the Stored Procedure to load Excel files\n\nDuring this step we will be creating our first Snowpark Python stored procedure (or SPROC) to Snowflake. This SPROC `LOAD_EXCEL_WORKSHEET_TO_TABLE_SP` will load the excel data files into snowflake tables for further analysis.\n\nBelow is the SQL query to create the SPROC:\n\n- We use the python runtime 3.10 for the SPROC\n- We use the python packages `snowflake-snowpark-python`, `pandas` and `openpyxl` to load excel files\n- Then import `snowflake.snowpark.files.SnowflakeFile` for the dynamic file access to load file from an external stage\n- We use `Snowflake.open()` to open the excel file, read the contents of the file into a pandas DataFrame, and save the DataFrame as a Snowflake table using `save_as_table()` API.\n\n```sql\nCREATE OR REPLACE PROCEDURE LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(file_path string, worksheet_name string, target_table string)\nRETURNS VARIANT\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.10'\nPACKAGES = ('snowflake-snowpark-python', 'pandas', 'openpyxl')\nHANDLER = 'main'\nAS\n$$\nfrom snowflake.snowpark.files import SnowflakeFile\nfrom openpyxl import load_workbook\nimport pandas as pd\n \ndef main(session, file_path, worksheet_name, target_table):\n with SnowflakeFile.open(file_path, 'rb') as f:\n     workbook = load_workbook(f)\n     sheet = workbook.get_sheet_by_name(worksheet_name)\n     data = sheet.values\n \n     # Get the first line in file as a header line\n     columns = next(data)[0:]\n     # Create a DataFrame based on the second and subsequent lines of data\n     df = pd.DataFrame(data, columns=columns)\n \n     df2 = session.create_dataframe(df)\n     df2.write.mode(\"overwrite\").save_as_table(target_table)\n \n return True\n$$;\n```\n\n### Running the Sproc in Snowflake\n\nIn the above step, we only created the Stored procedure. However, we need to invoke `CALL` on the SPROC to actually load the excel files into a Snowflake target table.\n\nTo load `ORDER_DETAIL.xlsx` file, you can run the following SQL command in the SQL file `steps/05_load_excel_files.sql` from VS Code.\n\n```sql\nCALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/order_detail.xlsx'), 'order_detail', 'ORDER_DETAIL');\n```\n\nTo laod the `LOCATION.xlsx` file, you can run the following SQL command in the SQL file `steps/05_load_excel_files.sql` from VS Code.\n\n```sql\nCALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/location.xlsx'), 'location', 'LOCATION');\n```\n\nNext up, to verify if the excel files are loaded successfully into respective Snowflake tables, you can run the following `DESC` and `SELECT` statements from VS Code.\n\n```sql\nDESCRIBE TABLE ORDER_DETAIL;\nSELECT * FROM ORDER_DETAIL;\n\nDESCRIBE TABLE LOCATION;\nSELECT * FROM LOCATION;\n```\n\nFantastic. We now have all the raw data needed to build our data pipeline and further analyze them.\n\n\u003C!-- ------------------------ --\u003E\n\n## Daily City Metrics Update Sproc\n\n\nDuring this step we will be creating our second Snowpark Python sproc to Snowflake. This sproc will join the `ORDER_DETAIL` table with the `LOCATION` table and `HISTORY_DAY` table to create a final, aggregated table for analysis named `DAILY_CITY_METRICS`.\n\nIn this step, we will run through the commands in the SQL file `steps/06_load_daily_city_metrics.sql`. You can open the sql file `steps/06_load_daily_city_metrics.sql` in VS Code. You can click on the `Execute` option above every SQL command to run each command separately or click on `Execute All` to run all the commands sequentially.\n\nTo put this in context, we are on step **#6** in our data flow overview:\n\n---\n\n![Quickstart Pipeline Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png)\n\n---\n\n### Creating Sproc to Calculate Daily City Metrics\n\nBelow is the SQL query to create the SPROC:\n\n- We use the python runtime 3.10 for the SPROC, and use the python package `snowflake-snowpark-python`\n- First part of the Sproc contains `table_exists()` function which we use to verify if the raw tables exist in the defined database and schema.\n- Second part of the Sproc contains the  `main()` function that reads the raw tables `ORDER_DETAIL`, `LOCATION`, and `FROSTBYTE_WEATHERSOURCE.ONPOINT_ID.HISTORY_DAY`. Remember the Frostbyte Weather Source data is from Snowflake Marketplace but we can use it like any other standard snowflake table.\n- After loading all 3 raw tables into their respective Snowpark Python Dataframes, we prepare to join the Dataframes.\n- We join all three Dataframes into a final DataFrame `order_detail`. \n- Next step is data aggregation based on date, country and city. So we use group_by() on `DATE_VALID_STD`, `ISO_COUNTRY_CODE` and `CITY_NAME` columns.\n- After grouping by these 3 columns, we calculate the aggregate daily sales, daily average temperature in Farenheit, and daily average precipitation in inches.\n- The resulting DataFrame called `final_agg` is saved. If the table `DAILY_CITY_METRICS` does not exist, we create a table and save the `final_agg` DataFrame as a table. If the table already exists, we append it to the existing table using Snowpark `merge()` function. \n\n```sql\nCREATE OR REPLACE PROCEDURE LOAD_DAILY_CITY_METRICS_SP()\nRETURNS VARIANT\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.10'\nPACKAGES = ('snowflake-snowpark-python')\nHANDLER = 'main'\nAS\n$$\nfrom snowflake.snowpark import Session\nimport snowflake.snowpark.functions as F\n\ndef table_exists(session, schema='', name=''):\n    exists = session.sql(\"SELECT EXISTS (SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = '{}' AND TABLE_NAME = '{}') AS TABLE_EXISTS\".format(schema, name)).collect()[0]['TABLE_EXISTS']\n    return exists\n\ndef main(session: Session) -\u003E str:\n    schema_name = \"HOL_SCHEMA\"\n    table_name = \"DAILY_CITY_METRICS\"\n\n    # Define the tables\n    order_detail = session.table(\"ORDER_DETAIL\")\n    history_day = session.table(\"FROSTBYTE_WEATHERSOURCE.ONPOINT_ID.HISTORY_DAY\")\n    location = session.table(\"LOCATION\")\n\n    # Join the tables\n    order_detail = order_detail.join(location, order_detail['LOCATION_ID'] == location['LOCATION_ID'])\n    order_detail = order_detail.join(history_day, (F.builtin(\"DATE\")(order_detail['ORDER_TS']) == history_day['DATE_VALID_STD']) & (location['ISO_COUNTRY_CODE'] == history_day['COUNTRY']) & (location['CITY'] == history_day['CITY_NAME']))\n\n    # Aggregate the data\n    final_agg = order_detail.group_by(F.col('DATE_VALID_STD'), F.col('CITY_NAME'), F.col('ISO_COUNTRY_CODE')) \\\n                        .agg( \\\n                            F.sum('PRICE').alias('DAILY_SALES_SUM'), \\\n                            F.avg('AVG_TEMPERATURE_AIR_2M_F').alias(\"AVG_TEMPERATURE_F\"), \\\n                            F.avg(\"TOT_PRECIPITATION_IN\").alias(\"AVG_PRECIPITATION_IN\"), \\\n                        ) \\\n                        .select(F.col(\"DATE_VALID_STD\").alias(\"DATE\"), F.col(\"CITY_NAME\"), F.col(\"ISO_COUNTRY_CODE\").alias(\"COUNTRY_DESC\"), \\\n                            F.builtin(\"ZEROIFNULL\")(F.col(\"DAILY_SALES_SUM\")).alias(\"DAILY_SALES\"), \\\n                            F.round(F.col(\"AVG_TEMPERATURE_F\"), 2).alias(\"AVG_TEMPERATURE_FAHRENHEIT\"), \\\n                            F.round(F.col(\"AVG_PRECIPITATION_IN\"), 2).alias(\"AVG_PRECIPITATION_INCHES\"), \\\n                        )\n\n    # If the table doesn't exist then create it\n    if not table_exists(session, schema=schema_name, name=table_name):\n        final_agg.write.mode(\"overwrite\").save_as_table(table_name)\n\n        return f\"Successfully created {table_name}\"\n    # Otherwise update it\n    else:\n        cols_to_update = {c: final_agg[c] for c in final_agg.schema.names}\n\n        dcm = session.table(f\"{schema_name}.{table_name}\")\n        dcm.merge(final_agg, (dcm['DATE'] == final_agg['DATE']) & (dcm['CITY_NAME'] == final_agg['CITY_NAME']) & (dcm['COUNTRY_DESC'] == final_agg['COUNTRY_DESC']), \\\n                            [F.when_matched().update(cols_to_update), F.when_not_matched().insert(cols_to_update)])\n\n        return f\"Successfully updated {table_name}\"\n$$;\n```\n\n### Running the Sproc\n\nIn the above step, we only created a Stored procedure. However, we need to invoke `CALL` on the SPROC to actually perform those aggregations and calculations.\n\nTo calculate `final_agg` and update the `DAILY_CITY_METRICS` table, you can run the following SQL command in the SQL file `steps/06_load_daily_city_metrics.sql` from VS Code.\n\n```sql\nCALL LOAD_DAILY_CITY_METRICS_SP();\n```\n\nYou can also simply run the entire script from Snowsight UI or VS Code. Execute the `steps/06_load_daily_city_metrics.sql` script by selecting Execute All from VS Code.\n\n### Viewing What Happened in Snowflake\n\nThe [Query History](https://docs.snowflake.com/en/user-guide/ui-snowsight-activity.html#query-history) in Snowflake is a very powerful feature, that logs every query run against your Snowflake account, no matter which tool or process initiated it. And this is especially helpful when working with client tools and APIs.\n\nThe stored procedure we invoked in the previous step `LOAD_DAILY_CITY_METRICS_SP` would be logged in the Query History tab in the Snowsight UI.\n\n### More on the Snowpark API\n\nIn this step we're starting to really use the Snowpark DataFrame API for data transformations. The Snowpark API provides the same functionality as the [Spark SQL API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/index.html). To begin with you need to create a Snowpark session object. Like PySpark, this is accomplished with the `Session.builder.configs().create()` methods.\n\nWhen building a Snowpark Python sproc the contract is that the first argument to the entry point (or handler) function is a Snowpark session.\n\nThe first thing you'll notice in the `steps/06_load_daily_city_metrics.sql` script is that we have some functions which use SQL to create objects in Snowflake and to check object status. To issue a SQL statement to Snowflake with the Snowpark API you use the `session.sql()` function, like you'd expect. Here's one example:\n\n```python\ndef table_exists(session, schema='', name=''):\n    exists = session.sql(\"SELECT EXISTS (SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = '{}' AND TABLE_NAME = '{}') AS TABLE_EXISTS\".format(schema, name)).collect()[0]['TABLE_EXISTS']\n    return exists\n```\n\nThe second thing to point out is how we're using DataFrames to join the data from different data sources into an `ORDER_DETAIL` df using the `join()` API.\n\n```python\norder_detail = order_detail.join(location, order_detail['LOCATION_ID'] == location['LOCATION_ID'])\n    order_detail = order_detail.join(history_day, (F.builtin(\"DATE\")(order_detail['ORDER_TS']) == history_day['DATE_VALID_STD']) & (location['ISO_COUNTRY_CODE'] == history_day['COUNTRY']) & (location['CITY'] == history_day['CITY_NAME']))\n```\n\nThe last thing to point out is how we are using the Snowpark Python Dataframe APIs to aggregate DataFrames using APIs such as `agg()`, `group_by()`, and `select()`.\n\n```python\nfinal_agg = order_detail.group_by(F.col('DATE_VALID_STD'), F.col('CITY_NAME'), F.col('ISO_COUNTRY_CODE')) \\\n                        .agg( \\\n                            F.sum('PRICE').alias('DAILY_SALES_SUM'), \\\n                            F.avg('AVG_TEMPERATURE_AIR_2M_F').alias(\"AVG_TEMPERATURE_F\"), \\\n                            F.avg(\"TOT_PRECIPITATION_IN\").alias(\"AVG_PRECIPITATION_IN\"), \\\n                        ) \\\n                        .select(F.col(\"DATE_VALID_STD\").alias(\"DATE\"), F.col(\"CITY_NAME\"), F.col(\"ISO_COUNTRY_CODE\").alias(\"COUNTRY_DESC\"), \\\n                            F.builtin(\"ZEROIFNULL\")(F.col(\"DAILY_SALES_SUM\")).alias(\"DAILY_SALES\"), \\\n                            F.round(F.col(\"AVG_TEMPERATURE_F\"), 2).alias(\"AVG_TEMPERATURE_FAHRENHEIT\"), \\\n                            F.round(F.col(\"AVG_PRECIPITATION_IN\"), 2).alias(\"AVG_PRECIPITATION_INCHES\"), \\\n                        )\n```\n\nAgain, for more details about the Snowpark Python DataFrame API, please check out our [Working with Dataframe in Snowpark Python](https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes.html) page.\n\n\u003C!-- ------------------------ --\u003E\n\n## Orchestrate Jobs\n\n\nDuring this step we will be orchestrating our new Snowpark pipelines with Snowflake's native orchestration feature named Tasks. You can create and deploy Snowflake Task objects using SQL as well as Python Task APIs. For the scope of this quickstart, we will use Snowflake Python Task APIs to create and run Tasks.\n\nIn this step, we will create two tasks, one for each stored procedure, and chain them together. We will then deploy or run the tasks to operationalize the data pipeline.\n\nIn this step, we will run through the commands in the Python file `steps/07_deploy_task_dag.py` from VS Code. To execute the file, you can open the terminal in VS Code and run `cd steps && python 07_deploy_task_dag.py`.\n\nTo put this in context, we are on step **#7** in our data flow overview:\n\n---\n\n![Quickstart Pipeline Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png)\n\n---\n\n### Exploring the different terminologies associated with Snowflake Tasks\n\n- **Tasks**: Task is a basic, smallest unit of execution. A task can execute a single SQL statement, a call to a stored procedure, or any procedural logic using [Snowflake Scripting](https://docs.snowflake.com/en/developer-guide/snowflake-scripting/index). One or more tasks make up a Dag.  A root task is a special type of task which is the first task in a dag with no parent tasks before it.\n- **Dags**: A Directed Acyclic Graph (DAG) is a series of tasks composed of a single root task and additional tasks, organized by their dependencies. DAGs flow in a single direction, meaning a task runs only after all of its predecessor tasks have run successfully to completion.\n- **Schedule Interval**: Schedule interval refers to the interval of time between successive scheduled executions of a standalone task or the root task in a DAG.\n- **Deploying a Dag**: When you create a dag, you define the schedule interval, transformation logic in the tasks and task dependencies. However, you need to deploy a dag to actually run those transformations on the schedule. If you don't deploy the dag, the dag will not run on the schedule.\n- **Running a dag**: When a dag is deployed, it is run on the defined schedule. Each instance of this scheduled run is called a Task Run.\n\n### Creating Tasks, and DAGs\n\nLet us create a dag and configure the schedule and transformations we want to run as part of the dag.\n\nIn this example, we have two tasks `dag_task1`, `dag_task2` and `dag_task3` as part of `HOL_DAG`. The dag is scheduled daily.\n\n- `dag_task1` loads the  `order_detail` data by calling the stored procedure `LOAD_EXCEL_WORKSHEET_TO_TABLE_SP`.\n- `dag_task2` loads the `location` data by calling the stored procedure `LOAD_EXCEL_WORKSHEET_TO_TABLE_SP`.\n- `dag_task3` updates the `DAILY_CITY_METRICS` table in snowflake by calling the stored procedure `LOAD_DAILY_CITY_METRICS_SP`.\n\nHere is the code snippet for dag and task creation:\n\n```python\ndag_name = \"HOL_DAG\"\n    dag = DAG(dag_name, schedule=timedelta(days=1), warehouse=warehouse_name)\n    with dag:\n        dag_task1 = DAGTask(\"LOAD_ORDER_DETAIL_TASK\", definition=\"CALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/order_detail.xlsx'), 'order_detail', 'ORDER_DETAIL')\", warehouse=warehouse_name)\n        dag_task2 = DAGTask(\"LOAD_LOCATION_TASK\", definition=\"CALL LOAD_EXCEL_WORKSHEET_TO_TABLE_SP(BUILD_SCOPED_FILE_URL(@FROSTBYTE_RAW_STAGE, 'intro/location.xlsx'), 'location', 'LOCATION')\", warehouse=warehouse_name)\n        dag_task3 = DAGTask(\"LOAD_DAILY_CITY_METRICS_TASK\", definition=\"CALL LOAD_DAILY_CITY_METRICS_SP()\", warehouse=warehouse_name)\n\n```\n\nGreat, we have the tasks and the dag created. But how do we define the task dependencies? Which tasks should run first?\n\nIf you are familiar with Apache Airflow, you might know how we can use `\u003E\u003E` operator to define task dependency. If not, let's understand the flow.\n\nHere is how we define the order of execution of the tasks in our dag:\n\n```python\ndag_task1 \u003E\u003E dag_task3\ndag_task2 \u003E\u003E dag_task3\n```\n\nThe above definition `dag_task1 \u003E\u003E dag_task3` means that `dag_task3` is dependant on `dag_task1`.\n\nSimilarly, `dag_task2 \u003E\u003E dag_task3` means that `dag_task3` is dependant on `dag_task2`.\n\n### Deploying the DAG\n\nSo far, we have only created the dag and defined the order of execution of tasks within the dag. To enable the dag to run on the defined schedule, we have to `deploy()` it first.\n\n```python\ndag_op.deploy(dag, mode=\"orreplace\")\n```\n\nAfter we deploy the dag, the tasks in the dag will run periodically as per the schedule interval in the dag definition.\n\n### Running a DAG\n\nIn addition to running the dags periodically, suppose if you need to run the dag on demand for debugging or troubleshooting, you can use the `run()` function.\n\n```python\ndag_op.run(dag)\n```\n\nAnd for more details on Tasks see [Introduction to Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro.html).\n\n\n\n### Task Metadata\n\nSnowflake keeps metadata for almost everything you do, and makes that metadata available for you to query (and to create any type of process around). Tasks are no different. Snowflake maintains rich metadata to help you monitor your task runs. Here are a few sample SQL queries you can use to monitor your tasks runs:\n\n```sql\n-- Get a list of tasks\nSHOW TASKS;\n\n-- Task execution history in the past day\nSELECT *\nFROM TABLE(INFORMATION_SCHEMA.TASK_HISTORY(\n    SCHEDULED_TIME_RANGE_START=\u003EDATEADD('DAY',-1,CURRENT_TIMESTAMP()),\n    RESULT_LIMIT =\u003E 100))\nORDER BY SCHEDULED_TIME DESC\n;\n\n-- Scheduled task runs\nSELECT\n    TIMESTAMPDIFF(SECOND, CURRENT_TIMESTAMP, SCHEDULED_TIME) NEXT_RUN,\n    SCHEDULED_TIME,\n    NAME,\n    STATE\nFROM TABLE(INFORMATION_SCHEMA.TASK_HISTORY())\nWHERE STATE = 'SCHEDULED'\nORDER BY COMPLETED_TIME DESC;\n```\n\n### Monitoring Tasks\n\nNow, after your dag is deployed, all the tasks in your dag will be running as per the defined schedule. However, as a Snowflake user, how can you peek into the success and failure of each run, look at the task run time, and more?\n\nSnowflake provides some rich task observability features in the Snowsight UI. Try it out for yourself by following these steps:\n\n- In the Snowsight navigation menu, click **Data** » **Databases**.\n- In the right pane, using the object explorer, navigate to a database and schema.\n- For the selected schema, select and expand **Tasks**.\n- Select a task. Task information is displayed, including **Task Details**, **Graph**, and **Run History** sub-tabs.\n- Select the **Graph** tab. The task graph appears, displaying a hierarchy of child tasks.\n- Select a task to view its details.\n\n![Tasks](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/tasks_and_dags.png)\n\nTo monitor all the tasks at an account level, you can use the Task History tab as well.\n\n- In the Snowsight navigation menu, click **Activity** » **Task History**.\n- In the right pane, select the **Task Runs** tab. You can scroll through all the task runs in the account.\n\n![Task Runs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/task_history_runs.png)\n\nFor more details, and to learn about viewing account level task history, please check out our [Viewing Task History](https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks.html) documentation.\n\n### Query History for Tasks\n\nOne important thing to understand about tasks, is that the queries which get executed by the task won't show up with the default Query History UI settings. In order to see the queries that just ran you need to do the following:\n\n- Remove filters at the top of this table, including your username, as later scheduled tasks will run as \"System\":\n\n![remove filter](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/query_history_remove_filter1.png)\n\n- Click \"Filter\", and add filter option 'Queries executed by user tasks' and click \"Apply Filters\":\n\n![remove filter](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/query_history_remove_filter2.png)\n\nYou should now see all the queries run by your tasks! \n\n\u003C!-- ------------------------ --\u003E\n## Teardown\n\n\nOnce you're finished with the Quickstart and want to clean things up, you can simply run the `steps/08_teardown.sql`. Since this is a SQL script we will be using our native VS Code extension to execute it. So simply open the `steps/08_teardown.sql` script in VS Code and run the whole thing using the \"Execute All Statements\" button in the upper right corner of the editor window.\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusion and Resources\n\n\nCongratulations! You have successfully built a data engineering pipeline using Snowpark Python, loaded data from an external stage using Snowflake Dynamic File Access, used data from Snowflake Marketplace and orchestrated the data pipeline using Snowflake Tasks as well.\n\nWe would love your feedback on this QuickStart Guide! Please submit your feedback using this [Feedback Form](https://forms.gle/M7C8EC3sYKide1ro9).\n\n### What You Learned\n\nHere is the overview of what we built:\n\n- How to ingest data from an external stage such as an S3 bucket into a Snowflake table\n- How to access data from Snowflake Marketplace and use it for your analysis\n- How to analyze data and perform data engineering tasks using Snowpark DataFrame API, Python Stored Procedures and more\n- How to use open-source Python libraries from curated Snowflake Anaconda channel\n- How to create Snowflake Tasks and use the Python Tasks API to schedule data pipelines\n- How to use VS Code extension for Snowflake to perform standard snowflake operations from VS Code and Snowsigt UI\n\n---\n\n![Quickstart Pipeline Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-snowpark-python-intro/data_pipeline_overview.png)\n\n---\n\n### Related Resources\n\n- [Source Code on GitHub](https://github.com/Snowflake-Labs/sfguide-data-engineering-with-snowpark-python-intro)\n- [Advanced: Data Engineering Pipelines with Snowpark Python](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\n- [Advanced: Getting Started with Data Engineering and ML using Snowpark for Python](/en/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python/)\n- [Snowpark for Python Developer Guide](https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html)\n  - [Writing Stored Procedures in Snowpark (Python)](https://docs.snowflake.com/en/sql-reference/stored-procedures-python.html)\n  - [Working with DataFrame in Snowpark Python](https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes.html)\n  - [Snowflake Visual Studio Code Extension](https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-f25746b727","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-e44f2ed287",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-bf4b0a13ac","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-70314fc690","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. It may be out of date with current Snowflake 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