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------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EModern businesses need modern data strategies, built on platforms that support agility, growth and operational efficiency.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://signup.snowflake.com/?utm_source=google&utm_medium=paidsearch&utm_content=go-eta-ld-free-trial&utm_term=c-g-snowflake-e&utm_campaign=na-en-Branded&_bt=470247374327&_bk=snowflake&_bm=e&_bn=g&_bg=64805047909&gclid=Cj0KCQjw18WKBhCUARIsAFiW7JwA-C_HmNQzG_OFKhM1Hn9vlW6EAs-9mveiMXychVbbK34lh4vGfHsaAv4NEALw_wcB&gclsrc=aw.ds\"\u003ESnowflake\u003C/a\u003E is the Data Cloud, a future-proof solution that simplifies data pipelines, so you can focus on data and analytics instead of infrastructure management.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.getdbt.com/\"\u003Edbt\u003C/a\u003E is a transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines. It transforms data in the warehouse, leveraging cloud data platforms like Snowflake.\u003C/p\u003E\n","\u003Cp\u003EIn this Quickstart, you will follow a step-by-step guide to using dbt with Snowflake, and see some of the benefits this tandem brings.\u003C/p\u003E\n","\u003Cp\u003ELet's get started.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Use During the Lab\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA trial \u003Ca href=\"https://trial.snowflake.com/\"\u003ESnowflake account\u003C/a\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA \u003Ca href=\"https://www.getdbt.com/signup/\"\u003Edbt Cloud account\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to build scalable data transformation pipelines using dbt & Snowflake\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to leverage data in Snowflake's Data Marketplace\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA set of data analytics pipelines for Financial Services data leveraging dbt and Snowflake, making use of best practices like data quality tests and code promotion between environments\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EArchitecture and Use Case Overview\u003C/h2\u003E\n","\u003Cp\u003EIn this lab, we are going to analyze the historical trading performance of a company that has trading desks spread across different regions. As inputs, we are going to leverage datasets available in Knoema Economy Data Atlas, which is available in Snowflake Marketplace. We’ll also make use of a few manual uploads.\nTo do this, we are going to set up the environments, build scalable pipelines in dbt, establish data tests , and promote code to production.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/arch_diagram.png\" alt=\"Architecture Overview\"\u003E\u003C/p\u003E\n","\u003Cp\u003EJust to give you a sneak peek, this is the data lineage we will be creating using dbt.\u003C/p\u003E\n","\u003Cp\u003EStay tuned!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/lineage_graph.png\" alt=\"DAG\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Configuration\u003C/h2\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ELogin to your Snowflake trial account.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/snowflake_login.png\" alt=\"Snowflake Log In Screen\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUI Tour (SE will walk through this live). For post-workshop participants, click \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-manager.html#quick-tour-of-the-web-interface\"\u003Ehere\u003C/a\u003E for a quick tour of the UI.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EWe will be using the new UI to getting started but you can also switch over to the Classic Console if you would like. It will not affect your dbt experience but may change where the buttons are in the Snowflake UI.\u003C/p\u003E\n","\u003Cp\u003ENew UI:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/new_snowflake_ui.png\" alt=\"New Snowflake UI\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClassic UI:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/snowflake_worksheets.png\" alt=\"Snowflake Worksheets\"\u003E\u003Cbr\u003E\nIf you ever want to change from the new UI to the classic one, click on the home button and then \u003Ccode\u003EClassic Console\u003C/code\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect to Data Source\u003C/h2\u003E\n","\u003Cp\u003ENow we need to obtain our raw data. We are going to the Snowflake Marketplace to connect to the Knoema dataset.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EWe want to go into the Snowflake Marketplace to connect to the Knoema dataset. Click on Data on the left hand sidebar and then Marketplace.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_marketplace.png\" alt=\"Click on Marketplace\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ENow let’s look for Knoema Economy Atlas Data. Type into the “Search Data Marketplace” search bar “Knoema Economy Atlas Data”.  Find the Knoema Economy Atlas Data tile in the results. Once you find it, click on it.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_knoema_tile.png\" alt=\"Click on Knoema Tile\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EYou should now on a page with a description of the data, sample queries, and other useful information. Let's get this data into our Snowflake account. Click the "Get Data" button.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/get_data.png\" alt=\"Click on Get Data\"\u003E\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EIn the pop-up, enter “KNOEMA_ECONOMY_DATA_ATLAS” as the database name and click “Get Data”. Do not forget to update the name of the database or you will have update some code down the line.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/rename_database.png\" alt=\"Rename Database\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESo what is happening here? Knoema has granted access to this data from their Snowflake account to yours. You're creating a new database in your account for this data to live - but the best part is that no data is going to move between accounts! When you query, you'll really be querying the data that lives in the Knoema account. If they change the data, you'll automatically see those changes. No need to define schemas, move data, or create a data pipeline either.\u003C/p\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003EClick on Query Data.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_data.png\" alt=\"Click on Query Data\"\u003E\u003C/p\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s look at the new data. On the left hand sidebar, you should see KNOEMA_ECONOMY_DATA_ATLAS listed as a database. If not, refresh the UI and check again.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s try to query the data. If you want to switch back to the original interface, you can click on the home button on the top left and then \u003Ccode\u003EClassic Console\u003C/code\u003E on the bottom of the left hand sidebar. This will port you back where you can see the database displayed the way it was before.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe Economy Atlas comes with more than 300 datasets. In order to improve navigation, the data provider has kindly supplied a table called DATASETS. Let's go ahead and find the ones related to the stock history and currency exchange rates that we are going to use in the next step.\u003C/p\u003E\n\u003Col start=\"11\"\u003E\u003Cli\u003ERemove the code in the worksheet and paste in this code.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nselect * \n  from "KNOEMA_ECONOMY_DATA_ATLAS"."ECONOMY"."DATASETS"\n where "DatasetName" ILIKE 'US Stock%'\n    OR "DatasetName" ILIKE 'Exchange%Rates%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_knoema_1.png\" alt=\"Query Knoema\"\u003E\u003C/p\u003E\n\u003Col start=\"12\"\u003E\u003Cli\u003ENow we can see what our datasets names are, let's try to query one of the datasets:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \n  from KNOEMA_ECONOMY_DATA_ATLAS.ECONOMY.USINDSSP2020\n where "Date" = '2020-02-03';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_knoema_2.png\" alt=\"Query Knoema\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You successfully tapped into a live data feed of Trade and FX rates data with NO ETL involved. Isn't this cool? Now let's start building our pipelines.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt Configuration\u003C/h2\u003E\n","\u003Cp\u003ENow it's time to set up dbt. We are going to be using \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ecosystem-partner-connect.html\"\u003ESnowflake Partner Connect\u003C/a\u003E to set up your dbt Cloud account and project. Using this method will allow you to spin up a fully fledged dbt account with your \u003Ca href=\"https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-your-database#connecting-to-snowflake\"\u003ESnowflake connection\u003C/a\u003E, \u003Ca href=\"https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-using-a-managed-repository\"\u003Emanaged repository\u003C/a\u003E, \u003Ca href=\"https://docs.getdbt.com/docs/guides/managing-environments\"\u003Eenvironments\u003C/a\u003E, and credentials in minutes.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Snowflake UI, click on the home icon on the top left side. To access Partner Connect, click on your user and then Partner Connect. Make sure you're still set as the ACCOUNTADMIN role.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_Partner_Connect_1.png\" alt=\"Click on Partner Connect\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you're using the classic console, it will be on the top right.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_Partner_Connect_2.png\" alt=\"Click on Partner Connect\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EFind the dbt Tile by typing into the "Search Partner Connect" search bar. Click on the dbt tile.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou should now see a popup that says \u003Ccode\u003EConnect to dbt\u003C/code\u003E. Click on \u003Ccode\u003EOptional Grant\u003C/code\u003E and you are going to add in the KNOEMA_ECONOMY_DATA_ATLAS database. This will grant access for your new dbt user role to the database.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Ccode\u003EConnect\u003C/code\u003E. This will create a dedicated dbt user, database, warehouse, and role for your dbt Cloud trial.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_dbt_Connect.png\" alt=\"Click on Connect\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen the popup that says "Your partner account has been created", click on activate.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_dbt_connect_activate.png\" alt=\"Click on Partner Activate\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou should be redirected to a dbt Cloud registration page. Fill out the form.\nMake sure to save the password somewhere for login in the future.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/register_dbt_cloud_account.png\" alt=\"Register dbt Cloud account\"\u003E\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EClick on Compete Registration. You should now be redirected to your dbt Cloud account, complete with a connection to your Snowflake account, a deployment and a development environment, and even a sample job.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ETo help you version control your dbt project, we have connected it to a \u003Ca href=\"https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-using-a-managed-repository\"\u003Emanaged repository\u003C/a\u003E, which means that dbt Labs will be hosting your repository for you. This will give you access to a git workflow without you having to create and host the repository yourself. You will not need to know git for this workshop; dbt Cloud will help guide you through the workflow. In the future, when you’re developing your own project, feel free to use \u003Ca href=\"https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-installing-the-github-application\"\u003Eyour own repository\u003C/a\u003E. This will allow you to play with features like \u003Ca href=\"https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration-with-github\"\u003ESlim CI\u003C/a\u003E builds after this workshop.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt Project Configuration\u003C/h2\u003E\n\u003Col\u003E\u003Cli\u003ENow let’s set up our dbt project. Click on the hamburger menu on the top left side and click on \u003Ccode\u003EDevelop\u003C/code\u003E.  This will spin up your IDE (Integrated Development Environment) where you will be developing your dbt Project.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/hamburger.png\" alt=\"Click on the hamburger\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/develop.png\" alt=\"Click on the develop\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EAfter the IDE loads, click \u003Ccode\u003EInitialize your project\u003C/code\u003E to set up your dbt project. Once you click on it, dbt will generate our starter project with the core files and folders.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/initialize.png\" alt=\"Click on the Initialize\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou should see new files and folders created in your file tree. These are all the files and folders you will need for a dbt project. Take a look around, especially at the \u003Ca href=\"https://docs.getdbt.com/reference/dbt_project.yml\"\u003Edbt_project.yml\u003C/a\u003E and what’s in the \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models\"\u003Emodels\u003C/a\u003E directory.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ECommit\u003C/code\u003E. Enter a commit message, and then click \u003Ccode\u003ECommit\u003C/code\u003E again to commit your work to your master branch.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/commit.png\" alt=\"Click on the commit\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/commit_message.png\" alt=\"commit message\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECommit messages should always be indicative of the work you are saving. This helps you create a reference point for the future in case of auditing and debugging.  By \u003Ca href=\"https://www.atlassian.com/git/tutorials/saving-changes/git-commit#:~:text=The%20git%20commit%20command%20captures,you%20explicitly%20ask%20it%20to.\"\u003Ecommitting\u003C/a\u003E, you are saving to a remote branch in Github. This will also be the only time you will save straight to your master branch (which is the main branch). We always want to create a degree of separation from development work and your production branch.\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003Ecreate new branch\u003C/code\u003E to check out a new git branch to start developing.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_branch.png\" alt=\"create branch\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EName the branch “dbt_snowflake_workshop” and click \u003Ccode\u003ESubmit\u003C/code\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/name_branch.png\" alt=\"name branch\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUI walkthrough (SA will walk through during the workshop). Post workshop, users can look at \u003Ca href=\"https://getdbt.wistia.com/medias/0828jynq3v\"\u003Ethis video for the walkthrough\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s validate that everything was initialized correctly by running the sample models that come with the starter dbt project.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EType in \u003Ccode\u003Edbt run\u003C/code\u003E on the command line at the bottom, and click \u003Ccode\u003EEnter\u003C/code\u003E.\nThe command line is where you will be entering in dbt commands to execute dbt actions.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIf you want to see the actual code being executed, you can go into the ‘Details’ tab next to \u003Ccode\u003ESummary\u003C/code\u003E and look through the logs. Here you can see that dbt is writing the DDL for you, allowing you to focus on just writing the SQL select statement.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/dbt_run.png\" alt=\"dbt run\"\u003E\u003C/p\u003E\n\u003Col start=\"11\"\u003E\u003Cli\u003EThe output should look like below, confirming dbt was able to connect to Snowflake and successfully execute the sample models in the models folder:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/successful_run_1.png\" alt=\"successful run\"\u003E\u003C/p\u003E\n\u003Col start=\"12\"\u003E\u003Cli\u003EIf you want to see what is executed against Snowflake, click into one of the model tabs and click on details. You will see the log of the compiled code and how dbt interacted with Snowflake. Note that dbt by default built all of your models into this development schema made up of your first initial and last name. We will cover more environments later on.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/successful_run_2.png\" alt=\"successful run\"\u003E\u003C/p\u003E\n\u003Col start=\"13\"\u003E\u003Cli\u003ENow let’s visually confirm the objects in Snowflake. Switch to the Snowflake UI (classic console) and refresh database objects. Expand the database \u003Ccode\u003EPC_DBT_WH\u003C/code\u003E. Then the development schema with your first initial and last name. And lastly Tables and Views. You should see the table \u003Ccode\u003EMY_FIRST_DBT_MODEL\u003C/code\u003E  and the view \u003Ccode\u003EMY_SECOND_DBT_MODEL\u003C/code\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/successful_run_3.png\" alt=\"successful run\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You just ran your first dbt models on Snowflake!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003EWe are going to start by adding a few more things to our dbt project configuration in order to improve maintainability.\u003C/p\u003E\n\u003Col start=\"14\"\u003E\u003Cli\u003ELet’s add in some folders to follow our dimensional modeling structure. To learn more about how we structure our project, \u003Ca href=\"https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355\"\u003Echeck out this discourse post\u003C/a\u003E.  To add new folders, ghost over the models directory in the file tree and click on the ‘…’ on the right next to the models directory. Click on “New Folder” and type into it “staging”. Click “Create” after you finish typing and you should see a new folder in the file tree.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_1.png\" alt=\"create folder\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_2.png\" alt=\"create folder\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_3.png\" alt=\"create folder\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_4.png\" alt=\"create folder\"\u003E\u003C/p\u003E\n\u003Col start=\"15\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow that you’ve gotten one completed, let’s go ahead and create three more folders. First create a folder named “knoema” in the staging model by clicking on the ‘…’ next to the staging folder. This will be where we will put our staging models specific to our knoema source.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow for our last folders, we are actually going to create two folders at once. Click on the ‘...’ next to models again. This time, we are going to fill the file path as “models/marts/core”. This will create a new folder under models called marts and in it, a folder called core.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou should have a file tree that looks like this after completing this task.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_5.png\" alt=\"create folder\"\u003E\u003C/p\u003E\n\u003Col start=\"17\"\u003E\u003Cli\u003ENext, let’s update the \u003Ca href=\"https://docs.getdbt.com/reference/dbt_project.yml\"\u003Edbt_project.yml file\u003C/a\u003E. This is a file that tells dbt that the repository is a dbt project as well as how to operate on your project. Click on the file \u003Ccode\u003Edbt_project.yml\u003C/code\u003E. The first thing we are going to do is update the project name from “my_new_project” to “<your_name>_dbt_workshop” at line 5. This will personalize the project a little, and make it yours.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EDo not forget to save after you have updated the file! This means you will want to either click save or use cmd+s (mac)/ctrl+s(pc). The IDE will also warn you with the teal button on the tab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/update_project.png\" alt=\"update project\"\u003E\u003C/p\u003E\n\u003Col start=\"18\"\u003E\u003Cli\u003ENow let’s update the models section in the file by declaring our new folders and their model configurations. We will be declaring the default \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas\"\u003Eschema\u003C/a\u003E that dbt builds the object into, the default \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations\"\u003Ematerialization\u003C/a\u003E, and disabling any unnecessary models from a run. All of these configurations can be overridden by model configurations on the model file. If a schema is not declared via a configuration, dbt will build into the default schema declared on our development credentials.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ECopy code below and paste into line 34 to line 38. Remember to replace <your_name>.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Emodels:\n  <your_name>_dbt_workshop:\n      example:\n          schema: example \n      staging:\n          schema: staging\n          materialized: view\n      marts:\n          schema: marts\n          materialized: table\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"19\"\u003E\u003Cli\u003E\n","\u003Cp\u003ESave the file.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EBe sure to click on “Commit” using the git command button (just in case your computer spontaneously explodes into a blob of smoke and melted plastic) to save your work. And supply a good commit message that conveys what you are saving!\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETo better understand how schema names work in dbt, let's take a look at the standard \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/jinja-macros\"\u003Emacro\u003C/a\u003E that \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas\"\u003Edbt uses to define what schema to build your object into\u003C/a\u003E. By default, dbt is generating a schema name by appending it to the target schema environment name(dev, prod). If you ever want to override this, you are going to create a new file in the macros folder named \u003Ccode\u003Egenerate_schema_name.sql\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/schema_macro_1.png\" alt=\"schema macro\"\u003E\u003C/p\u003E\n\u003Col start=\"22\"\u003E\u003Cli\u003ECopy and paste the below code into file generate_schema_name.sql to generate the macro.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{% macro generate_schema_name(custom_schema_name, node) -%}\n\n    {%- set default_schema = target.schema -%}\n    {%- if custom_schema_name is none -%}\n\n        {{ default_schema }}\n\n    {%- else -%}\n\n        {{ default_schema }}_{{ custom_schema_name | trim }}\n\n    {%- endif -%}\n\n{%- endmacro %}\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/schema_macro_2.png\" alt=\"schema macro\"\u003E\u003C/p\u003E\n\u003Col start=\"23\"\u003E\u003Cli\u003E\n","\u003Cp\u003EClick Save\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETo see this macro in action, do another \u003Ccode\u003Edbt run\u003C/code\u003E on the command line. Note how the schema has changed to be appending the custom schema name to our default schema per our macro. You can add in more \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas#jinja-context-available-in-generate_schema_name\"\u003Econditionals\u003C/a\u003E to fit your own needs in terms of environment promotions.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIn this workshop, we will show you how different configurations can easily change how and where your dbt project reads and writes from without needing you to change your code with code promotion.\u003C/p\u003E\n","\u003Cp\u003EMacros are a way to do this. Macros are written in a pythonic templating language called \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/jinja-macros\"\u003EJinja\u003C/a\u003E. Jinja allows you to do things that normally aren’t possible in SQL such as create control structures (like if statements and for loops) or abstract away snippets of SQL into reusable functions applicable throughout your project. Jinja helps you write \u003Ca href=\"https://en.wikipedia.org/wiki/Don%27t_repeat_yourself\"\u003EDRY\u003C/a\u003E code in a language that is generally wet.\u003C/p\u003E\n","\u003Cp\u003EIn this generate_schema_name macro, we are defining what schema your objects should be built into based on the conditionals specified.\nGoing through the macro, what is happening here:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E   {%- set default_schema = target.schema -%}\n    {%- if custom_schema_name is none -%}\n \n        {{ default_schema }}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIs that we are defining our default schema as the target database and schema defined for your development environment. The Partner Connect flow sets up our development environment to point to the partner connect created database and a schema made of your first initial and last name. You can verify this in your \u003Ca href=\"https://cloud.getdbt.com/#/profile\"\u003Eprofile\u003C/a\u003E.\nHowever, if a custom schema is provided as we did in our dbt_project.yml file, dbt will build into a schema that is a concatenation of our default schema and the custom schema name due to this logic:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\n{%- else -%}\n\n    {{ default_schema }}_{{ custom_schema_name | trim }}\n\n{%- endif -%}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAll of this logic is defined in one place and will be applied on your project which makes it simple to scale up your project, especially as business needs arise.\u003C/p\u003E\n","\u003Cp\u003ELater on, we will learn how to use macros from a package and see how those macros help us write SQL quickly.\u003C/p\u003E\n\u003Col start=\"25\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow we need to apply another operational macro. This time, this macro will add a \u003Ca href=\"https://docs.getdbt.com/reference/resource-configs/snowflake-configs#query-tags\"\u003Equery tag\u003C/a\u003E for every dbt run in the Snowflake history.  To do this, we’ll create a file in the “macros” folder called \u003Ccode\u003Equery_tag.sql\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy and paste the following code. This provides the ability to add an additional level of transparency by automatically setting Snowflake query_tag to the name of the model it is associated with.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{% macro set_query_tag() -%}\n  {% set new_query_tag = model.name %} {# always use model name #}\n  {% if new_query_tag %}\n    {% set original_query_tag = get_current_query_tag() %}\n    {{ log("Setting query_tag to '" ~ new_query_tag ~ "'. Will reset to '" ~ original_query_tag ~ "' after materialization.") }}\n    {% do run_query("alter session set query_tag = '{}'".format(new_query_tag)) %}\n    {{ return(original_query_tag)}}\n  {% endif %}\n  {{ return(none)}}\n{% endmacro %}\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"27\"\u003E\u003Cli\u003EClick “Save”.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_tag.png\" alt=\"query tags\"\u003E\u003C/p\u003E\n\u003Col start=\"28\"\u003E\u003Cli\u003ENow, do a \u003Ccode\u003Edbt run\u003C/code\u003E again on the command line.\u003C/li\u003E\u003Cli\u003ETo see where the query tags are applied, go to the Snowflake UI, click the “History” icon on top. You are going to see all the SQL queries you’ve run on your Snowflake account (successful, failed, running etc.). and clearly see which dbt model a  particular query is related to.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_tag_2.png\" alt=\"query tags\"\u003E\u003C/p\u003E\n","\u003Cp\u003EUpdate the Filter to the user is PC_DBT_USER and the status is Succeeded\nto see the same view as the screenshot above, you can also remove extra columns by clicking on the arrow after hovering over a column, hovering over “Columns” and unchecking any columns you don’t want.\u003C/p\u003E\n\u003Col start=\"30\"\u003E\u003Cli\u003ENow we are going to install a dbt package. A dbt \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/package-management\"\u003Epackage\u003C/a\u003E is essentially a dbt project that you can install onto your own dbt project to gain access to the code and use it as your own. Many of our packages are hosted on the \u003Ca href=\"https://hub.getdbt.com/\"\u003Edbt Packages Hub\u003C/a\u003E. In our lab, we are going to demonstrate how to use some useful macros in the dbt_utils package to write some complex SQL. To install it, create a file called \u003Ccode\u003Epackages.yml\u003C/code\u003E at the same level as your \u003Ccode\u003Edbt_project.yml\u003C/code\u003E file.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/packages_1.png\" alt=\"packages\"\u003E\u003C/p\u003E\n\u003Col start=\"31\"\u003E\u003Cli\u003ECopy and paste the following code into file packages.yml.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Epackages:\n  - package: dbt-labs/dbt_utils\n    version: 0.8.0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/packages_2.png\" alt=\"packages\"\u003E\u003C/p\u003E\n\u003Col start=\"32\"\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ESave\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow we are going to install the package. By running \u003Ccode\u003Edbt deps\u003C/code\u003E, you tell dbt to install the packages. With a successful run, you can check out the dbt_modules folder to see what packages you have installed and the code that is now available to you.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/packages_3.png\" alt=\"packages\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELast thing to do before we save our work. Remove the example subdirectory configuration in the \u003Ccode\u003Edbt_project.yml\u003C/code\u003E file and delete the example folder with the models in it.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ccode\u003ECommit\u003C/code\u003E to commit your work!\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuilding dbt Data Pipelines\u003C/h2\u003E\n","\u003Cp\u003ENow we start to get into the fun stuff. In the next few sections, we are going to build our dbt pipelines. This will include transformations that define these these areas of interest:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStock trading history\u003C/li\u003E\u003Cli\u003ECurrency exchange rates\u003C/li\u003E\u003Cli\u003ETrading books\u003C/li\u003E\u003Cli\u003EProfit & Loss calculation\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003Edbt pipelines - Sources & Staging\u003C/h2\u003E\n","\u003Ch4\u003ESetting up our Sources\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s head back to the dbt Cloud IDE. We are going to start building our pipelines by declaring our \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/using-sources\"\u003Edbt sources\u003C/a\u003E.  Create a \u003Ccode\u003Eknoema_sources.yml\u003C/code\u003E file in the staging/knoema folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste in the following code.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Eversion: 2\n\nsources:\n  - name: knoema_economy_data_atlas\n    database: knoema_economy_data_atlas\n    schema: economy\n    tables:\n      - name: exratescc2018\n      - name: usindssp2020\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/sources_1.png\" alt=\"sources\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs you might remember, these two objects were mentioned in the Knoema Dataset Catalog table: daily exchange rates and daily US trading history accordingly.\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EClick \u003Ccode\u003ECommit\u003C/code\u003E and provide a good commit message like “set up knoema source file”. We are going to keep building that muscle of saving your work as you develop.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENow we have defined the sources that we will be building our dbt models on top of. By declaring these, we are able to create lineage from our raw objects to our transformed objects and apply documentation and testing.\u003C/p\u003E\n","\u003Ch4\u003ESetting up our Staging Models\u003C/h4\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s create staging models to clean up our raw objects. Staging models bear a one-to-one relationship with the source data table they represent. They have the same granularity, but the columns have been renamed, recast, or reconsidered in some way to follow a consistent, useful format. By creating these as the first level transformation, you can create a consistent foundation to build more complex transformations upon. To learn more, check out our \u003Ca href=\"https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355#data-transformation-101-1\"\u003Ediscourse post\u003C/a\u003E.\nThe first staging model we are going to make will be for exratescc2018. Create a new file in the staging/knoema folder named \u003Ccode\u003Estg_knoema_fx_rates.sql\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste the following select statement.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E(To learn more about our recommendations around coding style, \u003Ca href=\"https://github.com/dbt-labs/corp/blob/master/dbt_style_guide.md#ctes\"\u003Echeck out this repository.\u003C/a\u003E)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith source as (\n \n    select * from {{source('knoema_economy_data_atlas','exratescc2018') }}\n \n), \n \nrenamed as (\n \nselect \n \n    "Currency" as currency,\n    "Currency Unit" as currency_unit,\n    "Frequency" as frequency,\n    "Date" as exchange_date,\n    "Value" as exchange_value,\n    "Indicator" as indicator,\n    "Indicator Name" as indicator_name,\n    'Knoema.FX Rates' as data_source_name\n \nfrom source \n \n) \n \nselect * from renamed\n \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ESave the file. To check the query before executing the code against the warehouse, you can click on the \u003Ccode\u003EPreview\u003C/code\u003E button. You can also click on \u003Ccode\u003ECompile\u003C/code\u003E to see the compiled code that will be executed.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_1.png\" alt=\"staging\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the compiled code, you will notice that the source function\n({{source('knoema_economy_data_atlas','exratescc2018')}})  compiles to the actual database object.\u003C/p\u003E\n","\u003Cp\u003ELet’s talk more about this function:\u003C/p\u003E\n","\u003Cp\u003EUsing the sources function allows you to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETell dbt how a model relates to its source and then dynamically generate a DAG from this, as you can see in the lineage tab\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_2.png\" alt=\"staging\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThis also allows dbt to dynamically replace the sources with their \u003Ca href=\"https://docs.getdbt.com/reference/dbt-jinja-functions/target\"\u003Etarget-specific name\u003C/a\u003E, whether it's from the configuration in sources.yml or from the environment. This allows you to promote the same code from dev to prod and any other environments without having to change the code. You can even use the \u003Ca href=\"https://docs.getdbt.com/reference/dbt-jinja-functions/target#use-targetname-to-change-your-source-database\"\u003Etarget jinja function\u003C/a\u003E to conditionally point to different databases based on the declared target.\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow to make the other staging model. Create the file in the same folder and name it \u003Ccode\u003Estg_knoema_stock_history.sql\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste in this code and save.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith source as (\n\n    select * from {{source('knoema_economy_data_atlas','usindssp2020')}}\n), \n\nrenamed as (\n\n    select \n\n        "Company" as company,\n        "Company Name" as company_name,\n        "Company Ticker" as company_symbol,\n        "Stock Exchange" as stock_exchange,\n        "Stock Exchange Name" as stock_exchange_name,\n        "Indicator" as indicator,\n        "Indicator Name" as indicator_name,\n        "Units" as units,\n        "Scale" as scale, \n        "Frequency" as frequency, \n        "Date" as stock_date,\n        "Value" as stock_value,\n        'Knoema.Stock History' as data_source_name \n\n    from source \n\n) \n\nselect * from renamed\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003ENow let’s create these views in Snowflake. Since we only want to run the models in the staging folder, we are going to use the following command on the command line:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt run --select staging.*\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EThis allows us to be selective and only run the models in the staging folder. \u003Ca href=\"https://docs.getdbt.com/reference/node-selection/syntax\"\u003ENode selectors\u003C/a\u003E are very helpful in refining what we actually want to run and test.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_3.png\" alt=\"staging\"\u003E\u003C/p\u003E\n\u003Col start=\"10\"\u003E\u003Cli\u003EOnce everything ran successfully, let’s save our work to our remote branch. Click on \u003Ccode\u003ECommit\u003C/code\u003E and provide a good commit message like “set up staging models”\u003C/li\u003E\u003Cli\u003ENow we can go and query this dataset to take a feel of what the data profile looks like. You can either put this query in the “Statement 1” tab or in Snowflake UI. Remember to change the schema name to the correct one for you.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \n  from pc_dbt_db.<dev_schema>_staging.stg_knoema_stock_history\n where company_symbol ='AAPL' \n   and stock_date ='2021-03-01'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_4.png\" alt=\"staging\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this dataset, different measures like Close, Open, High and Low price are represented as different rows. For our use case, this is a bit suboptimal - it would be better to see that data transposed into columns; something more like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_5.png\" alt=\"staging\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESo let’s do that!\u003C/p\u003E\n","\u003Ch2\u003Edbt pipelines - Intermediate\u003C/h2\u003E\n","\u003Ch3\u003ESetting up our intermediate models\u003C/h3\u003E\n","\u003Cp\u003ETo achieve this, we are going to have to create a few more models:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENow let’s create a new intermediate model in the core folder. You can do this by clicking on \u003Ccode\u003ENew File\u003C/code\u003E. Remove the prewritten path and paste in the following path:\nmodels/marts/core/intermediate/int_knoema_stock_history.sql\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_1.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will create the file as well as the intermediate folder in the core folder to organize the intermediate models.\u003C/p\u003E\n","\u003Cp\u003EIntermediate tables are transformations to get to the end data product but won’t be queried in the downstream processes like in the BI layer.\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste this code in the model and click “Save”. We are going to use the dbt_utils pivot macro  to transpose the dataset from rows to columns. We have also nested in another dbt_utils macro called get_columns to help us list the column values dynamically.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste this code in the model and save. We are going to use the \u003Ca href=\"https://github.com/dbt-labs/dbt-utils/tree/0.7.1/#pivot-source\"\u003Edbt_utils pivot macro\u003C/a\u003E to transpose the dataset from rows to columns. We have also nested in another dbt_utils macro called \u003Ca href=\"https://github.com/dbt-labs/dbt-utils/tree/0.7.1/#get_column_values-source\"\u003Eget_columns\u003C/a\u003E to help us list the column values dynamically.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nwith stock_history as (\n\n    select * from {{ ref('stg_knoema_stock_history') }}\n        where indicator_name in ('Close', 'Open','High','Low', 'Volume', 'Change %') \n\n),\n\npivoted as (\n\n    select \n        company_symbol, \n        company_name, \n        stock_exchange_name, \n        stock_date, \n        data_source_name,\n        {{ dbt_utils.pivot(\n      column = 'indicator_name',\n      values = dbt_utils.get_column_values(ref('stg_knoema_stock_history'), 'indicator_name'),\n      then_value = 'stock_value'\n            ) }}\n    \n    from stock_history\n    group by company_symbol, company_name, stock_exchange_name, stock_date, data_source_name\n\n)\n\nselect * from pivoted\n \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s see what dbt_utils pivot macro compiles to by clicking on the compile button.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_2.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s see what happens if you don’t have the dbt_utils macro to help you write the code. Create a new file in the same intermediate folder: \u003Ccode\u003Eint_knoema_stock_history_alt.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste below content into the new file and click “Save”. In this model, we will use Snowflake's \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/pivot.html\"\u003Epivot\u003C/a\u003E function.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith stock_history as (\n \n    select * from {{ ref('stg_knoema_stock_history') }}\n        where indicator_name IN ('Close', 'Open','High','Low', 'Volume', 'Change %') \n \n),\n \npivoted as (\n \n    select * from stock_history \n        pivot(sum(stock_value) for indicator_name in ('Close', 'Open','High','Low', 'Volume', 'Change %')\n            ) as \n            p(\n                company_symbol, \n                company_name, \n                stock_exchange_name, \n                stock_date, \n                data_source_name, \n                close,\n                open,\n                high,\n                low,\n                volume,change\n            ) \n)\n \nselect * from pivoted\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhat did you think of the difference? While both models create the same end result, the \u003Ca href=\"https://github.com/dbt-labs/dbt-utils#pivot-source\"\u003Edbt_utils.pivot macro\u003C/a\u003E requires less code and less brainpower. All you had to do is fill in the columns and the model to obtain the same end result. It is also more dynamic, automatically updating at compilation if a new value appears.\u003C/p\u003E\n","\u003Cp\u003EYou might have also noticed that in these models we are using a function called \u003Ca href=\"https://docs.getdbt.com/reference/dbt-jinja-functions/ref\"\u003Eref\u003C/a\u003E. This ref function is very similar to sources function but rather than telling dbt how a model relates to a source declared in the sources.yml file, the ref function tells dbt how a model relates to another model. It's useful in the same reasons as sources():\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYou will be able to automatically generate a DAG and promote your code through environments without having to update the code to change the database object.\u003C/li\u003E\u003Cli\u003ERefs also allow you to run based on dependencies as you will see below.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe main takeaway to remember is in a dbt project, you should never have to hardcode a database object. Always uses \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/using-sources\"\u003Esources()\u003C/a\u003E and \u003Ca href=\"https://docs.getdbt.com/reference/dbt-jinja-functions/ref\"\u003Erefs()\u003C/a\u003E\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003ENow let’s go ahead and create the new models in Snowflake. In this case, rather than running based on the model file, we will run based on dependencies. Run the following command to execute the int_knoema_stock_history model and its parent models.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt run --select +int_knoema_stock_history\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should see that we have run all of the nodes (excluding sources) to the left of this lineage.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_3.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_4.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003EExecute the below code, either in the Snowflake UI or in the dbt Cloud IDE\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n  FROM pc_dbt_db.<dev_schema>_marts.int_knoema_stock_history\n WHERE company_symbol = 'AAPL'\n   AND stock_date = '2021-03-01'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELooks like we have what we wanted!\u003C/p\u003E\n","\u003Cp\u003ENow let’s take a look at our exchange rates and create some intermediate tables on top of them.\u003C/p\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003ECreate a file called \u003Ccode\u003Eint_fx_rates.sql\u003C/code\u003E in the intermediate folder (models/marts/core/intermediate/int_fx_rates.sql).\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{{ \nconfig(\n    materialized='view', \n      tags=["hourly"]\n    ) \n}}\n \nselect * from {{ ref('stg_knoema_fx_rates') }} \n where indicator_name = 'Close' \n   and frequency = 'D' \n   and exchange_date > '2016-01-01'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThere's now a model configuration on top of our model. dbt offers \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations\"\u003Evarious materialization options\u003C/a\u003E. By default, if a materialization is not declared, the model will be created as a view. For this directory (marts) that the model is nested in, we have configured in our \u003Ccode\u003Edbt_project.yml\u003C/code\u003E that the default materialization is \u003Ccode\u003Etable\u003C/code\u003E. We will override the project level materialization with our model configuration, declaring the model to be materialized as a view.\nWe also include a \u003Ca href=\"https://docs.getdbt.com/reference/resource-configs/tags\"\u003Etag\u003C/a\u003E. Tags can be used to run parts of your project or a good way to group your models based on content and intent.\u003C/p\u003E\n\u003Col start=\"10\"\u003E\u003Cli\u003ELet’s try running this model based on a tag. In the command line, run :\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt run --select tag:hourly\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EBeing able to run models based on tags can be helpful for, say, an hourly job where you are selectively running specific models.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_5.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n\u003Col start=\"11\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow create the file \u003Ccode\u003Eint_stock_history_major_currency.sql\u003C/code\u003E in the intermediate folder. This model will start bringing FX and Trade history sets together.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste the below code into the file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith\nstock_history as (\n    select * from {{ ref('int_knoema_stock_history')}}\n),\n \nfx_rates as (\n    select * from {{ ref('int_fx_rates') }}\n),\n \nfx_rates_gdp as (\n    select * from fx_rates\n        where currency = 'USD/GBP'   \n),\n \nfx_rates_eur as (\n    select * from fx_rates\n        where currency = 'USD/EUR' \n),\n \njoined as (\n    select \n        stock_history.*,\n        fx_rates_gdp.exchange_value * stock_history."Open" as gbp_open,       \n        fx_rates_gdp.exchange_value * stock_history."High" as gbp_high,     \n        fx_rates_gdp.exchange_value * stock_history."Low" as gbp_low,   \n        fx_rates_gdp.exchange_value * stock_history."Close" as gbp_close,     \n        fx_rates_eur.exchange_value * stock_history."Open" as eur_open,       \n        fx_rates_eur.exchange_value * stock_history."High" as eur_high,     \n        fx_rates_eur.exchange_value *stock_history."Low" as eur_low,\n        fx_rates_eur.exchange_value * stock_history."Close" as eur_close    \n    from stock_history\n    left join fx_rates_gdp on stock_history.stock_date = fx_rates_gdp.exchange_date\n    left join fx_rates_eur on stock_history.stock_date = fx_rates_eur.exchange_date\n)\n \nselect * from joined\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"13\"\u003E\u003Cli\u003ENow, let's deploy newly built models by typing the following into the command line.\n\u003Ccode\u003Edbt run --select +int_stock_history_major_currency\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_6.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EGenerate Documentation Site\u003C/h4\u003E\n","\u003Cp\u003ENow that we have created a few models, let’s talk about \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/documentation\"\u003Edbt documentation\u003C/a\u003E.\ndbt is able to generate a static webpage with a data dictionary by pulling in information from your dbt project as well as your Snowflake information_schema. It also provides an interactive DAG so you can see the full lineage of your models; this is something you might have also noticed  in the lineage tab of the IDE. This is a fantastic way to share  information with your internal teams, as it contains all-important information about columns, tags, free-form model description, tests as well as the source code that is always in line with the code. So regardless of how big your project grows, it is super easy to understand what's happening with the help of dbt’s documentation.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ETo generate your documentation site, execute “dbt docs generate” on the command line:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt docs generate\u003C/code\u003E\u003C/p\u003E\n\u003Col start=\"14\"\u003E\u003Cli\u003EClick on \u003Ccode\u003EView Docs\u003C/code\u003E on the top left side.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_7.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n\u003Col start=\"15\"\u003E\u003Cli\u003ETake a look around on the docs site. You can check out the project lineage by clicking on the DAG button on the bottom. Explore the page and think about what information you would want to provide to your stakeholders and teammates.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_8.png\" alt=\"intermediate\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Seeds\u003C/h2\u003E\n","\u003Cp\u003EFollowing our use case story, we are going to manually upload two small datasets using \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/seeds\"\u003Edbt seed\u003C/a\u003E representing trading books of two desks. As you might notice, they were buying and selling AAPL shares, but logging the cash paid/received in different currencies: USD and GBP.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate a new file in the data folder called \u003Ccode\u003Emanual_book1.csv\u003C/code\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/seed_1.png\" alt=\"seed\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EPaste the following code in the file and save it.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003EBook,Date,Trader,Instrument,Action,Cost,Currency,Volume,Cost_Per_Share,Stock_exchange_name\nB2020SW1,2021-03-03,Jeff A.,AAPL,BUY,-17420,GBP,200,87.1,NASDAQ\nB2020SW1,2021-03-03,Jeff A.,AAPL,BUY,-320050,GBP,3700,86.5,NASDAQ\nB2020SW1,2021-01-26,Jeff A.,AAPL,SELL,52500,GBP,-500,105,NASDAQ\nB2020SW1,2021-01-22,Jeff A.,AAPL,BUY,-100940,GBP,980,103,NASDAQ\nB2020SW1,2021-01-22,Nick Z.,AAPL,SELL,5150,GBP,-50,103,NASDAQ\nB2020SW1,2019-08-31,Nick Z.,AAPL,BUY,-9800,GBP,100,98,NASDAQ\nB2020SW1,2019-08-31,Nick Z.,AAPL,BUY,-1000,GBP,50,103,NASDAQ\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate another file \u003Ccode\u003Emanual_book2.csv\u003C/code\u003E in the data folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste the following code in the file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003EBook,Date,Trader,Instrument,Action,Cost,Currency,Volume,Cost_Per_Share,Stock_exchange_name\nB-EM1,2021-03-03,Tina M.,AAPL,BUY,-17420,EUR,200,87.1,NASDAQ\nB-EM1,2021-03-03,Tina M.,AAPL,BUY,-320050,EUR,3700,86.5,NASDAQ\nB-EM1,2021-01-22,Tina M.,AAPL,BUY,-100940,EUR,980,103,NASDAQ\nB-EM1,2021-01-22,Tina M.,AAPL,BUY,-100940,EUR,980,103,NASDAQ\nB-EM1,2019-08-31,Tina M.,AAPL,BUY,-9800,EUR,100,98,NASDAQ\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ENow let's run the \u003Ccode\u003Edbt seed\u003C/code\u003E command to load the data into Snowflake. It is important to mention that while it is absolutely possible with this approach to bring low hundred-thousands of rows, it was not created for larger datasets and you should be using COPY/Snowpipe or other data integration options recommended for Snowflake in such cases.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt seed\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can see that it loaded the files in the default schema because we didn’t declare a schema for them. If we wanted to make sure the files were loaded into a specific file, we could go back to the dbt_project.yml and add that \u003Ca href=\"https://docs.getdbt.com/reference/seed-configs\"\u003Eschema configuration\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/seed_2.png\" alt=\"seed\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003E\n","\u003Cp\u003ECommit your work.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s model the seeded data by unioning them together. In this example we are going to see how \u003Ca href=\"https://github.com/dbt-labs/dbt-utils#union_relations-source\"\u003Edbt_utils.union_relations\u003C/a\u003E macro helps us write the necessary code. Create the file \u003Ccode\u003Eint_unioned_book.sql\u003C/code\u003E in the intermediate folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste the code below into the new file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith \nunioned as (\n    {{ dbt_utils.union_relations(\n        relations=[ref('manual_book1'), ref('manual_book2')]\n    ) }}\n \n),\n \nrenamed as (\n    select      \n        Book,\n        Date as book_date,\n        Trader,\n        Instrument,\n        Action as book_action,\n        Cost,\n        Currency,\n        Volume,\n        Cost_Per_Share,\n        Stock_exchange_name\n    from unioned \n)\n \nselect * from renamed\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003EBefore we deploy this model, let's have a look at what it is compiled into. You can do this by clicking on the compile button.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/seed_3.png\" alt=\"seed\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs you can see,  the macro from the dbt_utils package wrote the code, aligned the attributes by name and type, and combined the datasets via a UNION ALL. The beauty of this is, we were able to write 46 lines of code with only 3.  It’s also dynamic so if we were to add new columns to our referenced models, dbt would update the compile code with the new fields. You can imagine the amount of time saved.\u003C/p\u003E\n\u003Col start=\"10\"\u003E\u003Cli\u003ENow let’s create the object by running:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt run --select int_unioned_book\u003C/code\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Intermediate Part 2\u003C/h2\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENext challenge! We have a great log of trading activities, but it only provides records when shares were bought or sold. Ideally, to make the daily performance analysis more meaningful, we should have rows for the days shares were held as well. To do this, let’s make this new file: \u003Ccode\u003Eint_daily_position.sql\u003C/code\u003E in the intermediate folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste below into the new file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith \nstock_history as (\n    select * from {{ ref('int_stock_history_major_currency') }} \n), \n\nunioned_book as (\n    select * from {{ ref('int_unioned_book') }}\n),\n\ncst_market_days as (\n    select distinct stock_date\n        from stock_history\n        where stock_history.stock_date >= (select min(book_date) as min_dt from  unioned_book)\n),\n\njoined as (\n    select \n        cst_market_days.stock_date,\n        unioned_book.trader,\n        unioned_book.stock_exchange_name,\n        unioned_book.instrument,\n        unioned_book.book,\n        unioned_book.currency,\n        sum(unioned_book.volume) as total_shares\n    from cst_market_days\n    inner join unioned_book on unioned_book.book_date = cst_market_days.stock_date\n    where unioned_book.book_date <= cst_market_days.stock_date\n    {{ dbt_utils.group_by(6) }}\n)\n\nselect * from joined\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou might have noticed we used another dbt_utils macro. Rather than having to manually list out all of the columns to group by, you can use the \u003Ca href=\"https://github.com/dbt-labs/dbt-utils#group_by-source\"\u003Edbt_utils group_by macro\u003C/a\u003E to write it for you.\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s group the daily position with trades. Create the file \u003Ccode\u003Eint_daily_position_with_trades.sql\u003C/code\u003E in the intermediate folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste the following code in the new file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nwith unioned_book as (\n    select * from {{ ref('int_unioned_book') }}\n),\n \ndaily_position as (\n    select * from {{ ref('int_daily_position') }}\n),\n \nunioned as (\n    select \n        book,\n        book_date,\n        trader,\n        instrument,\n        book_action,\n        cost, \n        currency,\n        volume, \n        cost_per_share, \n        stock_exchange_name,\n        sum(unioned_book.volume) \n            over(\n                partition by \n                    instrument, \n                    stock_exchange_name, \n                    trader \n                order by \n                    unioned_book.book_date rows unbounded preceding) \n                        as total_shares\n    from unioned_book  \n \n    union all \n \n    select  \n        book,\n        stock_date as book_date, \n        trader, \n        instrument, \n        'HOLD' as book_action,\n        0 as cost,\n        currency, \n        0 as volume, \n        0 as cost_per_share,\n        stock_exchange_name,\n        total_shares\n    from daily_position\n    where (book_date,trader,instrument,book,stock_exchange_name) \n        not in \n        (select book_date,trader,instrument,book,stock_exchange_name\n            from unioned_book\n        )\n)\n \nselect * from unioned\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EExecute the following code to build int_unioned_book and it’s child models.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt run --select int_unioned_book+\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003ENow that we have created all of these models, we are able to attribute shares by trader.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_9.png\" alt=\"int\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003E\n","\u003Cp\u003ECommit the work.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun the following code in either the dbt Cloud IDE or a Snowflake worksheet.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom pc_dbt_db.<dev_schema>_marts.int_daily_position_with_trades\nwhere trader = 'Tina M.'\norder by  book_date\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_10.png\" alt=\"int\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Facts\u003C/h2\u003E\n","\u003Ch4\u003Edbt pipelines - PnL calculation\u003C/h4\u003E\n","\u003Cp\u003ENow for the last models in our tale. We have the trading history of our desks and our stock price history.\u003C/p\u003E\n","\u003Cp\u003ELet's create a model to show how Market Value and PnL changed over time. You might notice that this is going into our core folder. This is because this is a part of the core logic of our fictional company. This model will be a source of truth, to be used by many in downstream processes like a BI tool.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate the file \u003Ccode\u003Efct_trading_pnl.sql\u003C/code\u003E in the core folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy the following code to the new file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{{ \nconfig(\n      tags = 'core'\n      ) \n}}\n\n\nwith\ndaily_positions as (\n    select * from {{ ref('int_daily_position_with_trades' )}}\n\n),\n\nstock_history as (\n    select * from {{ ref('int_stock_history_major_currency') }}\n\n),\n\njoined as (\n    select \n        daily_positions.instrument, \n        daily_positions.stock_exchange_name, \n        daily_positions.book_date, \n        daily_positions.trader, \n        daily_positions.volume,\n        daily_positions.cost, \n        daily_positions.cost_per_share,\n        daily_positions.currency,\n        sum(cost) over(\n                partition by \n                    daily_positions.instrument, \n                    daily_positions.stock_exchange_name, \n                    trader \n                order by\n                    daily_positions.book_date rows unbounded preceding \n                    )\n                as cash_cumulative,\n       case when daily_positions.currency = 'GBP' then gbp_close\n            when daily_positions.currency = 'EUR' then eur_close\n            else 'Close'\n       end AS close_price_matching_ccy, \n       daily_positions.total_shares  * close_price_matching_ccy as market_value, \n       daily_positions.total_shares  * close_price_matching_ccy + cash_cumulative as PnL\n   from daily_positions\n   inner join stock_history \n      on daily_positions.instrument = stock_history.company_symbol \n     and stock_history.stock_date = daily_positions.book_date \n     and daily_positions.stock_exchange_name = stock_history.stock_exchange_name\n)\n\nselect * from joined \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ERun the model with \u003Ccode\u003Edbt run --select fct_trading_pnl.sql\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou might have noticed that fct_trading_pnl takes some time to build when you do a dbt run. As we use larger and larger datasets, the run times are getting longer. We have already materialized our models as a table, meaning we are preemptively front loading our run times to save on the query times when the table is queried. We could always increase our warehouse size, but there’s another trick up our sleeve we won’t touch on just yet.\u003C/p\u003E\n","\u003Cp\u003EWe can materialize the model as an incremental model, meaning that it will not be rebuilt each time, but rather only the latest rows transformed and added to the existing table.\nAll you have to do is update the model configuration and include the is_incremental macro which comes into action for the incremental runs (and is ignored during initial run and \u003Ca href=\"https://docs.getdbt.com/reference/resource-configs/full_refresh\"\u003Efull_refresh\u003C/a\u003E option). You can learn more about this \u003Ca href=\"http://greatexpectations.io/\"\u003Eincremental materialization here\u003C/a\u003E.\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\n","\u003Cp\u003ELet’s try out an incremental model. Create a new file \u003Ccode\u003Efct_trading_pnl_incremental.sql\u003C/code\u003E in the core folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy the following code to the new file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{{ \nconfig(\n      materialized='incremental',\n      unique_key= 'pk_key',\n      tags = 'core'\n      ) \n}}\n \n \nwith \ndaily_positions as (\n    select * from {{ ref('int_daily_position_with_trades' )}}\n),\n \nstock_history as (\n    select * from {{ ref('int_stock_history_major_currency') }}\n),\n \njoined as (\n    select \n \n        daily_positions.instrument, \n        daily_positions.stock_exchange_name, \n        daily_positions.book_date, \n        daily_positions.trader, \n        daily_positions.volume,\n        daily_positions.cost, \n        daily_positions.cost_per_share,\n        daily_positions.currency,\n        sum(cost) over(\n                partition by \n                    daily_positions.instrument, \n                    daily_positions.stock_exchange_name, \n                    trader \n                order by\n                    daily_positions.book_date rows unbounded preceding \n                    )\n                as cash_cumulative,\n       case when daily_positions.currency = 'GBP' then gbp_close\n            when daily_positions.currency = 'EUR' then eur_close\n            else 'Close'\n       end as close_price_matching_ccy, \n       daily_positions.total_shares  * close_price_matching_ccy as market_value, \n       daily_positions.total_shares  * close_price_matching_ccy + cash_cumulative as PnL\n   from daily_positions\n   inner join stock_history \n      on daily_positions.instrument = stock_history.company_symbol \n     and stock_history.stock_date = daily_positions.book_date \n     and daily_positions.stock_exchange_name = stock_history.stock_exchange_name\n),\n \nprimary_key as (\n \n    select \n \n        {{ dbt_utils.surrogate_key([\n                'trader', \n                'instrument', \n                'book_date', \n                'stock_exchange_name',\n                'PnL', \n            ]) }} as pk_key,\n                *\n \n    from joined \n)\n \nselect * from primary_key\n \n{% if is_incremental() %}\n  -- this filter will only be applied on an incremental run\n   where book_date > (select max(book_date) from {{ this }})\n \n{% endif %}\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ENow let’s see how differently this model acts in the first run versus subsequent runs.\nRun \u003Ccode\u003Edbt run --select fct_trading_pnl_incremental\u003C/code\u003E twice and compare the logs.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor the first run, you will notice that the where clause does not come in into the sql statement.\u003C/p\u003E\n","\u003Cp\u003EIn subsequent runs, dbt is including the where clause to create a temporary table to then merge into the existing table.\nThe first run of an incremental model will builds the table that will then new rows will be added to in subsequent runs.\u003C/p\u003E\n","\u003Cp\u003EFirst run:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/fct_1.png\" alt=\"fct\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESecond run:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/fct_2.png\" alt=\"fct\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will not see a true performance improvement in this example because our data is not loading exponentially but in production applications, you will.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Tests & Docs\u003C/h2\u003E\n","\u003Ch3\u003ETesting & Documentation\u003C/h3\u003E\n","\u003Cp\u003ETo build trust in data with your organization, it is impossible to overstate the importance of testing and documentation. While there are many ways to organize automated testing and documentation, dbt comes with an accessible \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/tests\"\u003Edata testing\u003C/a\u003E and \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/documentation\"\u003Edocumentation\u003C/a\u003E framework.\u003C/p\u003E\n","\u003Cp\u003EFor tests, dbt comes with a set of 4 pre-defined data tests:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Euniqueness\u003C/li\u003E\u003Cli\u003Enot_null\u003C/li\u003E\u003Cli\u003Echeck constraints\u003C/li\u003E\u003Cli\u003Erelationship integrity\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe are going to set up tests on a few models for this workshop. However, we strongly recommend you establish reasonable test coverage across the board. Adding your own tests is easy; as long as it can be written as a select statement, you can use it as a test. And just like with macros, there are plenty of fantastic packages out there like the \u003Ca href=\"https://hub.getdbt.com/calogica/dbt_expectations/latest/\"\u003Edbt_expectations\u003C/a\u003E package that provides tests similar to the \u003Ca href=\"http://greatexpectations.io/\"\u003EGreat Expectations\u003C/a\u003E open source project.\u003C/p\u003E\n","\u003Cp\u003EFor documentation, dbt takes model and column descriptions and will add them to the documentation site so that you have even more information about your models to share with your stakeholders via the site. It also pulls in information from the Snowflake information schema for a one stop show about your data project.\u003C/p\u003E\n","\u003Cp\u003ENow let's add testing and documentation to this project.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EFirst, create this file \u003Ccode\u003Eintermediate.yml\u003C/code\u003E in the intermediate folder.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy the following code into the new file and save it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Eversion: 2\n \nmodels:\n  - name: int_fx_rates\n    description: “An intermediate model that filters stg_knoema_fx_rates"\n    columns:\n      - name: currency||exchange_date\n        tests:\n          - unique\n          - not_null\n \n  - name: int_unioned_book\n    description: “An intermediate model unions the manual_book csvs"\n    columns:\n      - name: instrument\n        tests:\n          - not_null\n          - relationships:\n              to: ref('int_knoema_stock_history')\n              field: company_symbol\n \n  - name: int_knoema_stock_history\n    description: "An intermediate model that pivots the stg_knoema_stock_history model by indicator"\n    columns:\n      - name: company_symbol||stock_date\n        tests:\n          - not_null\n          - unique\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_1.png\" alt=\"test\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EExecute the following code on the command line:\u003Cbr\u003E\n\u003Ccode\u003Edbt test\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_2.png\" alt=\"test\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOh no! One of our tests failed! Let's try to understand why. If you click into the details tab, dbt provides you with the SQL query that failed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_4.png\" alt=\"test\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let’s take that query and paste it either into the Snowflake UI or the IDE to debug it.\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EExecute the following query to check the full row width for one of the records failed either in the IDE or the Snowflake UI (remember to update the dev_schema!):\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith cst as\n(\n    select\n        company_symbol||stock_date as conctat\n    from <dev_schema>_marts.int_knoema_stock_history\n    where company_symbol||stock_date is not null\n    group by company_symbol||stock_date\n    having count(*) > 1 \n    limit 1\n)\n \nselect * from <dev_schema>_marts.int_knoema_stock_history\nwhere company_symbol||stock_date IN (SELECT conctat FROM cst)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_3.png\" alt=\"test\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAha! There are shares which are traded on more than one stock exchanges.\nSo we need to include the stock_exchange_name attribute to the unique test key.\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003ELet's go back to \u003Ccode\u003Eintemediate.yml\u003C/code\u003E file and update the test configuration for int_knoema_stock_history model :\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Eversion: 2\n\nmodels:\n  - name: int_fx_rates\n    description: “An intermediate model that filters stg_knoema_fx_rates"\n    columns:\n      - name: currency||exchange_date\n        tests:\n          - unique\n          - not_null\n\n  - name: int_unioned_book\n    description: “An intermediate model unions the manual_book csvs"\n    columns:\n      - name: instrument\n        tests:\n          - not_null\n          - relationships:\n              to: ref('int_stock_history')\n              field: company_symbol\n\n  - name: int_knoema_stock_history\n    description: "An intermediate model that pivots the stg_knoema_stock_history model by indicator"\n    columns:\n      - name: company_symbol||stock_date||stock_exchange_name\n        tests:\n          - not_null\n          - unique\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ENow let’s run only the previously failing test.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt test -m int_knoema_stock_history\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_5.png\" alt=\"test\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELook at all that green!\nNow the last thing to do is to generate the documentation and see how our description fields are populated.\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003E\n","\u003Cp\u003ERun the command \u003Ccode\u003Edbt docs generate\u003C/code\u003E and click on view docs.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESearch for one of the models that we applied descriptions to either via the file tree or the search bar.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/docs_1.png\" alt=\"docs\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should now see the description field populated.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/docs_2.png\" alt=\"docs\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Deployment\u003C/h2\u003E\n","\u003Cp\u003EOkay, it seems like we have everything in place: pipelines have been developed, tested and documented.\u003C/p\u003E\n","\u003Cp\u003EThe next step would be to promote this code up the chain through our environments (which in this lab are simplified to just DEV & PROD).\nWe’ll do this by committing the work to our feature branch into the main branch and setting up a job to orchestrate the execution of the models in production. Generally it would be best for a pull request to be opened, so that your code is reviewed and tested via \u003Ca href=\"https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration-with-github#slim-ci\"\u003ESlim CI\u003C/a\u003E prior to promotion to production, but in the interest of keeping things concise for this workshop, we are going to merge into production without review.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ETo do this, the first thing you are going to do is commit any remaining work you haven’t yet.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_1.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ENow we are going to click merge to master\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_2.png\" alt=\"deploy\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EBy merging into master, you should have reverted back to your master branch for you to start the cycle over again for development.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_3.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ENow we are going to deploy our code via dbt Cloud. Luckily for you, Partner Connection has already created your Production Environment for you. Now all we have to do is update some defaults.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EClick on the hamburger menu and click on \u003Ccode\u003EEnvironments\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_4.png\" alt=\"deploy\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_5.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should see two different Environments: Development and Deployment.\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EClick on the \u003Ccode\u003EDeployment\u003C/code\u003E environment and then click on \u003Ccode\u003ESettings\u003C/code\u003E in the top right.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_6.png\" alt=\"deploy\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_7.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EWe are going to update the schema name from your development schema to \u003Ccode\u003Eproduction\u003C/code\u003E. To do this, click on Edit.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_8.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003E\n","\u003Cp\u003EAfter you update the field, be sure to click on \u003Ccode\u003ESave\u003C/code\u003E at the top.\nNow in doing this, dbt will default to building in a schema named \u003Ccode\u003Eproduction\u003C/code\u003E in the declared default database pc_dbt_db. This helps us separate out the objects we create in our production environment from our development sandbox.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAfter saving your environment, let’s update the default Partner Connect created job. To do this, click on the hamburger menu on the top left and click on \u003Ccode\u003EJobs\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_9.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003E\n","\u003Cp\u003ENow select the pre-configured job named \u003Ccode\u003EPartner Connect Trial Job\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s edit it by first going to \u003Ccode\u003ESettings\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Ccode\u003EEdit\u003C/code\u003E on the top right.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAnd now we are going to update a few things. Change the name of the Job to \u003Ccode\u003EProduction Job\u003C/code\u003E to signify that this is going to be your production run.\nUpdate the number of \u003Ca href=\"https://docs.getdbt.com/dbt-cli/configure-your-profile#understanding-threads\"\u003Ethreads\u003C/a\u003E to 8. This will allow dbt to run 8 dbt models in the DAG concurrently sans dependencies. 8 is the default we recommend for Snowflake.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_10.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n\u003Col start=\"12\"\u003E\u003Cli\u003E\n","\u003Cp\u003EBecause the dbt commands section already include the sequence of commands that we need things to operate, we are going to keep it as is. We want to seed our manual uploads to our production schema first. Then we want to create our objects in our schema prior to running tests on them.\nWe are also going to leave the scheduling on it’s default off configuration because we will be manually kicking off the job for this workshop.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow go to the top and click \u003Ccode\u003ESave\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThen click on the job name in the path on top. This will return you to the job run history page, from where you can click on \u003Ccode\u003ERun now\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick into the run to check out how the run is going. Your run should be successful like this after completion.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_11.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETake a look at the various run steps. You can validate that on the dbt run step, you are building into the prod schemas as noted in our environment configuration.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EVerify  in Snowflake UI that you now we have data in the production schema:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_12.png\" alt=\"deploy\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAnd this concludes our workshop! If you’re interested in exploring further, there are “extra credit” tasks in the appendix of this workshop, in which you’ll cover things like how to visualize what you’ve created with Snowsight.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion & Next Steps\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on completing the lab!\u003C/p\u003E\n","\u003Cp\u003EToday, you learned how to use dbt and Snowflake to build data transformation pipelines for analytics. You’re now ready to apply these fundamentals to your own data.\nWe encourage you to continue with your free trial by loading your own sample or production data, and by continuing to dive into some of the more advanced functionality of dbt Cloud and Snowflake.\u003C/p\u003E\n","\u003Ch4\u003EWhat we've covered:\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up dbt & Snowflake\u003C/li\u003E\u003Cli\u003EHow to leverage data in Snowflake's Data Marketplace\u003C/li\u003E\u003Cli\u003EHow to run a dbt project and develop pipelines\u003C/li\u003E\u003Cli\u003EHow to create data tests and documentation\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EAdditional Resources:\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EJoin our \u003Ca href=\"https://community.getdbt.com/\"\u003Edbt community Slack\u003C/a\u003E which contains more than 18,000 data practitioners today. We have a dedicated slack channel #db-snowflake to Snowflake related content.\u003C/li\u003E\u003Cli\u003ETo continue to learn to use dbt more effectively, check out the \u003Ca href=\"https://learn.getdbt.com/\"\u003Edbt Learn site\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EContact the \u003Ca href=\"https://www.getdbt.com/contact/\"\u003Edbt Cloud Sales team\u003C/a\u003E if you’re interested in exploring dbt Cloud for your team or organization.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAppendix\u003C/h2\u003E\n","\u003Ch3\u003EVisualizations\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you want to create a simple data visualization for this dataset, we can use Snowsight. To do that, let's click on the Preview App button once again:\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnable the Dashboards and worksheets.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThen click Worksheets -> + Worksheet to add a new one.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy and paste the following query in the worksheet.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom pc_dbt_db.<dev_schema>_marts.int_daily_position_with_trades\nwhere trader = 'Tina M.'\norder by  book_date\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003E\n","\u003Cp\u003EHit the run button and switch from a table view to chart.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EBy default it shows a breakdown by Volume. Click on the measure and switch it into PNL.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick “+Add column” to add another measure and select “Market Value” to display Market value and PnL side by side.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAnd that’s it! Now you have a worksheet that you can slice and dice, share with your colleagues or embed in the SnowSight dashboard as a tile. For more details on SnowSight, please refer to the Snowflake documentation.\u003C/p\u003E"],"title":"Accelerating Data Teams with dbt Cloud & Snowflake","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nModern businesses need modern data strategies, built on platforms that support agility, growth and operational efficiency. \r\n\r\n[Snowflake](https://signup.snowflake.com/?utm_source=google&utm_medium=paidsearch&utm_content=go-eta-ld-free-trial&utm_term=c-g-snowflake-e&utm_campaign=na-en-Branded&_bt=470247374327&_bk=snowflake&_bm=e&_bn=g&_bg=64805047909&gclid=Cj0KCQjw18WKBhCUARIsAFiW7JwA-C_HmNQzG_OFKhM1Hn9vlW6EAs-9mveiMXychVbbK34lh4vGfHsaAv4NEALw_wcB&gclsrc=aw.ds) is the Data Cloud, a future-proof solution that simplifies data pipelines, so you can focus on data and analytics instead of infrastructure management.\r\n\r\n[dbt](https://www.getdbt.com/) is a transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines. It transforms data in the warehouse, leveraging cloud data platforms like Snowflake.\r\n\r\n\r\nIn this Quickstart, you will follow a step-by-step guide to using dbt with Snowflake, and see some of the benefits this tandem brings.\r\n\r\nLet's get started.\r\n\r\n### What You'll Use During the Lab\r\n\r\n* A trial [Snowflake account](https://trial.snowflake.com/) with `ACCOUNTADMIN` access\r\n\r\n* A [dbt Cloud account](https://www.getdbt.com/signup/) \r\n\r\n### What You'll Learn\r\n\r\n* How to build scalable data transformation pipelines using dbt & Snowflake\r\n\r\n* How to leverage data in Snowflake's Data Marketplace\r\n\r\n### What You'll Build\r\n* A set of data analytics pipelines for Financial Services data leveraging dbt and Snowflake, making use of best practices like data quality tests and code promotion between environments\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n\r\n## Architecture and Use Case Overview\r\n\r\nIn this lab, we are going to analyze the historical trading performance of a company that has trading desks spread across different regions. As inputs, we are going to leverage datasets available in Knoema Economy Data Atlas, which is available in Snowflake Marketplace. We’ll also make use of a few manual uploads.\r\nTo do this, we are going to set up the environments, build scalable pipelines in dbt, establish data tests , and promote code to production. \r\n\r\n![Architecture Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/arch_diagram.png)  \r\n\r\nJust to give you a sneak peek, this is the data lineage we will be creating using dbt.\r\n\r\nStay tuned!\r\n\r\n![DAG](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/lineage_graph.png) \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Snowflake Configuration \r\n\r\n1. Login to your Snowflake trial account.  \r\n![Snowflake Log In Screen](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/snowflake_login.png)  \r\n\r\n2. UI Tour (SE will walk through this live). For post-workshop participants, click [here](https://docs.snowflake.com/en/user-guide/snowflake-manager.html#quick-tour-of-the-web-interface) for a quick tour of the UI.  \r\n\r\nWe will be using the new UI to getting started but you can also switch over to the Classic Console if you would like. It will not affect your dbt experience but may change where the buttons are in the Snowflake UI. \r\n\r\nNew UI:\r\n![New Snowflake UI](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/new_snowflake_ui.png)  \r\n\r\nClassic UI:\r\n\r\n![Snowflake Worksheets](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/snowflake_worksheets.png)  \r\nIf you ever want to change from the new UI to the classic one, click on the home button and then `Classic Console`\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Connect to Data Source\r\n\r\nNow we need to obtain our raw data. We are going to the Snowflake Marketplace to connect to the Knoema dataset. \r\n\r\n1. We want to go into the Snowflake Marketplace to connect to the Knoema dataset. Click on Data on the left hand sidebar and then Marketplace.\r\n\r\n![Click on Marketplace](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_marketplace.png) \r\n\r\n2. Now let’s look for Knoema Economy Atlas Data. Type into the “Search Data Marketplace” search bar “Knoema Economy Atlas Data”.  Find the Knoema Economy Atlas Data tile in the results. Once you find it, click on it.\r\n\r\n![Click on Knoema Tile](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_knoema_tile.png) \r\n\r\n6. You should now on a page with a description of the data, sample queries, and other useful information. Let's get this data into our Snowflake account. Click the \"Get Data\" button. \r\n\r\n![Click on Get Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/get_data.png) \r\n\r\n7. In the pop-up, enter “KNOEMA_ECONOMY_DATA_ATLAS” as the database name and click “Get Data”. Do not forget to update the name of the database or you will have update some code down the line.\r\n\r\n![Rename Database](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/rename_database.png) \r\n\r\n\r\nSo what is happening here? Knoema has granted access to this data from their Snowflake account to yours. You're creating a new database in your account for this data to live - but the best part is that no data is going to move between accounts! When you query, you'll really be querying the data that lives in the Knoema account. If they change the data, you'll automatically see those changes. No need to define schemas, move data, or create a data pipeline either.\r\n\r\n8. Click on Query Data. \r\n\r\n![Click on Query Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_data.png)\r\n\r\n9. Now let’s look at the new data. On the left hand sidebar, you should see KNOEMA_ECONOMY_DATA_ATLAS listed as a database. If not, refresh the UI and check again.\r\n\r\n10. Now let’s try to query the data. If you want to switch back to the original interface, you can click on the home button on the top left and then `Classic Console` on the bottom of the left hand sidebar. This will port you back where you can see the database displayed the way it was before. \r\n\r\nThe Economy Atlas comes with more than 300 datasets. In order to improve navigation, the data provider has kindly supplied a table called DATASETS. Let's go ahead and find the ones related to the stock history and currency exchange rates that we are going to use in the next step.\r\n\r\n\r\n11. Remove the code in the worksheet and paste in this code. \r\n\r\n```sql\r\n\r\nselect * \r\n  from \"KNOEMA_ECONOMY_DATA_ATLAS\".\"ECONOMY\".\"DATASETS\"\r\n where \"DatasetName\" ILIKE 'US Stock%'\r\n    OR \"DatasetName\" ILIKE 'Exchange%Rates%';\r\n```\r\n![Query Knoema](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_knoema_1.png)\r\n\r\n\r\n12. Now we can see what our datasets names are, let's try to query one of the datasets:\r\n\r\n\r\n```sql\r\nselect * \r\n  from KNOEMA_ECONOMY_DATA_ATLAS.ECONOMY.USINDSSP2020\r\n where \"Date\" = '2020-02-03';\r\n```\r\n![Query Knoema](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_knoema_2.png)\r\n \r\nCongratulations! You successfully tapped into a live data feed of Trade and FX rates data with NO ETL involved. Isn't this cool? Now let's start building our pipelines.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## dbt Configuration \r\n\r\nNow it's time to set up dbt. We are going to be using [Snowflake Partner Connect](https://docs.snowflake.com/en/user-guide/ecosystem-partner-connect.html) to set up your dbt Cloud account and project. Using this method will allow you to spin up a fully fledged dbt account with your [Snowflake connection](https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-your-database#connecting-to-snowflake), [managed repository](https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-using-a-managed-repository), [environments](https://docs.getdbt.com/docs/guides/managing-environments), and credentials in minutes.\r\n \r\n1. In the Snowflake UI, click on the home icon on the top left side. To access Partner Connect, click on your user and then Partner Connect. Make sure you're still set as the ACCOUNTADMIN role. \r\n\r\n![Click on Partner Connect](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_Partner_Connect_1.png)\r\n\r\nIf you're using the classic console, it will be on the top right. \r\n![Click on Partner Connect](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_Partner_Connect_2.png)\r\n\r\n2. Find the dbt Tile by typing into the \"Search Partner Connect\" search bar. Click on the dbt tile. \r\n\r\n3. You should now see a popup that says `Connect to dbt`. Click on `Optional Grant` and you are going to add in the KNOEMA_ECONOMY_DATA_ATLAS database. This will grant access for your new dbt user role to the database. \r\n\r\n4. Click on `Connect`. This will create a dedicated dbt user, database, warehouse, and role for your dbt Cloud trial.  \r\n\r\n![Click on Connect](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_dbt_Connect.png)\r\n\r\n5. When the popup that says \"Your partner account has been created\", click on activate. \r\n![Click on Partner Activate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/click_on_dbt_connect_activate.png)\r\n\r\n6. You should be redirected to a dbt Cloud registration page. Fill out the form. \r\nMake sure to save the password somewhere for login in the future. \r\n\r\n![Register dbt Cloud account](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/register_dbt_cloud_account.png)\r\n\r\n7. Click on Compete Registration. You should now be redirected to your dbt Cloud account, complete with a connection to your Snowflake account, a deployment and a development environment, and even a sample job. \r\n\r\nTo help you version control your dbt project, we have connected it to a [managed repository](https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-using-a-managed-repository), which means that dbt Labs will be hosting your repository for you. This will give you access to a git workflow without you having to create and host the repository yourself. You will not need to know git for this workshop; dbt Cloud will help guide you through the workflow. In the future, when you’re developing your own project, feel free to use [your own repository](https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-installing-the-github-application). This will allow you to play with features like [Slim CI](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration-with-github) builds after this workshop.\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## dbt Project Configuration\r\n\r\n1. Now let’s set up our dbt project. Click on the hamburger menu on the top left side and click on `Develop`.  This will spin up your IDE (Integrated Development Environment) where you will be developing your dbt Project. \r\n\r\n![Click on the hamburger](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/hamburger.png)\r\n![Click on the develop](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/develop.png)\r\n\r\n2. After the IDE loads, click `Initialize your project` to set up your dbt project. Once you click on it, dbt will generate our starter project with the core files and folders.\r\n![Click on the Initialize](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/initialize.png)\r\n\r\n\r\n4. You should see new files and folders created in your file tree. These are all the files and folders you will need for a dbt project. Take a look around, especially at the [dbt_project.yml](https://docs.getdbt.com/reference/dbt_project.yml) and what’s in the [models](https://docs.getdbt.com/docs/building-a-dbt-project/building-models) directory. \r\n\r\n5. Click `Commit`. Enter a commit message, and then click `Commit` again to commit your work to your master branch. \r\n\r\n![Click on the commit](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/commit.png)\r\n![commit message](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/commit_message.png)\r\n\r\nCommit messages should always be indicative of the work you are saving. This helps you create a reference point for the future in case of auditing and debugging.  By [committing](https://www.atlassian.com/git/tutorials/saving-changes/git-commit#:~:text=The%20git%20commit%20command%20captures,you%20explicitly%20ask%20it%20to.), you are saving to a remote branch in Github. This will also be the only time you will save straight to your master branch (which is the main branch). We always want to create a degree of separation from development work and your production branch.  \r\n\r\n6. Click `create new branch` to check out a new git branch to start developing. \r\n![create branch](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_branch.png)\r\n\r\n7. Name the branch “dbt_snowflake_workshop” and click `Submit`.\r\n![name branch](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/name_branch.png)\r\n\r\n8. UI walkthrough (SA will walk through during the workshop). Post workshop, users can look at [this video for the walkthrough](https://getdbt.wistia.com/medias/0828jynq3v).\r\n\r\n9. Now let’s validate that everything was initialized correctly by running the sample models that come with the starter dbt project. \r\n\r\n10. Type in `dbt run` on the command line at the bottom, and click `Enter`. \r\nThe command line is where you will be entering in dbt commands to execute dbt actions.\r\n\r\nIf you want to see the actual code being executed, you can go into the ‘Details’ tab next to `Summary` and look through the logs. Here you can see that dbt is writing the DDL for you, allowing you to focus on just writing the SQL select statement.  \r\n\r\n![dbt run](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/dbt_run.png)\r\n\r\n11. The output should look like below, confirming dbt was able to connect to Snowflake and successfully execute the sample models in the models folder: \r\n\r\n![successful run](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/successful_run_1.png)\r\n\r\n12. If you want to see what is executed against Snowflake, click into one of the model tabs and click on details. You will see the log of the compiled code and how dbt interacted with Snowflake. Note that dbt by default built all of your models into this development schema made up of your first initial and last name. We will cover more environments later on. \r\n\r\n![successful run](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/successful_run_2.png)\r\n\r\n\r\n13. Now let’s visually confirm the objects in Snowflake. Switch to the Snowflake UI (classic console) and refresh database objects. Expand the database `PC_DBT_WH`. Then the development schema with your first initial and last name. And lastly Tables and Views. You should see the table `MY_FIRST_DBT_MODEL`  and the view `MY_SECOND_DBT_MODEL`. \r\n\r\n![successful run](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/successful_run_3.png)\r\n\r\nCongratulations! You just ran your first dbt models on Snowflake!\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\nWe are going to start by adding a few more things to our dbt project configuration in order to improve maintainability. \r\n\r\n14. Let’s add in some folders to follow our dimensional modeling structure. To learn more about how we structure our project, [check out this discourse post](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355).  To add new folders, ghost over the models directory in the file tree and click on the ‘…’ on the right next to the models directory. Click on “New Folder” and type into it “staging”. Click “Create” after you finish typing and you should see a new folder in the file tree.\r\n\r\n![create folder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_1.png)\r\n![create folder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_2.png)\r\n![create folder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_3.png)\r\n![create folder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_4.png)\r\n\r\n15. Now that you’ve gotten one completed, let’s go ahead and create three more folders. First create a folder named “knoema” in the staging model by clicking on the ‘…’ next to the staging folder. This will be where we will put our staging models specific to our knoema source. \r\n\r\n16. Now for our last folders, we are actually going to create two folders at once. Click on the ‘...’ next to models again. This time, we are going to fill the file path as “models/marts/core”. This will create a new folder under models called marts and in it, a folder called core.  \r\n\r\nYou should have a file tree that looks like this after completing this task.\r\n\r\n![create folder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/create_folder_5.png)\r\n\r\n17. Next, let’s update the [dbt_project.yml file](https://docs.getdbt.com/reference/dbt_project.yml). This is a file that tells dbt that the repository is a dbt project as well as how to operate on your project. Click on the file `dbt_project.yml`. The first thing we are going to do is update the project name from “my_new_project” to “\u003Cyour_name\u003E_dbt_workshop” at line 5. This will personalize the project a little, and make it yours.  \r\n\r\nDo not forget to save after you have updated the file! This means you will want to either click save or use cmd+s (mac)/ctrl+s(pc). The IDE will also warn you with the teal button on the tab. \r\n\r\n![update project](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/update_project.png)\r\n\r\n\r\n18. Now let’s update the models section in the file by declaring our new folders and their model configurations. We will be declaring the default [schema](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas) that dbt builds the object into, the default [materialization](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations), and disabling any unnecessary models from a run. All of these configurations can be overridden by model configurations on the model file. If a schema is not declared via a configuration, dbt will build into the default schema declared on our development credentials. \r\n\r\nCopy code below and paste into line 34 to line 38. Remember to replace \u003Cyour_name\u003E.\r\n\r\n```yml \r\nmodels:\r\n  \u003Cyour_name\u003E_dbt_workshop:\r\n      example:\r\n          schema: example \r\n      staging:\r\n          schema: staging\r\n          materialized: view\r\n      marts:\r\n          schema: marts\r\n          materialized: table\r\n```\r\n19. Save the file. \r\n20. Be sure to click on “Commit” using the git command button (just in case your computer spontaneously explodes into a blob of smoke and melted plastic) to save your work. And supply a good commit message that conveys what you are saving!\r\n\r\n\r\n21. To better understand how schema names work in dbt, let's take a look at the standard [macro](https://docs.getdbt.com/docs/building-a-dbt-project/jinja-macros) that [dbt uses to define what schema to build your object into](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas). By default, dbt is generating a schema name by appending it to the target schema environment name(dev, prod). If you ever want to override this, you are going to create a new file in the macros folder named `generate_schema_name.sql`. \r\n\r\n![schema macro](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/schema_macro_1.png)\r\n\r\n22. Copy and paste the below code into file generate_schema_name.sql to generate the macro. \r\n\r\n```sql\r\n{% macro generate_schema_name(custom_schema_name, node) -%}\r\n\r\n    {%- set default_schema = target.schema -%}\r\n    {%- if custom_schema_name is none -%}\r\n\r\n        {{ default_schema }}\r\n\r\n    {%- else -%}\r\n\r\n        {{ default_schema }}_{{ custom_schema_name | trim }}\r\n\r\n    {%- endif -%}\r\n\r\n{%- endmacro %}\r\n\r\n```\r\n\r\n![schema macro](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/schema_macro_2.png)\r\n\r\n23. Click Save \r\n\r\n24.  To see this macro in action, do another `dbt run` on the command line. Note how the schema has changed to be appending the custom schema name to our default schema per our macro. You can add in more [conditionals](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas#jinja-context-available-in-generate_schema_name) to fit your own needs in terms of environment promotions. \r\n\r\n\r\nIn this workshop, we will show you how different configurations can easily change how and where your dbt project reads and writes from without needing you to change your code with code promotion. \r\n\r\n\r\nMacros are a way to do this. Macros are written in a pythonic templating language called [Jinja](https://docs.getdbt.com/docs/building-a-dbt-project/jinja-macros). Jinja allows you to do things that normally aren’t possible in SQL such as create control structures (like if statements and for loops) or abstract away snippets of SQL into reusable functions applicable throughout your project. Jinja helps you write [DRY](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself) code in a language that is generally wet.\r\n\r\nIn this generate_schema_name macro, we are defining what schema your objects should be built into based on the conditionals specified. \r\nGoing through the macro, what is happening here: \r\n\r\n```sql \r\n   {%- set default_schema = target.schema -%}\r\n    {%- if custom_schema_name is none -%}\r\n \r\n        {{ default_schema }}\r\n```\r\n \r\nIs that we are defining our default schema as the target database and schema defined for your development environment. The Partner Connect flow sets up our development environment to point to the partner connect created database and a schema made of your first initial and last name. You can verify this in your [profile](https://cloud.getdbt.com/#/profile). \r\nHowever, if a custom schema is provided as we did in our dbt_project.yml file, dbt will build into a schema that is a concatenation of our default schema and the custom schema name due to this logic: \r\n\r\n```sql \r\n\r\n{%- else -%}\r\n\r\n    {{ default_schema }}_{{ custom_schema_name | trim }}\r\n\r\n{%- endif -%}\r\n```\r\n\r\nAll of this logic is defined in one place and will be applied on your project which makes it simple to scale up your project, especially as business needs arise. \r\n\r\nLater on, we will learn how to use macros from a package and see how those macros help us write SQL quickly. \r\n\r\n\r\n25.  Now we need to apply another operational macro. This time, this macro will add a [query tag](https://docs.getdbt.com/reference/resource-configs/snowflake-configs#query-tags) for every dbt run in the Snowflake history.  To do this, we’ll create a file in the “macros” folder called `query_tag.sql`. \r\n\r\n26. Copy and paste the following code. This provides the ability to add an additional level of transparency by automatically setting Snowflake query_tag to the name of the model it is associated with. \r\n\r\n```sql \r\n{% macro set_query_tag() -%}\r\n  {% set new_query_tag = model.name %} {# always use model name #}\r\n  {% if new_query_tag %}\r\n    {% set original_query_tag = get_current_query_tag() %}\r\n    {{ log(\"Setting query_tag to '\" ~ new_query_tag ~ \"'. Will reset to '\" ~ original_query_tag ~ \"' after materialization.\") }}\r\n    {% do run_query(\"alter session set query_tag = '{}'\".format(new_query_tag)) %}\r\n    {{ return(original_query_tag)}}\r\n  {% endif %}\r\n  {{ return(none)}}\r\n{% endmacro %}\r\n\r\n```\r\n27. Click “Save”. \r\n\r\n![query tags](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_tag.png)\r\n\r\n28. Now, do a `dbt run` again on the command line.\r\n29. To see where the query tags are applied, go to the Snowflake UI, click the “History” icon on top. You are going to see all the SQL queries you’ve run on your Snowflake account (successful, failed, running etc.). and clearly see which dbt model a  particular query is related to. \r\n\r\n![query tags](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/query_tag_2.png)\r\n\r\n\r\nUpdate the Filter to the user is PC_DBT_USER and the status is Succeeded\r\nto see the same view as the screenshot above, you can also remove extra columns by clicking on the arrow after hovering over a column, hovering over “Columns” and unchecking any columns you don’t want.\r\n\r\n30. Now we are going to install a dbt package. A dbt [package](https://docs.getdbt.com/docs/building-a-dbt-project/package-management) is essentially a dbt project that you can install onto your own dbt project to gain access to the code and use it as your own. Many of our packages are hosted on the [dbt Packages Hub](https://hub.getdbt.com/). In our lab, we are going to demonstrate how to use some useful macros in the dbt_utils package to write some complex SQL. To install it, create a file called `packages.yml` at the same level as your `dbt_project.yml` file.\r\n\r\n![packages](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/packages_1.png)\r\n\r\n31. Copy and paste the following code into file packages.yml.\r\n\r\n```yml\r\npackages:\r\n  - package: dbt-labs/dbt_utils\r\n    version: 0.8.0\r\n```\r\n\r\n![packages](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/packages_2.png)\r\n\r\n32. Click `Save`. \r\n33. Now we are going to install the package. By running `dbt deps`, you tell dbt to install the packages. With a successful run, you can check out the dbt_modules folder to see what packages you have installed and the code that is now available to you. \r\n![packages](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/packages_3.png)\r\n\r\n34. Last thing to do before we save our work. Remove the example subdirectory configuration in the `dbt_project.yml` file and delete the example folder with the models in it.\r\n\r\n35. Click `Commit` to commit your work! \r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Building dbt Data Pipelines\r\n\r\nNow we start to get into the fun stuff. In the next few sections, we are going to build our dbt pipelines. This will include transformations that define these these areas of interest: \r\n- Stock trading history\r\n- Currency exchange rates\r\n- Trading books\r\n- Profit & Loss calculation\r\n\r\n## dbt pipelines - Sources & Staging\r\n\r\n#### Setting up our Sources\r\n\r\n1. Now let’s head back to the dbt Cloud IDE. We are going to start building our pipelines by declaring our [dbt sources](https://docs.getdbt.com/docs/building-a-dbt-project/using-sources).  Create a `knoema_sources.yml` file in the staging/knoema folder. \r\n\r\n\r\n2. Paste in the following code. \r\n\r\n```yml\r\nversion: 2\r\n\r\nsources:\r\n  - name: knoema_economy_data_atlas\r\n    database: knoema_economy_data_atlas\r\n    schema: economy\r\n    tables:\r\n      - name: exratescc2018\r\n      - name: usindssp2020\r\n```\r\n![sources](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/sources_1.png)\r\n\r\nAs you might remember, these two objects were mentioned in the Knoema Dataset Catalog table: daily exchange rates and daily US trading history accordingly.\r\n\r\n3. Click `Commit` and provide a good commit message like “set up knoema source file”. We are going to keep building that muscle of saving your work as you develop. \r\n\r\nNow we have defined the sources that we will be building our dbt models on top of. By declaring these, we are able to create lineage from our raw objects to our transformed objects and apply documentation and testing. \r\n\r\n#### Setting up our Staging Models\r\n\r\n4. Now let’s create staging models to clean up our raw objects. Staging models bear a one-to-one relationship with the source data table they represent. They have the same granularity, but the columns have been renamed, recast, or reconsidered in some way to follow a consistent, useful format. By creating these as the first level transformation, you can create a consistent foundation to build more complex transformations upon. To learn more, check out our [discourse post](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355#data-transformation-101-1).\r\nThe first staging model we are going to make will be for exratescc2018. Create a new file in the staging/knoema folder named `stg_knoema_fx_rates.sql`. \r\n\r\n5. Paste the following select statement. \r\n\r\n(To learn more about our recommendations around coding style, [check out this repository.](https://github.com/dbt-labs/corp/blob/master/dbt_style_guide.md#ctes))\r\n\r\n```sql \r\nwith source as (\r\n \r\n    select * from {{source('knoema_economy_data_atlas','exratescc2018') }}\r\n \r\n), \r\n \r\nrenamed as (\r\n \r\nselect \r\n \r\n    \"Currency\" as currency,\r\n    \"Currency Unit\" as currency_unit,\r\n    \"Frequency\" as frequency,\r\n    \"Date\" as exchange_date,\r\n    \"Value\" as exchange_value,\r\n    \"Indicator\" as indicator,\r\n    \"Indicator Name\" as indicator_name,\r\n    'Knoema.FX Rates' as data_source_name\r\n \r\nfrom source \r\n \r\n) \r\n \r\nselect * from renamed\r\n \r\n```\r\n\r\n6. Save the file. To check the query before executing the code against the warehouse, you can click on the `Preview` button. You can also click on `Compile` to see the compiled code that will be executed. \r\n\r\n![staging](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_1.png)\r\n\r\nIn the compiled code, you will notice that the source function\r\n({{source('knoema_economy_data_atlas','exratescc2018')}})  compiles to the actual database object. \r\n\r\nLet’s talk more about this function: \r\n\r\nUsing the sources function allows you to: \r\n- Tell dbt how a model relates to its source and then dynamically generate a DAG from this, as you can see in the lineage tab\r\n\r\n![staging](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_2.png)\r\n\r\n- This also allows dbt to dynamically replace the sources with their [target-specific name](https://docs.getdbt.com/reference/dbt-jinja-functions/target), whether it's from the configuration in sources.yml or from the environment. This allows you to promote the same code from dev to prod and any other environments without having to change the code. You can even use the [target jinja function](https://docs.getdbt.com/reference/dbt-jinja-functions/target#use-targetname-to-change-your-source-database) to conditionally point to different databases based on the declared target. \r\n\r\n7. Now to make the other staging model. Create the file in the same folder and name it `stg_knoema_stock_history.sql`. \r\n\r\n8. Paste in this code and save. \r\n\r\n```sql \r\nwith source as (\r\n\r\n    select * from {{source('knoema_economy_data_atlas','usindssp2020')}}\r\n), \r\n\r\nrenamed as (\r\n\r\n    select \r\n\r\n        \"Company\" as company,\r\n        \"Company Name\" as company_name,\r\n        \"Company Ticker\" as company_symbol,\r\n        \"Stock Exchange\" as stock_exchange,\r\n        \"Stock Exchange Name\" as stock_exchange_name,\r\n        \"Indicator\" as indicator,\r\n        \"Indicator Name\" as indicator_name,\r\n        \"Units\" as units,\r\n        \"Scale\" as scale, \r\n        \"Frequency\" as frequency, \r\n        \"Date\" as stock_date,\r\n        \"Value\" as stock_value,\r\n        'Knoema.Stock History' as data_source_name \r\n\r\n    from source \r\n\r\n) \r\n\r\nselect * from renamed\r\n```\r\n\r\n9. Now let’s create these views in Snowflake. Since we only want to run the models in the staging folder, we are going to use the following command on the command line: \r\n\r\n`dbt run --select staging.*`\r\n\r\nThis allows us to be selective and only run the models in the staging folder. [Node selectors](https://docs.getdbt.com/reference/node-selection/syntax) are very helpful in refining what we actually want to run and test. \r\n\r\n![staging](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_3.png)\r\n\r\n10. Once everything ran successfully, let’s save our work to our remote branch. Click on `Commit` and provide a good commit message like “set up staging models”\r\n11. Now we can go and query this dataset to take a feel of what the data profile looks like. You can either put this query in the “Statement 1” tab or in Snowflake UI. Remember to change the schema name to the correct one for you. \r\n\r\n```sql \r\nselect * \r\n  from pc_dbt_db.\u003Cdev_schema\u003E_staging.stg_knoema_stock_history\r\n where company_symbol ='AAPL' \r\n   and stock_date ='2021-03-01'\r\n```\r\n\r\n![staging](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_4.png)\r\n\r\nIn this dataset, different measures like Close, Open, High and Low price are represented as different rows. For our use case, this is a bit suboptimal - it would be better to see that data transposed into columns; something more like this:\r\n\r\n![staging](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/staging_5.png)\r\n\r\n\r\nSo let’s do that!\r\n\r\n## dbt pipelines - Intermediate \r\n\r\n### Setting up our intermediate models\r\n\r\n\r\nTo achieve this, we are going to have to create a few more models:\r\n\r\n1. Now let’s create a new intermediate model in the core folder. You can do this by clicking on `New File`. Remove the prewritten path and paste in the following path: \r\nmodels/marts/core/intermediate/int_knoema_stock_history.sql\r\n\r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_1.png)\r\n\r\nThis will create the file as well as the intermediate folder in the core folder to organize the intermediate models. \r\n\r\nIntermediate tables are transformations to get to the end data product but won’t be queried in the downstream processes like in the BI layer. \r\n\r\n2. Paste this code in the model and click “Save”. We are going to use the dbt_utils pivot macro  to transpose the dataset from rows to columns. We have also nested in another dbt_utils macro called get_columns to help us list the column values dynamically. \r\n\r\n3.  Paste this code in the model and save. We are going to use the [dbt_utils pivot macro](https://github.com/dbt-labs/dbt-utils/tree/0.7.1/#pivot-source) to transpose the dataset from rows to columns. We have also nested in another dbt_utils macro called [get_columns](https://github.com/dbt-labs/dbt-utils/tree/0.7.1/#get_column_values-source) to help us list the column values dynamically. \r\n\r\n```sql \r\n\r\nwith stock_history as (\r\n\r\n    select * from {{ ref('stg_knoema_stock_history') }}\r\n        where indicator_name in ('Close', 'Open','High','Low', 'Volume', 'Change %') \r\n\r\n),\r\n\r\npivoted as (\r\n\r\n    select \r\n        company_symbol, \r\n        company_name, \r\n        stock_exchange_name, \r\n        stock_date, \r\n        data_source_name,\r\n        {{ dbt_utils.pivot(\r\n      column = 'indicator_name',\r\n      values = dbt_utils.get_column_values(ref('stg_knoema_stock_history'), 'indicator_name'),\r\n      then_value = 'stock_value'\r\n            ) }}\r\n    \r\n    from stock_history\r\n    group by company_symbol, company_name, stock_exchange_name, stock_date, data_source_name\r\n\r\n)\r\n\r\nselect * from pivoted\r\n \r\n```\r\n\r\n4. Now let’s see what dbt_utils pivot macro compiles to by clicking on the compile button. \r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_2.png)\r\n\r\n5.  Now let’s see what happens if you don’t have the dbt_utils macro to help you write the code. Create a new file in the same intermediate folder: `int_knoema_stock_history_alt.sql`\r\n\r\n6.  Paste below content into the new file and click “Save”. In this model, we will use Snowflake's [pivot](https://docs.snowflake.com/en/sql-reference/constructs/pivot.html) function.\r\n\r\n```sql \r\nwith stock_history as (\r\n \r\n    select * from {{ ref('stg_knoema_stock_history') }}\r\n        where indicator_name IN ('Close', 'Open','High','Low', 'Volume', 'Change %') \r\n \r\n),\r\n \r\npivoted as (\r\n \r\n    select * from stock_history \r\n        pivot(sum(stock_value) for indicator_name in ('Close', 'Open','High','Low', 'Volume', 'Change %')\r\n            ) as \r\n            p(\r\n                company_symbol, \r\n                company_name, \r\n                stock_exchange_name, \r\n                stock_date, \r\n                data_source_name, \r\n                close,\r\n                open,\r\n                high,\r\n                low,\r\n                volume,change\r\n            ) \r\n)\r\n \r\nselect * from pivoted\r\n\r\n```\r\n\r\nWhat did you think of the difference? While both models create the same end result, the [dbt_utils.pivot macro](https://github.com/dbt-labs/dbt-utils#pivot-source) requires less code and less brainpower. All you had to do is fill in the columns and the model to obtain the same end result. It is also more dynamic, automatically updating at compilation if a new value appears. \r\n\r\nYou might have also noticed that in these models we are using a function called [ref](https://docs.getdbt.com/reference/dbt-jinja-functions/ref). This ref function is very similar to sources function but rather than telling dbt how a model relates to a source declared in the sources.yml file, the ref function tells dbt how a model relates to another model. It's useful in the same reasons as sources(): \r\n\r\n- You will be able to automatically generate a DAG and promote your code through environments without having to update the code to change the database object. \r\n- Refs also allow you to run based on dependencies as you will see below.  \r\n\r\nThe main takeaway to remember is in a dbt project, you should never have to hardcode a database object. Always uses [sources()](https://docs.getdbt.com/docs/building-a-dbt-project/using-sources) and [refs()](https://docs.getdbt.com/reference/dbt-jinja-functions/ref)\r\n\r\n7.  Now let’s go ahead and create the new models in Snowflake. In this case, rather than running based on the model file, we will run based on dependencies. Run the following command to execute the int_knoema_stock_history model and its parent models. \r\n\r\n`dbt run --select +int_knoema_stock_history`\r\n\r\nYou should see that we have run all of the nodes (excluding sources) to the left of this lineage. \r\n\r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_3.png)\r\n\r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_4.png)\r\n\r\n\r\n8.  Execute the below code, either in the Snowflake UI or in the dbt Cloud IDE\r\n\r\n\r\n```sql \r\nSELECT * \r\n  FROM pc_dbt_db.\u003Cdev_schema\u003E_marts.int_knoema_stock_history\r\n WHERE company_symbol = 'AAPL'\r\n   AND stock_date = '2021-03-01'\r\n```\r\n\r\n\r\nLooks like we have what we wanted!\r\n\r\nNow let’s take a look at our exchange rates and create some intermediate tables on top of them. \r\n\r\n9.  Create a file called `int_fx_rates.sql` in the intermediate folder (models/marts/core/intermediate/int_fx_rates.sql). \r\n\r\n```sql \r\n{{ \r\nconfig(\r\n    materialized='view', \r\n      tags=[\"hourly\"]\r\n    ) \r\n}}\r\n \r\nselect * from {{ ref('stg_knoema_fx_rates') }} \r\n where indicator_name = 'Close' \r\n   and frequency = 'D' \r\n   and exchange_date \u003E '2016-01-01'\r\n```\r\n\r\nThere's now a model configuration on top of our model. dbt offers [various materialization options](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations). By default, if a materialization is not declared, the model will be created as a view. For this directory (marts) that the model is nested in, we have configured in our `dbt_project.yml` that the default materialization is `table`. We will override the project level materialization with our model configuration, declaring the model to be materialized as a view. \r\nWe also include a [tag](https://docs.getdbt.com/reference/resource-configs/tags). Tags can be used to run parts of your project or a good way to group your models based on content and intent. \r\n\r\n10. Let’s try running this model based on a tag. In the command line, run :\r\n\r\n`dbt run --select tag:hourly`\r\n\r\nBeing able to run models based on tags can be helpful for, say, an hourly job where you are selectively running specific models. \r\n\r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_5.png)\r\n\r\n11.  Now create the file `int_stock_history_major_currency.sql` in the intermediate folder. This model will start bringing FX and Trade history sets together.\r\n\r\n12.  Paste the below code into the file and save it.\r\n\r\n```sql\r\nwith\r\nstock_history as (\r\n    select * from {{ ref('int_knoema_stock_history')}}\r\n),\r\n \r\nfx_rates as (\r\n    select * from {{ ref('int_fx_rates') }}\r\n),\r\n \r\nfx_rates_gdp as (\r\n    select * from fx_rates\r\n        where currency = 'USD/GBP'   \r\n),\r\n \r\nfx_rates_eur as (\r\n    select * from fx_rates\r\n        where currency = 'USD/EUR' \r\n),\r\n \r\njoined as (\r\n    select \r\n        stock_history.*,\r\n        fx_rates_gdp.exchange_value * stock_history.\"Open\" as gbp_open,       \r\n        fx_rates_gdp.exchange_value * stock_history.\"High\" as gbp_high,     \r\n        fx_rates_gdp.exchange_value * stock_history.\"Low\" as gbp_low,   \r\n        fx_rates_gdp.exchange_value * stock_history.\"Close\" as gbp_close,     \r\n        fx_rates_eur.exchange_value * stock_history.\"Open\" as eur_open,       \r\n        fx_rates_eur.exchange_value * stock_history.\"High\" as eur_high,     \r\n        fx_rates_eur.exchange_value *stock_history.\"Low\" as eur_low,\r\n        fx_rates_eur.exchange_value * stock_history.\"Close\" as eur_close    \r\n    from stock_history\r\n    left join fx_rates_gdp on stock_history.stock_date = fx_rates_gdp.exchange_date\r\n    left join fx_rates_eur on stock_history.stock_date = fx_rates_eur.exchange_date\r\n)\r\n \r\nselect * from joined\r\n```\r\n13. Now, let's deploy newly built models by typing the following into the command line.\r\n`dbt run --select +int_stock_history_major_currency`\r\n\r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_6.png)\r\n\r\n \r\n#### Generate Documentation Site\r\n\r\n\r\nNow that we have created a few models, let’s talk about [dbt documentation](https://docs.getdbt.com/docs/building-a-dbt-project/documentation). \r\ndbt is able to generate a static webpage with a data dictionary by pulling in information from your dbt project as well as your Snowflake information_schema. It also provides an interactive DAG so you can see the full lineage of your models; this is something you might have also noticed  in the lineage tab of the IDE. This is a fantastic way to share  information with your internal teams, as it contains all-important information about columns, tags, free-form model description, tests as well as the source code that is always in line with the code. So regardless of how big your project grows, it is super easy to understand what's happening with the help of dbt’s documentation. \r\n\r\n1.  To generate your documentation site, execute “dbt docs generate” on the command line:\r\n\r\n`dbt docs generate`\r\n\r\n14. Click on `View Docs` on the top left side.\r\n\r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_7.png)\r\n\r\n\r\n15.  Take a look around on the docs site. You can check out the project lineage by clicking on the DAG button on the bottom. Explore the page and think about what information you would want to provide to your stakeholders and teammates. \r\n\r\n\r\n![intermediate](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_8.png) \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## dbt pipelines - Seeds\r\n\r\nFollowing our use case story, we are going to manually upload two small datasets using [dbt seed](https://docs.getdbt.com/docs/building-a-dbt-project/seeds) representing trading books of two desks. As you might notice, they were buying and selling AAPL shares, but logging the cash paid/received in different currencies: USD and GBP.\r\n\r\n1. Create a new file in the data folder called `manual_book1.csv`.\r\n\r\n![seed](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/seed_1.png) \r\n\r\n2. Paste the following code in the file and save it.\r\n\r\n```\r\nBook,Date,Trader,Instrument,Action,Cost,Currency,Volume,Cost_Per_Share,Stock_exchange_name\r\nB2020SW1,2021-03-03,Jeff A.,AAPL,BUY,-17420,GBP,200,87.1,NASDAQ\r\nB2020SW1,2021-03-03,Jeff A.,AAPL,BUY,-320050,GBP,3700,86.5,NASDAQ\r\nB2020SW1,2021-01-26,Jeff A.,AAPL,SELL,52500,GBP,-500,105,NASDAQ\r\nB2020SW1,2021-01-22,Jeff A.,AAPL,BUY,-100940,GBP,980,103,NASDAQ\r\nB2020SW1,2021-01-22,Nick Z.,AAPL,SELL,5150,GBP,-50,103,NASDAQ\r\nB2020SW1,2019-08-31,Nick Z.,AAPL,BUY,-9800,GBP,100,98,NASDAQ\r\nB2020SW1,2019-08-31,Nick Z.,AAPL,BUY,-1000,GBP,50,103,NASDAQ\r\n```\r\n\r\n3. Create another file `manual_book2.csv` in the data folder.\r\n\r\n\r\n4. Paste the following code in the file and save it.\r\n\r\n```\r\nBook,Date,Trader,Instrument,Action,Cost,Currency,Volume,Cost_Per_Share,Stock_exchange_name\r\nB-EM1,2021-03-03,Tina M.,AAPL,BUY,-17420,EUR,200,87.1,NASDAQ\r\nB-EM1,2021-03-03,Tina M.,AAPL,BUY,-320050,EUR,3700,86.5,NASDAQ\r\nB-EM1,2021-01-22,Tina M.,AAPL,BUY,-100940,EUR,980,103,NASDAQ\r\nB-EM1,2021-01-22,Tina M.,AAPL,BUY,-100940,EUR,980,103,NASDAQ\r\nB-EM1,2019-08-31,Tina M.,AAPL,BUY,-9800,EUR,100,98,NASDAQ\r\n```\r\n\r\n5. Now let's run the `dbt seed` command to load the data into Snowflake. It is important to mention that while it is absolutely possible with this approach to bring low hundred-thousands of rows, it was not created for larger datasets and you should be using COPY/Snowpipe or other data integration options recommended for Snowflake in such cases.\r\n\r\n`dbt seed`\r\n\r\nYou can see that it loaded the files in the default schema because we didn’t declare a schema for them. If we wanted to make sure the files were loaded into a specific file, we could go back to the dbt_project.yml and add that [schema configuration](https://docs.getdbt.com/reference/seed-configs). \r\n\r\n![seed](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/seed_2.png) \r\n\r\n6. Commit your work. \r\n\r\n7. Now let’s model the seeded data by unioning them together. In this example we are going to see how [dbt_utils.union_relations](https://github.com/dbt-labs/dbt-utils#union_relations-source) macro helps us write the necessary code. Create the file `int_unioned_book.sql` in the intermediate folder. \r\n\r\n8. Paste the code below into the new file and save it.\r\n\r\n```sql \r\nwith \r\nunioned as (\r\n    {{ dbt_utils.union_relations(\r\n        relations=[ref('manual_book1'), ref('manual_book2')]\r\n    ) }}\r\n \r\n),\r\n \r\nrenamed as (\r\n    select      \r\n        Book,\r\n        Date as book_date,\r\n        Trader,\r\n        Instrument,\r\n        Action as book_action,\r\n        Cost,\r\n        Currency,\r\n        Volume,\r\n        Cost_Per_Share,\r\n        Stock_exchange_name\r\n    from unioned \r\n)\r\n \r\nselect * from renamed\r\n```\r\n\r\n9. Before we deploy this model, let's have a look at what it is compiled into. You can do this by clicking on the compile button. \r\n\r\n![seed](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/seed_3.png) \r\n \r\nAs you can see,  the macro from the dbt_utils package wrote the code, aligned the attributes by name and type, and combined the datasets via a UNION ALL. The beauty of this is, we were able to write 46 lines of code with only 3.  It’s also dynamic so if we were to add new columns to our referenced models, dbt would update the compile code with the new fields. You can imagine the amount of time saved. \r\n\r\n10. Now let’s create the object by running:\r\n \r\n`dbt run --select int_unioned_book`\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## dbt pipelines - Intermediate Part 2\r\n \r\n1. Next challenge! We have a great log of trading activities, but it only provides records when shares were bought or sold. Ideally, to make the daily performance analysis more meaningful, we should have rows for the days shares were held as well. To do this, let’s make this new file: `int_daily_position.sql` in the intermediate folder. \r\n\r\n2. Paste below into the new file and save it.\r\n\r\n```sql \r\nwith \r\nstock_history as (\r\n    select * from {{ ref('int_stock_history_major_currency') }} \r\n), \r\n\r\nunioned_book as (\r\n    select * from {{ ref('int_unioned_book') }}\r\n),\r\n\r\ncst_market_days as (\r\n    select distinct stock_date\r\n        from stock_history\r\n        where stock_history.stock_date \u003E= (select min(book_date) as min_dt from  unioned_book)\r\n),\r\n\r\njoined as (\r\n    select \r\n        cst_market_days.stock_date,\r\n        unioned_book.trader,\r\n        unioned_book.stock_exchange_name,\r\n        unioned_book.instrument,\r\n        unioned_book.book,\r\n        unioned_book.currency,\r\n        sum(unioned_book.volume) as total_shares\r\n    from cst_market_days\r\n    inner join unioned_book on unioned_book.book_date = cst_market_days.stock_date\r\n    where unioned_book.book_date \u003C= cst_market_days.stock_date\r\n    {{ dbt_utils.group_by(6) }}\r\n)\r\n\r\nselect * from joined\r\n\r\n```\r\n\r\nYou might have noticed we used another dbt_utils macro. Rather than having to manually list out all of the columns to group by, you can use the [dbt_utils group_by macro](https://github.com/dbt-labs/dbt-utils#group_by-source) to write it for you. \r\n\r\n3. Now let’s group the daily position with trades. Create the file `int_daily_position_with_trades.sql` in the intermediate folder. \r\n\r\n\r\n4. Paste the following code in the new file and save it.\r\n\r\n```sql \r\n\r\nwith unioned_book as (\r\n    select * from {{ ref('int_unioned_book') }}\r\n),\r\n \r\ndaily_position as (\r\n    select * from {{ ref('int_daily_position') }}\r\n),\r\n \r\nunioned as (\r\n    select \r\n        book,\r\n        book_date,\r\n        trader,\r\n        instrument,\r\n        book_action,\r\n        cost, \r\n        currency,\r\n        volume, \r\n        cost_per_share, \r\n        stock_exchange_name,\r\n        sum(unioned_book.volume) \r\n            over(\r\n                partition by \r\n                    instrument, \r\n                    stock_exchange_name, \r\n                    trader \r\n                order by \r\n                    unioned_book.book_date rows unbounded preceding) \r\n                        as total_shares\r\n    from unioned_book  \r\n \r\n    union all \r\n \r\n    select  \r\n        book,\r\n        stock_date as book_date, \r\n        trader, \r\n        instrument, \r\n        'HOLD' as book_action,\r\n        0 as cost,\r\n        currency, \r\n        0 as volume, \r\n        0 as cost_per_share,\r\n        stock_exchange_name,\r\n        total_shares\r\n    from daily_position\r\n    where (book_date,trader,instrument,book,stock_exchange_name) \r\n        not in \r\n        (select book_date,trader,instrument,book,stock_exchange_name\r\n            from unioned_book\r\n        )\r\n)\r\n \r\nselect * from unioned\r\n\r\n```\r\n\r\n5. Execute the following code to build int_unioned_book and it’s child models.\r\n\r\n`dbt run --select int_unioned_book+`\r\n\r\n\r\nNow that we have created all of these models, we are able to attribute shares by trader.\r\n\r\n![int](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_9.png) \r\n\r\n\r\n6. Commit the work. \r\n\r\n7. Run the following code in either the dbt Cloud IDE or a Snowflake worksheet. \r\n\r\n```sql\r\nselect * \r\nfrom pc_dbt_db.\u003Cdev_schema\u003E_marts.int_daily_position_with_trades\r\nwhere trader = 'Tina M.'\r\norder by  book_date\r\n```\r\n\r\n![int](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/int_10.png) \r\n\r\n \r\n\u003C!-- ------------------------ --\u003E\r\n## dbt pipelines - Facts\r\n\r\n#### dbt pipelines - PnL calculation\r\nNow for the last models in our tale. We have the trading history of our desks and our stock price history. \r\n\r\nLet's create a model to show how Market Value and PnL changed over time. You might notice that this is going into our core folder. This is because this is a part of the core logic of our fictional company. This model will be a source of truth, to be used by many in downstream processes like a BI tool. \r\n\r\n1. Create the file `fct_trading_pnl.sql` in the core folder. \r\n\r\n2. Copy the following code to the new file and save it.\r\n\r\n```sql \r\n{{ \r\nconfig(\r\n      tags = 'core'\r\n      ) \r\n}}\r\n\r\n\r\nwith\r\ndaily_positions as (\r\n    select * from {{ ref('int_daily_position_with_trades' )}}\r\n\r\n),\r\n\r\nstock_history as (\r\n    select * from {{ ref('int_stock_history_major_currency') }}\r\n\r\n),\r\n\r\njoined as (\r\n    select \r\n        daily_positions.instrument, \r\n        daily_positions.stock_exchange_name, \r\n        daily_positions.book_date, \r\n        daily_positions.trader, \r\n        daily_positions.volume,\r\n        daily_positions.cost, \r\n        daily_positions.cost_per_share,\r\n        daily_positions.currency,\r\n        sum(cost) over(\r\n                partition by \r\n                    daily_positions.instrument, \r\n                    daily_positions.stock_exchange_name, \r\n                    trader \r\n                order by\r\n                    daily_positions.book_date rows unbounded preceding \r\n                    )\r\n                as cash_cumulative,\r\n       case when daily_positions.currency = 'GBP' then gbp_close\r\n            when daily_positions.currency = 'EUR' then eur_close\r\n            else 'Close'\r\n       end AS close_price_matching_ccy, \r\n       daily_positions.total_shares  * close_price_matching_ccy as market_value, \r\n       daily_positions.total_shares  * close_price_matching_ccy + cash_cumulative as PnL\r\n   from daily_positions\r\n   inner join stock_history \r\n      on daily_positions.instrument = stock_history.company_symbol \r\n     and stock_history.stock_date = daily_positions.book_date \r\n     and daily_positions.stock_exchange_name = stock_history.stock_exchange_name\r\n)\r\n\r\nselect * from joined \r\n```\r\n\r\n3. Run the model with `dbt run --select fct_trading_pnl.sql`\r\n\r\nYou might have noticed that fct_trading_pnl takes some time to build when you do a dbt run. As we use larger and larger datasets, the run times are getting longer. We have already materialized our models as a table, meaning we are preemptively front loading our run times to save on the query times when the table is queried. We could always increase our warehouse size, but there’s another trick up our sleeve we won’t touch on just yet. \r\n\r\nWe can materialize the model as an incremental model, meaning that it will not be rebuilt each time, but rather only the latest rows transformed and added to the existing table. \r\nAll you have to do is update the model configuration and include the is_incremental macro which comes into action for the incremental runs (and is ignored during initial run and [full_refresh](https://docs.getdbt.com/reference/resource-configs/full_refresh) option). You can learn more about this [incremental materialization here](http://greatexpectations.io/).\r\n\r\n4. Let’s try out an incremental model. Create a new file `fct_trading_pnl_incremental.sql` in the core folder. \r\n\r\n5. Copy the following code to the new file and save it.\r\n\r\n```sql\r\n{{ \r\nconfig(\r\n      materialized='incremental',\r\n      unique_key= 'pk_key',\r\n      tags = 'core'\r\n      ) \r\n}}\r\n \r\n \r\nwith \r\ndaily_positions as (\r\n    select * from {{ ref('int_daily_position_with_trades' )}}\r\n),\r\n \r\nstock_history as (\r\n    select * from {{ ref('int_stock_history_major_currency') }}\r\n),\r\n \r\njoined as (\r\n    select \r\n \r\n        daily_positions.instrument, \r\n        daily_positions.stock_exchange_name, \r\n        daily_positions.book_date, \r\n        daily_positions.trader, \r\n        daily_positions.volume,\r\n        daily_positions.cost, \r\n        daily_positions.cost_per_share,\r\n        daily_positions.currency,\r\n        sum(cost) over(\r\n                partition by \r\n                    daily_positions.instrument, \r\n                    daily_positions.stock_exchange_name, \r\n                    trader \r\n                order by\r\n                    daily_positions.book_date rows unbounded preceding \r\n                    )\r\n                as cash_cumulative,\r\n       case when daily_positions.currency = 'GBP' then gbp_close\r\n            when daily_positions.currency = 'EUR' then eur_close\r\n            else 'Close'\r\n       end as close_price_matching_ccy, \r\n       daily_positions.total_shares  * close_price_matching_ccy as market_value, \r\n       daily_positions.total_shares  * close_price_matching_ccy + cash_cumulative as PnL\r\n   from daily_positions\r\n   inner join stock_history \r\n      on daily_positions.instrument = stock_history.company_symbol \r\n     and stock_history.stock_date = daily_positions.book_date \r\n     and daily_positions.stock_exchange_name = stock_history.stock_exchange_name\r\n),\r\n \r\nprimary_key as (\r\n \r\n    select \r\n \r\n        {{ dbt_utils.surrogate_key([\r\n                'trader', \r\n                'instrument', \r\n                'book_date', \r\n                'stock_exchange_name',\r\n                'PnL', \r\n            ]) }} as pk_key,\r\n                *\r\n \r\n    from joined \r\n)\r\n \r\nselect * from primary_key\r\n \r\n{% if is_incremental() %}\r\n  -- this filter will only be applied on an incremental run\r\n   where book_date \u003E (select max(book_date) from {{ this }})\r\n \r\n{% endif %}\r\n\r\n```\r\n\r\n6. Now let’s see how differently this model acts in the first run versus subsequent runs. \r\nRun `dbt run --select fct_trading_pnl_incremental` twice and compare the logs. \r\n \r\nFor the first run, you will notice that the where clause does not come in into the sql statement.\r\n\r\nIn subsequent runs, dbt is including the where clause to create a temporary table to then merge into the existing table. \r\nThe first run of an incremental model will builds the table that will then new rows will be added to in subsequent runs. \r\n\r\nFirst run: \r\n![fct](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/fct_1.png) \r\n\r\nSecond run:\r\n![fct](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/fct_2.png) \r\n\r\nYou will not see a true performance improvement in this example because our data is not loading exponentially but in production applications, you will. \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## dbt pipelines - Tests & Docs\r\n\r\n### Testing & Documentation \r\n\r\n\r\nTo build trust in data with your organization, it is impossible to overstate the importance of testing and documentation. While there are many ways to organize automated testing and documentation, dbt comes with an accessible [data testing](https://docs.getdbt.com/docs/building-a-dbt-project/tests) and [documentation](https://docs.getdbt.com/docs/building-a-dbt-project/documentation) framework. \r\n\r\nFor tests, dbt comes with a set of 4 pre-defined data tests: \r\n- uniqueness\r\n- not_null\r\n- check constraints\r\n- relationship integrity\r\n\r\nWe are going to set up tests on a few models for this workshop. However, we strongly recommend you establish reasonable test coverage across the board. Adding your own tests is easy; as long as it can be written as a select statement, you can use it as a test. And just like with macros, there are plenty of fantastic packages out there like the [dbt_expectations](https://hub.getdbt.com/calogica/dbt_expectations/latest/) package that provides tests similar to the [Great Expectations](http://greatexpectations.io/) open source project.\r\n\r\nFor documentation, dbt takes model and column descriptions and will add them to the documentation site so that you have even more information about your models to share with your stakeholders via the site. It also pulls in information from the Snowflake information schema for a one stop show about your data project.\r\n\r\nNow let's add testing and documentation to this project. \r\n\r\n1. First, create this file `intermediate.yml` in the intermediate folder. \r\n\r\n2. Copy the following code into the new file and save it.\r\n\r\n```yml\r\nversion: 2\r\n \r\nmodels:\r\n  - name: int_fx_rates\r\n    description: “An intermediate model that filters stg_knoema_fx_rates\"\r\n    columns:\r\n      - name: currency||exchange_date\r\n        tests:\r\n          - unique\r\n          - not_null\r\n \r\n  - name: int_unioned_book\r\n    description: “An intermediate model unions the manual_book csvs\"\r\n    columns:\r\n      - name: instrument\r\n        tests:\r\n          - not_null\r\n          - relationships:\r\n              to: ref('int_knoema_stock_history')\r\n              field: company_symbol\r\n \r\n  - name: int_knoema_stock_history\r\n    description: \"An intermediate model that pivots the stg_knoema_stock_history model by indicator\"\r\n    columns:\r\n      - name: company_symbol||stock_date\r\n        tests:\r\n          - not_null\r\n          - unique\r\n```\r\n\r\n![test](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_1.png) \r\n \r\n3. Execute the following code on the command line:  \r\n`dbt test`\r\n\r\n![test](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_2.png) \r\n\r\nOh no! One of our tests failed! Let's try to understand why. If you click into the details tab, dbt provides you with the SQL query that failed. \r\n\r\n![test](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_4.png) \r\n\r\nNow let’s take that query and paste it either into the Snowflake UI or the IDE to debug it. \r\n \r\n\r\n4. Execute the following query to check the full row width for one of the records failed either in the IDE or the Snowflake UI (remember to update the dev_schema!): \r\n\r\n```sql \r\nwith cst as\r\n(\r\n    select\r\n        company_symbol||stock_date as conctat\r\n    from \u003Cdev_schema\u003E_marts.int_knoema_stock_history\r\n    where company_symbol||stock_date is not null\r\n    group by company_symbol||stock_date\r\n    having count(*) \u003E 1 \r\n    limit 1\r\n)\r\n \r\nselect * from \u003Cdev_schema\u003E_marts.int_knoema_stock_history\r\nwhere company_symbol||stock_date IN (SELECT conctat FROM cst)\r\n\r\n```\r\n\r\n![test](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_3.png) \r\n \r\nAha! There are shares which are traded on more than one stock exchanges. \r\nSo we need to include the stock_exchange_name attribute to the unique test key. \r\n\r\n4. Let's go back to `intemediate.yml` file and update the test configuration for int_knoema_stock_history model :\r\n\r\n```yml\r\nversion: 2\r\n\r\nmodels:\r\n  - name: int_fx_rates\r\n    description: “An intermediate model that filters stg_knoema_fx_rates\"\r\n    columns:\r\n      - name: currency||exchange_date\r\n        tests:\r\n          - unique\r\n          - not_null\r\n\r\n  - name: int_unioned_book\r\n    description: “An intermediate model unions the manual_book csvs\"\r\n    columns:\r\n      - name: instrument\r\n        tests:\r\n          - not_null\r\n          - relationships:\r\n              to: ref('int_stock_history')\r\n              field: company_symbol\r\n\r\n  - name: int_knoema_stock_history\r\n    description: \"An intermediate model that pivots the stg_knoema_stock_history model by indicator\"\r\n    columns:\r\n      - name: company_symbol||stock_date||stock_exchange_name\r\n        tests:\r\n          - not_null\r\n          - unique\r\n```\r\n5. Now let’s run only the previously failing test. \r\n\r\n`dbt test -m  int_knoema_stock_history`\r\n\r\n\r\n![test](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/test_5.png) \r\n \r\nLook at all that green! \r\nNow the last thing to do is to generate the documentation and see how our description fields are populated. \r\n\r\n6. Run the command `dbt docs generate` and click on view docs. \r\n\r\n7. Search for one of the models that we applied descriptions to either via the file tree or the search bar. \r\n\r\n![docs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/docs_1.png) \r\n\r\nYou should now see the description field populated. \r\n\r\n![docs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/docs_2.png) \r\n \r\n\u003C!-- ------------------------ --\u003E\r\n## dbt pipelines - Deployment\r\n\r\nOkay, it seems like we have everything in place: pipelines have been developed, tested and documented.  \r\n\r\nThe next step would be to promote this code up the chain through our environments (which in this lab are simplified to just DEV & PROD).\r\nWe’ll do this by committing the work to our feature branch into the main branch and setting up a job to orchestrate the execution of the models in production. Generally it would be best for a pull request to be opened, so that your code is reviewed and tested via [Slim CI](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration-with-github#slim-ci) prior to promotion to production, but in the interest of keeping things concise for this workshop, we are going to merge into production without review. \r\n \r\n1. To do this, the first thing you are going to do is commit any remaining work you haven’t yet. \r\n\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_1.png) \r\n\r\n2. Now we are going to click merge to master\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_2.png) \r\n\r\nBy merging into master, you should have reverted back to your master branch for you to start the cycle over again for development. \r\n\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_3.png) \r\n \r\n3. Now we are going to deploy our code via dbt Cloud. Luckily for you, Partner Connection has already created your Production Environment for you. Now all we have to do is update some defaults. \r\n\r\nClick on the hamburger menu and click on `Environments`.  \r\n\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_4.png) \r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_5.png) \r\n\r\nYou should see two different Environments: Development and Deployment. \r\n\r\n \r\n4. Click on the `Deployment` environment and then click on `Settings` in the top right. \r\n\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_6.png) \r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_7.png) \r\n \r\n5. We are going to update the schema name from your development schema to `production`. To do this, click on Edit. \r\n\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_8.png) \r\n\r\n\r\n\r\n6. After you update the field, be sure to click on `Save` at the top.\r\nNow in doing this, dbt will default to building in a schema named `production` in the declared default database pc_dbt_db. This helps us separate out the objects we create in our production environment from our development sandbox. \r\n\r\n7. After saving your environment, let’s update the default Partner Connect created job. To do this, click on the hamburger menu on the top left and click on `Jobs`. \r\n\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_9.png) \r\n\r\n8. Now select the pre-configured job named `Partner Connect Trial Job`. \r\n\r\n9. Now let’s edit it by first going to `Settings`. \r\n\r\n10. Click on `Edit` on the top right. \r\n\r\n11. And now we are going to update a few things. Change the name of the Job to `Production Job` to signify that this is going to be your production run. \r\nUpdate the number of [threads](https://docs.getdbt.com/dbt-cli/configure-your-profile#understanding-threads) to 8. This will allow dbt to run 8 dbt models in the DAG concurrently sans dependencies. 8 is the default we recommend for Snowflake. \r\n\r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_10.png) \r\n\r\n12. Because the dbt commands section already include the sequence of commands that we need things to operate, we are going to keep it as is. We want to seed our manual uploads to our production schema first. Then we want to create our objects in our schema prior to running tests on them. \r\nWe are also going to leave the scheduling on it’s default off configuration because we will be manually kicking off the job for this workshop. \r\n12. Now go to the top and click `Save`. \r\n13. Then click on the job name in the path on top. This will return you to the job run history page, from where you can click on `Run now`. \r\n14. Click into the run to check out how the run is going. Your run should be successful like this after completion. \r\n![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_11.png) \r\n\r\n \r\n15. Take a look at the various run steps. You can validate that on the dbt run step, you are building into the prod schemas as noted in our environment configuration. \r\n \r\n16. Verify  in Snowflake UI that you now we have data in the production schema:\r\n \r\n ![deploy](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-cloud/deploy_12.png) \r\n\r\n \r\nAnd this concludes our workshop! If you’re interested in exploring further, there are “extra credit” tasks in the appendix of this workshop, in which you’ll cover things like how to visualize what you’ve created with Snowsight.\r\n \r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion & Next Steps\r\n\r\nCongratulations on completing the lab! \r\n\r\nToday, you learned how to use dbt and Snowflake to build data transformation pipelines for analytics. You’re now ready to apply these fundamentals to your own data. \r\nWe encourage you to continue with your free trial by loading your own sample or production data, and by continuing to dive into some of the more advanced functionality of dbt Cloud and Snowflake.\r\n\r\n#### What we've covered:\r\n- How to set up dbt & Snowflake\r\n- How to leverage data in Snowflake's Data Marketplace\r\n- How to run a dbt project and develop pipelines\r\n- How to create data tests and documentation \r\n\r\n#### Additional Resources:\r\n\r\n- Join our [dbt community Slack ](https://community.getdbt.com/) which contains more than 18,000 data practitioners today. We have a dedicated slack channel #db-snowflake to Snowflake related content. \r\n- To continue to learn to use dbt more effectively, check out the [dbt Learn site](https://learn.getdbt.com/).\r\n- Contact the [dbt Cloud Sales team](https://www.getdbt.com/contact/) if you’re interested in exploring dbt Cloud for your team or organization.\r\n \r\n \r\n\u003C!-- ------------------------ --\u003E\r\n## Appendix\r\n\r\n### Visualizations\r\n\r\n\r\n1. If you want to create a simple data visualization for this dataset, we can use Snowsight. To do that, let's click on the Preview App button once again:\r\n\r\n2. Enable the Dashboards and worksheets. \r\n\r\n3. Then click Worksheets -\u003E + Worksheet to add a new one.\r\n\r\n4. Copy and paste the following query in the worksheet.\r\n \r\n```sql \r\nselect * \r\nfrom pc_dbt_db.\u003Cdev_schema\u003E_marts.int_daily_position_with_trades\r\nwhere trader = 'Tina M.'\r\norder by  book_date\r\n```\r\n5. Hit the run button and switch from a table view to chart.\r\n\r\n \r\n6. By default it shows a breakdown by Volume. Click on the measure and switch it into PNL. \r\n\r\n \r\n7. Click “+Add column” to add another measure and select “Market Value” to display Market value and PnL side by side.\r\n \r\n\r\nAnd that’s it! Now you have a worksheet that you can slice and dice, share with your colleagues or embed in the SnowSight dashboard as a tile. 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