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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-d6368bee6a","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"External Collaboration","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/external-collaboration","tagIcon":""},"isDeveloperGuidesPage":false,"quickstartHeroAuthor":"Dmytro 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Snowflake is Data Cloud, a future proof solution that can simplify data pipelines for all your businesses so you can focus on your data and analytics instead of infrastructure management and maintenance.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.getdbt.com/\"\u003Edbt\u003C/a\u003E is a modern data engineering framework maintained by \u003Ca href=\"https://www.getdbt.com/dbt-labs/about-us/\"\u003Edbt Labs\u003C/a\u003E that is becoming very popular in modern data architectures, leveraging cloud data platforms like Snowflake. \u003Ca href=\"https://docs.getdbt.com/dbt-cli/cli-overview\"\u003Edbt CLI\u003C/a\u003E is the open-source version of dbtCloud that is providing similar functionality, but as a SaaS.\nIn this virtual hands-on lab, you will follow a step-by-step guide to Snowflake and dbt to see some of the benefits this tandem brings.\u003C/p\u003E\n","\u003Cp\u003ELet’s get started.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Cp\u003ETo participate in the virtual hands-on lab, attendees need the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA \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\u003EFamiliarity with Snowflake and Snowflake objects\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need During the Lab\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.getdbt.com/dbt-cli/installation\"\u003Edbt CLI\u003C/a\u003E installed\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EText editor of your choice\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 leverage data in Snowflake's Data Marketplace\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to build scalable pipelines using dbt & Snowflake\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA set of data analytics pipelines for Financial Services data leveraging dbt and Snowflake\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EImplement data quality tests\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPromote code between the environments\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\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-core/image124.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.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image29.png\" alt=\"Snowflake Worksheets\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's now create a database and a service accounts for dbt.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-------------------------------------------\n-- dbt credentials\n-------------------------------------------\nUSE ROLE securityadmin;\n-- dbt roles\nCREATE OR REPLACE ROLE dbt_dev_role;\nCREATE OR REPLACE ROLE dbt_prod_role;\n------------------------------------------- Please replace with your dbt user password\nCREATE OR REPLACE USER dbt_user PASSWORD = "<mysecretpassword>";\n\nGRANT ROLE dbt_dev_role,dbt_prod_role TO USER dbt_user;\nGRANT ROLE dbt_dev_role,dbt_prod_role TO ROLE sysadmin;\n\n-------------------------------------------\n-- dbt objects\n-------------------------------------------\nUSE ROLE sysadmin;\n\nCREATE OR REPLACE WAREHOUSE dbt_dev_wh  WITH WAREHOUSE_SIZE = 'XSMALL' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\nCREATE OR REPLACE WAREHOUSE dbt_dev_heavy_wh  WITH WAREHOUSE_SIZE = 'LARGE' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\nCREATE OR REPLACE WAREHOUSE dbt_prod_wh WITH WAREHOUSE_SIZE = 'XSMALL' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\nCREATE OR REPLACE WAREHOUSE dbt_prod_heavy_wh  WITH WAREHOUSE_SIZE = 'LARGE' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\n\nGRANT ALL ON WAREHOUSE dbt_dev_wh  TO ROLE dbt_dev_role;\nGRANT ALL ON WAREHOUSE dbt_dev_heavy_wh  TO ROLE dbt_dev_role;\nGRANT ALL ON WAREHOUSE dbt_prod_wh TO ROLE dbt_prod_role;\nGRANT ALL ON WAREHOUSE dbt_prod_heavy_wh  TO ROLE dbt_prod_role;\n\nCREATE OR REPLACE DATABASE dbt_hol_dev; \nCREATE OR REPLACE DATABASE dbt_hol_prod; \nGRANT ALL ON DATABASE dbt_hol_dev  TO ROLE dbt_dev_role;\nGRANT ALL ON DATABASE dbt_hol_prod TO ROLE dbt_prod_role;\nGRANT ALL ON ALL SCHEMAS IN DATABASE dbt_hol_dev   TO ROLE dbt_dev_role;\nGRANT ALL ON ALL SCHEMAS IN DATABASE dbt_hol_prod  TO ROLE dbt_prod_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs result of these steps, we should have:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Etwo empty databases: PROD, DEV\u003C/li\u003E\u003Cli\u003Etwo pair of virtual warehouses: two for prod, two for dev workloads\u003C/li\u003E\u003Cli\u003Ea pair of roles and one user\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EPlease note, this set up is simplified for the purpose of the lab.\nThere are many ways environments, roles, credentials could be modeled to fit your final requirements.\u003C/p\u003E\n","\u003Cp\u003EWe would suggest having a look at these articles for inspiration: \u003Ca href=\"https://blog.getdbt.com/how-we-configure-snowflake/\"\u003EHow we configure Snowflake by the dbt Labs Team\u003C/a\u003E, \u003Ca href=\"https://about.gitlab.com/handbook/business-technology/data-team/platform/dbt-guide/#model-structure\"\u003EModel Structure by GitLab team\u003C/a\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt Configuration\u003C/h2\u003E\n","\u003Ch3\u003EInitialize dbt project\u003C/h3\u003E\n","\u003Cp\u003ECreate a new dbt project in any local folder by running the following commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Shell\"\u003E$ dbt init dbt_hol\n$ cd dbt_hol\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure dbt/Snowflake profiles\u003C/h3\u003E\n","\u003Cp\u003E1.. Open  \u003Ccode\u003E~/.dbt/profiles.yml\u003C/code\u003E in text editor and add the following section\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Edbt_hol:\n  target: dev\n  outputs:\n    dev:\n      type: snowflake\n      ######## Please replace with your Snowflake account name\n      account: <your_snowflake_trial_account>\n      \n      user: dbt_user\n      ######## Please replace with your Snowflake dbt user password\n      password: <mysecretpassword>\n      \n      role: dbt_dev_role\n      database: dbt_hol_dev\n      warehouse: dbt_dev_wh\n      schema: public\n      threads: 200\n    prod:\n      type: snowflake\n      ######## Please replace with your Snowflake account name\n      account: <your_snowflake_trial_account>\n      \n      user: dbt_user\n      ######## Please replace with your Snowflake dbt user password\n      password: <mysecretpassword>\n      \n      role: dbt_prod_role\n      database: dbt_hol_prod\n      warehouse: dbt_prod_wh\n      schema: public\n      threads: 200\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E2..  Open \u003Ccode\u003Edbt_project.yml\u003C/code\u003E (in dbt_hol folder) and update the following sections:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image3.png\" alt=\"dbt_project.yml\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EValidate the configuration\u003C/h3\u003E\n","\u003Cp\u003ERun the following command (in dbt_hol folder):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Shell\"\u003E$ dbt debug\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe expected output should look like this, confirming that dbt was able to access the database:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image4.png\" alt=\"dbt debug output\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETest run\u003C/h3\u003E\n","\u003Cp\u003EFinally, let's run the sample models that comes with dbt templates by default to validate everything is set up correctly.\nFor this, please run the following command (in dbt_hol folder):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-Shell\"\u003E$ dbt run\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe expected output should look like this, confirming dbt was able to connect and successfully run sample models:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image5.png\" alt=\"dbt run output\"\u003E\u003Cbr\u003E\nPlease note, this operation is completely rerunable and does not provide any harm to our next steps in the lab.\u003C/p\u003E\n","\u003Cp\u003EYou can use Snowflake worksheets to validate that the sample view and the table are now available in DEV database:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image6.png\" alt=\"Snowflake UI\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You just run your first dbt models on Snowflake!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EArchitecture and Use Case Overview\u003C/h2\u003E\n","\u003Cp\u003EIn this lab, we are going to analyze 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 that is available in Snowflake Marketplace, plus few manual uploads.\u003C/p\u003E\n","\u003Cp\u003EWe are going to set up the environments from scratch, build scalable pipelines in dbt, establish data tests, and Snowflake and promote code to production.  Finally we will use Snowsight to build a simple dashboard to visualize the results.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image7.png\" alt=\"Architecture \"\u003E\u003C/p\u003E\n","\u003Cp\u003EJust to give you a sneak peek, this is where we are going to be in just 30 minutes.\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-core/image21.png\" alt=\"dbt target view \"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect to Data Sources\u003C/h2\u003E\n","\u003Cp\u003ELet's go to the Snowflake Marketplace and find what we need. The Data Marketplace lives in the new UI called Snowsight (currently in Preview mode but feel free to test drive after the lab). Click on Preview App at the top of the UI\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image9.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick Sign in to continue. You will need to use the same user and password that you used to login to your Snowflake account the first time.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image11.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou're now in the new UI - Snowsight. It's pretty cool - with charting and dashboards and context-sensitivity - but today we're just focused on getting to the Data Marketplace. Click on Data...\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image14.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003E...and then Marketplace...\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image12.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003E..and now you're in! Hundreds of providers have made datasets available for you to enrich your data. Today we're going to grab a Knoema Economy Atlas Data. Click the Ready to Query checkbox and then find the Knoema Economy Atlas Data tile. Once you find it, click on it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image13.png\" alt=\"Preview App\"\u003E\u003Cbr\u003E\nHere you'll find a description of the data, example queries, and other useful information. Let's get this data into our Snowflake account. You'll be amazed at how fast and easy this is. Click the "Get Data" button\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image15.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the pop-up, leave the database name as proposed by default (important!), check the "I accept..." box and then add PUBLIC role to the additional roles\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image16.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhat 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. Isn't that slick?\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image17.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let's go back to worksheets and after refreshing the database browser and notice you have a new shared database, ready to query and join with your data. Click on it and you'll see views under the ECONOMY schema. We'll use one of these next.\nPlease note, Knoema recently changed the database name from KNOEMA_ECONOMY_DATA_ATLAS to ECONOMY_DATA_ATLAS. All code snippets are now reflecting the new name, but please don't be confused if old name appear in some screenshots. The content is exactly the same!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image18.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs you would see, this Economy Atlas comes with more than 300 datasets. In order to improve navigation, provider kindly supplied a table called DATASETS. Let's 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\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT * \n  FROM "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-core/image19.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFinally, let's try to query one of the datasets:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * \n  FROM ECONOMY_DATA_ATLAS.ECONOMY.USINDSSP2020\n WHERE "Date" = current_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-core/image20.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You successfully tapped into live data feed of Trade and FX rates data with NO ETL involved. As we promised. Isn't it cool?\nNow let's start building our pipelines.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuilding dbt Data Pipelines\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we are going to start building our dbt pipelines:\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","\u003Ch3\u003EConfiguration\u003C/h3\u003E\n","\u003Cp\u003EWe are going to start by adding few more things to our dbt project configuration in order to improve maintainability.\n1.. \u003Cstrong\u003EModel folders/layers\u003C/strong\u003E. From our dbt project folder location, let's run few command line commands to create separate folders for models, representing different logical levels in the pipeline:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Emkdir models/l10_staging\nmkdir models/l20_transform\nmkdir models/l30_mart\nmkdir models/tests\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen let's open our dbt_project.yml and modify the section below to reflect the model structure.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Emodels:\n  dbt_hol:\n      # Applies to all files under models/example/\n      example:\n          materialized: view\n          +enabled: false\n      l10_staging:\n          schema: l10_staging\n          materialized: view\n      l20_transform:\n          schema: l20_transform\n          materialized: view\n      l30_mart:\n          schema: l30_mart\n          materialized: view\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-core/image22.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs you can see, this is allowing you to set multiple parameters on the layer level (like materialization in this example). Also, you would notice that we added \u003Cem\u003E\u003Cstrong\u003E+enabled: false\u003C/strong\u003E\u003C/em\u003E to the \u003Cem\u003E\u003Cstrong\u003Eexamples\u003C/strong\u003E\u003C/em\u003E section as we won't need to run those sample models in the final state.\u003C/p\u003E\n","\u003Cp\u003E2.. \u003Cstrong\u003ECustom schema naming macros.\u003C/strong\u003E\nBy default, dbt is \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas\"\u003Egenerating a schema name\u003C/a\u003E by appending it to the target schema environment name(dev, prod). In this lab we are going to show you a quick way to override this macro, making our schema names to look exactly the same between dev and prod databases. For this, let's create a file \u003Cstrong\u003Emacroscall_me_anything_you_want.sql\u003C/strong\u003E with the following content:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-YAML\"\u003E{% macro generate_schema_name(custom_schema_name, node) -%}\n    {%- set default_schema = target.schema -%}\n    {%- if custom_schema_name is none -%}\n        {{ default_schema }}\n    {%- else -%}\n        {{ custom_schema_name | trim }}\n    {%- endif -%}\n{%- endmacro %}\n\n\n{% 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\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-core/image23.png\" alt=\"Preview App\"\u003E\u003C/p\u003E\n","\u003Cp\u003E3.. \u003Cstrong\u003EQuery Tag\u003C/strong\u003E. As you might notice, in the screenshot above there is another macro overridden in the file: \u003Cstrong\u003Eset_query_tag()\u003C/strong\u003E. This one provides the ability to add additional level of transparency by automatically setting Snowflake query_tag to the name of the model it associated with.\u003C/p\u003E\n","\u003Cp\u003ESo if you go in Snowflake UI and click 'History' icon on top, you are going to see all SQL queries run on Snowflake account(successful, failed, running etc) and clearly see what dbt model this particular query is related to:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image24.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003E4.. \u003Cstrong\u003Edbt plugins\u003C/strong\u003E. Last one, we promise! Alongside functionality coming out of the box with dbt core, dbt also provide capability to plug-in additional packages. Those could be published in the \u003Ca href=\"https://hub.getdbt.com\"\u003Edbt Hub\u003C/a\u003E or straight out of GitHub repository. In our lab, we are going to demonstrate how to use some automation that the \u003Ca href=\"https://hub.getdbt.com/dbt-labs/dbt_utils/latest/\"\u003Edbt_utils\u003C/a\u003E package provides.\nLet's install it. For that, let's create a file called \u003Cem\u003E\u003Cstrong\u003Epackages.yml\u003C/strong\u003E\u003C/em\u003E in the root of your dbt project folder and add the following lines:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Epackages:\n  - package: dbt-labs/dbt_utils\n    version: 0.8.2\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-core/image25.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce this done, let's open a command line and run\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt deps\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-core/image26.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow that we are fully armed. Let's start building data pipelines!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Stock trading history\u003C/h2\u003E\n","\u003Cp\u003E1.. We are going to start building our pipelines starts by declaring \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/using-sources\"\u003Edbt sources\u003C/a\u003E.\nFor this let's create a \u003Cstrong\u003Emodels/l10_staging/sources.yml\u003C/strong\u003E file and add the following configuration:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Eversion: 2\n\nsources:\n  - name: economy_data_atlas\n    database: economy_data_atlas\n    schema: economy\n    tables:\n      - name: exratescc2018\n      - name: usindssp2020\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs you probably remember, these two objects were mentioned in Knoema Dataset Catalog table: daily exchange rates and daily US trading history accordingly.\u003C/p\u003E\n","\u003Cp\u003E2.. \u003Cstrong\u003EBase views\u003C/strong\u003E is the concept of models that act as a first-level transformation. While not mandatory, these could act as a level of abstraction, separating ultimate source structure from the entry point of dbt pipeline. Providing your project more options to react to an upstream structure change. You can read more about arguments on benefits provided by the base view concept \u003Ca href=\"https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355\"\u003Ehere\u003C/a\u003E.\nWe are going to create a fairly simple pass-through pair of base views:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l10_staging/base_knoema_fx_rates.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT "Currency"        currency\n     , "Currency Unit"   currency_unit\n     , "Frequency"       frequency\n     , "Date"            date\n     , "Value"           value\n     , 'Knoema.FX Rates' data_source_name\n     , src.*\n  FROM {{source('economy_data_atlas','exratescc2018')}}  src \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l10_staging/base_knoema_stock_history.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT "Company"                    Company\n     , "Company Name"               Company_Name\n     , "Company Ticker"             Company_Ticker\n     , "Stock Exchange"             Stock_Exchange\n     , "Stock Exchange Name"        Stock_Exchange_Name\n     , "Indicator"                  Indicator\n     , "Indicator Name"             Indicator_Name\n     , "Units"                      Units\n     , "Scale"                      Scale\n     , "Frequency"                  Frequency\n     , "Date"                       Date\n     , "Value"                      Value\n     , 'Knoema.Stock History' data_source_name\n  FROM {{source('economy_data_atlas','usindssp2020')}}  src \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs you can see we used the opportunity to change case-sensitive & quoted name of the attributes to case insensitive to improve readability. Also as I am sure you noticed, this looks like SQL with the exception of macro \u003Cstrong\u003E{{source()}}\u003C/strong\u003E that is used in "FROM" part of the query instead of fully qualified path (database.schema.table). This is one of the key concepts that is allowing dbt during compilation to replace this with target-specific name. As result, you as a developer, can promote \u003Cstrong\u003Esame\u003C/strong\u003E pipeline code to DEV, PROD and any other environments without any changes.\u003C/p\u003E\n","\u003Cp\u003ELet's run it. Please notice how versatile \u003Cstrong\u003Edbt run\u003C/strong\u003E parameters are. In this example we are going to run all models that are located in \u003Cstrong\u003Emodels/l10_staging\u003C/strong\u003E. More details are in \u003Ca href=\"(https://docs.getdbt.com/reference/node-selection/syntax)\"\u003Edocumentation\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt run --model l10_staging \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-core/image27.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we can go and query this dataset to take a feel of what the data profile looks like.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n  FROM dbt_hol_dev.l10_staging.base_knoema_stock_history \n WHERE Company_Ticker ='AAPL' \n   AND 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-core/image28.png\" alt=\"Query Tag\"\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 case this looks is a bit suboptimal - to simplify the use we would rather see that data transposed into columns, towards something like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image30.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo achieve that, let's create few more models:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_knoema_stock_history.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn this model, we use Snowflake's \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/pivot.html\"\u003EPIVOT\u003C/a\u003E function to transpose the dataset from rows to columns\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EWITH cst AS\n(\nSELECT company_ticker, company_name, stock_exchange_name, indicator_name, date, value , data_source_name\n  FROM {{ref('base_knoema_stock_history')}} src\n WHERE indicator_name IN ('Close', 'Open','High','Low', 'Volume', 'Change %') \n)\nSELECT * \n  FROM cst\n  PIVOT(SUM(Value) for indicator_name IN ('Close', 'Open','High','Low', 'Volume', 'Change %')) \n  AS p(company_ticker, company_name, stock_exchange_name, date, data_source_name, close ,open ,high,low,volume,change)  \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_knoema_stock_history_alt.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhile this model is more for illustration purposes on how similar could be achieved by leveraging \u003Cstrong\u003Edbt_utils.pivot\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT\n  company_ticker, company_name, stock_exchange_name, date, data_source_name,\n  {{ dbt_utils.pivot(\n      column = 'indicator_name',\n      values = dbt_utils.get_column_values(ref('base_knoema_stock_history'), 'indicator_name'),\n      then_value = 'value'\n  ) }}\nFROM {{ ref('base_knoema_stock_history') }}\nGROUP BY company_ticker, company_name, stock_exchange_name, date, data_source_name\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_stock_history.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFinally we are going to create another model that abstracts source-specific transformations into a business view. In case there were multiple feeds providing datasets of the same class (stock history in this case), this view would be able to consolidate (UNION ALL) data from all of them. Thus becoming a one-stop-shop for all stock_history data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT src.*\n  FROM {{ref('tfm_knoema_stock_history')}} src\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E3.. \u003Cstrong\u003EDeploy\u003C/strong\u003E. The hard work is done. Let's go and deploy these. In this case we will automatically deploy tfm_stock_history and all of its ancestors.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt run --model +tfm_stock_history\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-core/image31.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's we go to Snowflake UI to check the results\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n  FROM dbt_hol_dev.l20_transform.tfm_stock_history\n WHERE company_ticker = 'AAPL'\n   AND 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-core/image39.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Currency exchange rates\u003C/h2\u003E\n","\u003Cp\u003EOur Stock history comes in USD. In this step, we are going to complement our landscape with additional models for currency exchange rates so we can see key measures in different currencies.\u003C/p\u003E\n","\u003Cp\u003ELet's start by creating new models:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_fx_rates.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere we are doing something new. dbt offers various \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations\"\u003Ematerialization options\u003C/a\u003E and in our \u003Cstrong\u003Edbt_project.yml\u003C/strong\u003E we identified \u003Cstrong\u003Eview\u003C/strong\u003E as default option. In this model we are going to explicitly override the materialization, turning it into a \u003Cstrong\u003Etable\u003C/strong\u003E. When we deploy this model, dbt would automatically generate a new table (CTAS) replacing old content. As an example, we also add a tag that could help identifying subsets of models for processing.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E{{ \nconfig(\n   materialized='table'\n   , tags=["Reference Data"]\n   ) \n}}\nSELECT src.* \n  FROM {{ref('base_knoema_fx_rates')}} src\n WHERE "Indicator Name" = 'Close' \n   AND "Frequency"      = 'D' \n   AND "Date"           > '2016-01-01'\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_stock_history_major_currency.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis model will start bringing FX and Trade history sets together.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT tsh.*\n     , fx_gbp.value * open          AS gbp_open      \n     , fx_gbp.value * high   AS gbp_high  \n     , fx_gbp.value * low           AS gbp_low      \n     , fx_gbp.value * close         AS gbp_close    \n     , fx_eur.value * open          AS eur_open      \n     , fx_eur.value * high   AS eur_high  \n     , fx_eur.value * low           AS eur_low      \n     , fx_eur.value * close         AS eur_close    \n  FROM {{ref('tfm_stock_history')}} tsh\n     , {{ref('tfm_fx_rates')}}      fx_gbp\n     , {{ref('tfm_fx_rates')}}      fx_eur\n WHERE fx_gbp.currency              = 'USD/GBP'     \n   AND fx_eur.currency              = 'USD/EUR'     \n   AND tsh.date                     = fx_gbp.date\n   AND tsh.date                     = fx_eur.date\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, let's deploy newly built models:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt run --model +tfm_stock_history_major_currency\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-core/image32.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs we now have more models in play, it is a good moment to talk about \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/documentation\"\u003Edbt documentation\u003C/a\u003E. By a run of new following commands dbt will analyze all models in our project and generate a static webpage with a data dictionary/documentation. This is a fantastic way of sharing information with your engineering & user community as it has 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 how big project grows, it is super easy to understand whats happening. And as cherry-on-pie there is also a possibility to see the full lineage of models in the visual DAG:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt docs generate\ndbt docs serve\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-core/image33.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image34.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's we go to Snowflake UI to check the results\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n  FROM dbt_hol_dev.l20_transform.tfm_stock_history_major_currency\n WHERE company_ticker = 'AAPL'\n   AND 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-core/image38.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - Trading books\u003C/h2\u003E\n","\u003Cp\u003EFollowing our use case story, we are going to manually upload two small datasets using\n\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 would notice, they were buying and selling AAPL shares, but logging the cash paid/received in different currencies: USD and GBP.\u003C/p\u003E\n","\u003Cp\u003EFor this let's create two csv files with the following content:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Eseeds/manual_book1.csv\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-csv\"\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\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Eseeds/manual_book2.csv\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-csv\"\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","\u003Cp\u003EOnce created, let's run the following command to load the data into Snowflake. It is important to mention that whilst this approach is absolutely feasible to bring low hundred-thousands of rows it is suboptimal to integrate larger data and you should be using COPY/Snowpipe or other data integration options recommended for Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt seed\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-core/image35.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo simplify usage, let's create a model that would combine data from all desks. In this example we are going to see how \u003Cstrong\u003Edbt_utils.union_relations\u003C/strong\u003E macro helps to automate code automation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_book.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E{{ dbt_utils.union_relations(\n    relations=[ref('manual_book1'), ref('manual_book2')]\n) }}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce we deploy this model, let's have a look what it is compiled into. For this, please open \u003Cstrong\u003Etarget/run/dbt_hol/models/l20_transform/tfm_book.sql\u003C/strong\u003E. As you can see dbt automatically scanned stuctures of the involved objects, aligned all possible attributes by name and type and combined all datasets via UNION ALL. Comparing this to the size of code we entered in the model itself, you can imagine the amount of time saved by such automation.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt run -m tfm_book\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-core/image36.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOkay. 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 we need to have rows for the days shares were HOLD. For this let's introduce some more models:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_daily_position.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EWITH cst_market_days AS\n(\n    SELECT DISTINCT date\n    FROM {{ref('tfm_stock_history_major_currency')}} hist\n    WHERE hist.date >= ( SELECT min(date) AS min_dt FROM {{ref('tfm_book')}}  )\n)\nSELECT\n    cst_market_days.date,\n    trader,\n    stock_exchange_name,\n    instrument,\n    book,\n    currency,\n    sum(volume) AS total_shares\nFROM cst_market_days\n   , {{ref('tfm_book')}} book\nWHERE book.date <= cst_market_days.date\nGROUP BY 1, 2, 3, 4, 5, 6 \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_daily_position_with_trades.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003ESELECT book\n     , date\n     , trader\n     , instrument\n     , action\n     , cost\n     , currency\n     , volume\n     , cost_per_share\n     , stock_exchange_name\n     , SUM(t.volume) OVER(partition BY t.instrument, t.stock_exchange_name, trader ORDER BY t.date rows UNBOUNDED PRECEDING ) total_shares\n  FROM {{ref('tfm_book')}}  t\nUNION ALL   \nSELECT book\n     , date\n     , trader\n     , instrument\n     , 'HOLD' as action\n     , 0 AS cost\n     , currency\n     , 0      as volume\n     , 0      as cost_per_share\n     , stock_exchange_name\n     , total_shares\nFROM {{ref('tfm_daily_position')}} \nWHERE (date,trader,instrument,book,stock_exchange_name) \n      NOT IN \n      (SELECT date,trader,instrument,book,stock_exchange_name\n         FROM {{ref('tfm_book')}}\n      )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt run -m tfm_book+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's go back to Snowflake worksheets and run a query to see the results:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n  FROM dbt_hol_dev.l20_transform.tfm_daily_position_with_trades\n WHERE trader = 'Jeff A.'\n ORDER BY 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-core/image37.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt pipelines - PnL calculation\u003C/h2\u003E\n","\u003Cp\u003EThis section should bring the last models to complete the story.\nNow we have trading history of our desks and stock price history. Let's create a model to show how Market Value and PnL were changing over time. For this we are going to start by creating a model:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l20_transform/tfm_trading_pnl.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT t.instrument, t.stock_exchange_name, \n       t.date, trader, t.volume,cost, cost_per_share,currency,\n       SUM(cost) OVER(partition BY t.instrument, t.stock_exchange_name, trader ORDER BY t.date rows UNBOUNDED PRECEDING ) cash_cumulative,\n       CASE WHEN t.currency = 'GBP' THEN gbp_close\n            WHEN t.currency = 'EUR' THEN eur_close\n            ELSE close\n       END                                                        AS close_price_matching_ccy,     \n       total_shares  * close_price_matching_ccy                   AS market_value, \n       total_shares  * close_price_matching_ccy + cash_cumulative AS PnL\n   FROM       {{ref('tfm_daily_position_with_trades')}}    t\n   INNER JOIN {{ref('tfm_stock_history_major_currency')}}  s \n      ON t.instrument = s.company_ticker \n     AND s.date = t.date \n     AND t.stock_exchange_name = s.stock_exchange_name\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l30_mart/fct_trading_pnl.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis model will be the one we created in the mart area, prepared to be used by many. With that in mind, it will be good idea to materialize this model as a table with incremental load mode. You can see that this materialization mode has a special macro that comes into action for the incremental runs (and ignored during initial run and full_refresh option).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{{ \nconfig(\n   materialized='incremental'\n   , tags=["Fact Data"]\n   ) \n}}\nSELECT src.*\n  FROM {{ref('tfm_trading_pnl')}} src\n\n{% if is_incremental() %}\n  -- this filter will only be applied on an incremental run\n WHERE (trader, instrument, date, stock_exchange_name) NOT IN (select trader, instrument, date, stock_exchange_name from {{ this }})\n\n{% endif %}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, for illustration purposes we are going to create a couple of views that could be extended further, representing different lens of interpreting PnL data between treasury, risk and finance departments.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l30_mart/fct_trading_pnl_finance_view.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n-- this is a placeholder for illustration purposes\n  FROM {{ref('fct_trading_pnl')}} src\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l30_mart/fct_trading_pnl_risk_view.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n-- this is a placeholder for illustration purposes\n  FROM {{ref('fct_trading_pnl')}} src\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/l30_mart/fct_trading_pnl_treasury_view.sql\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n-- this is a placeholder for illustration purposes\n  FROM {{ref('fct_trading_pnl')}} src\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's deploy all of these models and run a query to check the final results:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt run \ndbt docs serve\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-core/image40.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe final lineage tree:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image42.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * \n  FROM dbt_hol_dev.l30_mart.fct_trading_pnl\n WHERE trader = 'Jeff A.'\n ORDER by 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-core/image41.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow, let's create a simple data visualization for this dataset. For that, let's click on the Preview App button once again:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image9.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen \u003Cstrong\u003EWorksheets -> + Worksheet\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image49.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen let's copy-paste the same query we run in classic Snowflake UI worksheets. Hit the run button and switch from a table view to chart:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image50.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBy default it shows a breakdown by Volume. Let's click on the measure and switch it into \u003Cstrong\u003EPNL\u003C/strong\u003E. Then let's add another measure to our chart for displaying Market value and PnL side by side.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image51.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image52.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAnd this is it! Now you have a worksheet that you can slice'n'dice, share with your colleagues or embed in the SnowSight dashboard as one of the tiles. As you can see, Snowsight offers a great capability to quickly visualize the insight and always there for you as a part of the Snowflake platform. For more details on SnowSight, please refer to the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-web.html\"\u003ESnowflake documentation\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-core/image53.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETesting, Deployment, Materializations\u003C/h2\u003E\n","\u003Ch3\u003EEstablishing Testing\u003C/h3\u003E\n","\u003Cp\u003ETo build trust in your data solution, it is hard to underestimate the importance of testing. While there are many ways to organize automated testing, thankfully dbt tool comes with the great \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/tests\"\u003Edata tests framework\u003C/a\u003E. Let's build an example.\u003C/p\u003E\n","\u003Cp\u003EFirst, let's add the test configuration file and add the content below. dbt comes with a set of pre-defined data tests, such as uniqueness, not_null, check constraints, ref integrity etc. We are going to set up tests on the few models, however it is highly recommended to establish reasonable test coverage across the board.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emodels/tests/data_quality_tests.yml\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Eversion: 2\n\nmodels:\n  - name: tfm_fx_rates\n    columns:\n      - name: currency||date\n        tests:\n          - unique\n          - not_null\n\n  - name: tfm_book\n    columns:\n      - name: instrument\n        tests:\n          - not_null\n          - relationships:\n              to: ref('tfm_stock_history')\n              field: company_ticker\n\n  - name: tfm_stock_history\n    columns:\n      - name: company_ticker||date\n        tests:\n          - not_null\n          - unique\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, let's run these tests:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt test\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoom! One of the tests failed! Let's try to understand why. dbt command line is kindly provided a link to the file with the SQL check that failed. Let's open it and copy-paste the content to Snowflake worksheet:\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image54.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image55.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet's quickly check the full row width for one of the records failed by extending this check towards something like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH cst AS\n(\n    select\n        company_ticker||date conctat\n\n    from dbt_hol_dev.l20_transform.tfm_stock_history\n    where company_ticker||date is not null\n    group by company_ticker||date\n    having count(*) > 1 \n    limit 1\n)\nSELECT * FROM dbt_hol_dev.l20_transform.tfm_stock_history\n WHERE company_ticker||date IN (SELECT conctat FROM cst) \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-core/image56.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAha! There are shares which are traded on more than one stock exchanges. So we need to include \u003Cstrong\u003Estock_exchange_name\u003C/strong\u003E attribute to your unique test key. Let's go back to \u003Cstrong\u003Emodels/tests/data_quality_tests.yml\u003C/strong\u003E and update the test configuration for \u003Cstrong\u003Etfm_stock_history\u003C/strong\u003E model :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003E  - name: tfm_stock_history\n    columns:\n      - name: company_ticker||date||stock_exchange_name\n        tests:\n          - not_null\n          - unique\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnd run the test again\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt test\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-core/image57.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFinishing testing note, it is also worth mentioning that alongside such tests, dbt framework also supports custom tests that are massively expanding scenarios(like regression testing) could be covered by data tests. And just to expand it even further, in dbt hub there is a package \u003Ca href=\"https://hub.getdbt.com/calogica/dbt_expectations/latest/\"\u003Edbt_expectations\u003C/a\u003E that implements a lot of additional tests, inspired by popular\u003Ca href=\"http://greatexpectations.io/\"\u003Ehttp://greatexpectations.io/\u003C/a\u003E framework.\u003C/p\u003E\n","\u003Ch3\u003EDeployment\u003C/h3\u003E\n","\u003Cp\u003EOkay, seems like we have everything in place: pipelines been developed and tested. The next step would be to promote this code up the chain through our SDLC environments(which in this lab is simplified to just DEV & PROD).\u003C/p\u003E\n","\u003Cp\u003EIn real life, the project code we are working should be in source version control system like git and by now pushed into one of the feature branches and merged into dev/trunk branch. From there, typically users raise pull requests to master/release version and then perform a deployment in production environment. Thanks to the fact dbt pipelines are very readable it is possible to implement good code review practices as well as set up automatic testing with various stages as a part of CICD automation.\u003C/p\u003E\n","\u003Cp\u003EWorking with git and branches is not in scope of this lab so we will just run the following command to deploy the very same codebase to PROD environment.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt seed --target=prod\ndbt run  --target=prod\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-core/image58.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can check the UI that now we have data in \u003Cstrong\u003Edbt_hol_prod\u003C/strong\u003E database:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image59.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EMaterialization & Scaling\u003C/h3\u003E\n","\u003Cp\u003Edbt provides the ability to easily change the \u003Ca href=\"https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations\"\u003Ematerialization option\u003C/a\u003E, taking away all the burden related to generating new version of DDL & DML. What does it means for modern data engineering? You no longer need to spend precious time performing upfront performance optimization and rather focus on building models, bringing more insights to your business. And when it comes to understand the usage patterns, models that are heavy and/or accessed frequently could be selectively materialized.\u003C/p\u003E\n","\u003Cp\u003EDuring the lab you've probably seen how easily Snowflake could deal with many models materialized as views, provided the input data volume of stock history is >200Mn records alone. We also explicitly configured one model to be materialized as 'table'(CTAS) and another one as 'incremental'(MERGE). Once you move into persisted methods of materialization you will be using Snowflake virtual warehouses as compute power to perform the materialization.\u003C/p\u003E\n","\u003Cp\u003ELet's have a look on a couple of ways to manage compute size Snowflake will dedicate to a specific model(s).\u003C/p\u003E\n","\u003Cp\u003E1.. Let's open \u003Cstrong\u003Edbt_projects.yml\u003C/strong\u003E and add the additional line\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Emodels:\n  dbt_hol:\n      # Applies to all files under models/example/\n      example:\n          materialized: view\n          +enabled: false\n      l10_staging:\n          schema: l10_staging\n          materialized: view\n      l20_transform:\n          schema: l20_transform\n          materialized: view\n          +snowflake_warehouse: dbt_dev_heavy_wh\n      l30_mart:\n          schema: l30_mart\n          materialized: view\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-core/image60.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003E2.. Let's modify the content of \u003Cstrong\u003Emodels/l30_mart/fct_trading_pnl.sql\u003C/strong\u003E changing config section to include pre and post run hooks:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{{ \nconfig(\n   materialized='incremental'\n   , tags=["Fact Data"]\n   , pre_hook ="ALTER WAREHOUSE dbt_dev_wh SET WAREHOUSE_SIZE ='XXLARGE'" \n      , post_hook="ALTER WAREHOUSE dbt_dev_wh SET WAREHOUSE_SIZE ='XSMALL'"\n   ) \n}}\nSELECT src.*\n  FROM {{ref('tfm_trading_pnl')}} src\n\n{% if is_incremental() %}\n  -- this filter will only be applied on an incremental run\n WHERE (trader, instrument, date, stock_exchange_name) NOT IN (select trader, instrument, date, stock_exchange_name from {{ this }})\n\n{% endif %}\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-core/image62.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let's run the project again:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-cmd\"\u003Edbt run\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce finished, lets' go into Snowflake UI and look at the Query History page. As you can see, dbt automatically switched into a separate warehouse \u003Cstrong\u003Edbt_dev_heavy_wh\u003C/strong\u003E (of a larger size) once it started working on the models in \u003Cstrong\u003El20_transform\u003C/strong\u003E folder. Once it reached the \u003Cstrong\u003El30_mart/fct_trading_pnl\u003C/strong\u003E model, the pipeline increased the size of the \u003Cstrong\u003Edbt_dev_wh\u003C/strong\u003E to 2XL, processed the model faster, and then decreased the size of compute back to XS to keep it economical.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image61.png\" alt=\"Query Tag\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThese are just a couple of examples how you could leverage elasticity and workload isolation of Snowflake compute by switching between or resizing virtual warehouses as a simple DDL command, embedded in your pipelines.\u003C/p\u003E\n","\u003Cp\u003EWith that, let's move to our final section for this lab!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion & Next Steps\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on completing this lab using dbt and Snowflake for building data pipelines to drive analytics! You’ve mastered the dbt and Snowflake basics and are ready to apply these fundamentals to your own data. Be sure to reference this guide if you ever need a refresher.\u003C/p\u003E\n","\u003Cp\u003EWe encourage you to continue with your free trial by loading your own sample or production data and by using some of the more advanced capabilities of dbt and Snowflake not covered in this lab.\u003C/p\u003E\n","\u003Ch3\u003EAdditional Resources:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERead the \u003Ca href=\"/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/\"\u003EDefinitive Guide to Maximizing Your Free Trial\u003C/a\u003E document\u003C/li\u003E\u003Cli\u003EAttend a \u003Ca href=\"/about/events/\"\u003ESnowflake virtual or in-person event\u003C/a\u003E to learn more about our capabilities and customers\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community.snowflake.com/s/topic/0TO0Z000000wmFQWAY/getting-started-with-snowflake\"\u003EJoin the Snowflake community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community.snowflake.com/s/article/Getting-Access-to-Snowflake-University\"\u003ESign up for Snowflake University\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/free-trial-contact-sales/\"\u003EContact our Sales Team\u003C/a\u003E to learn more\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community.getdbt.com/\"\u003EJoin dbt community slack\u003C/a\u003E where thousands of dbt on Snowflake users discussing their best practices\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat we've covered:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to set up 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\u003Cli\u003E\n","\u003Cp\u003EHow to run a dbt project and develop pipelines\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to create data tests\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to leverage Snowflake elasticity and scalability to support dbt calculations at scale\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E"],":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nModern Businesses need modern data strategy built on platforms that could support agility, growth and operational efficiency. Snowflake is Data Cloud, a future proof solution that can simplify data pipelines for all your businesses so you can focus on your data and analytics instead of infrastructure management and maintenance.\r\n\r\n[dbt](https://www.getdbt.com/) is a modern data engineering framework maintained by [dbt Labs](https://www.getdbt.com/dbt-labs/about-us/) that is becoming very popular in modern data architectures, leveraging cloud data platforms like Snowflake. [dbt CLI](https://docs.getdbt.com/dbt-cli/cli-overview) is the open-source version of dbtCloud that is providing similar functionality, but as a SaaS.\r\nIn this virtual hands-on lab, you will follow a step-by-step guide to Snowflake and dbt to see some of the benefits this tandem brings. \r\n\r\nLet’s get started. \r\n\r\n### Prerequisites\r\nTo participate in the virtual hands-on lab, attendees need the following:\r\n\r\n* A [Snowflake account](https://trial.snowflake.com/) with `ACCOUNTADMIN` access\r\n\r\n* Familiarity with Snowflake and Snowflake objects\r\n\r\n\r\n### What You'll Need During the Lab\r\n\r\n* [dbt CLI](https://docs.getdbt.com/dbt-cli/installation) installed \r\n\r\n* Text editor of your choice\r\n\r\n\r\n### What You'll Learn\r\n\r\n* How to leverage data in Snowflake's Data Marketplace \r\n\r\n* How to build scalable pipelines using dbt & Snowflake\r\n\r\n\r\n### What You'll Build\r\n* A set of data analytics pipelines for Financial Services data leveraging dbt and Snowflake\r\n\r\n* Implement data quality tests\r\n\r\n* Promote code between the environments\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-core/image124.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![Snowflake Worksheets](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image29.png)  \r\n\r\n3. Let's now create a database and a service accounts for dbt.\r\n\r\n```sql\r\n-------------------------------------------\r\n-- dbt credentials\r\n-------------------------------------------\r\nUSE ROLE securityadmin;\r\n-- dbt roles\r\nCREATE OR REPLACE ROLE dbt_dev_role;\r\nCREATE OR REPLACE ROLE dbt_prod_role;\r\n------------------------------------------- Please replace with your dbt user password\r\nCREATE OR REPLACE USER dbt_user PASSWORD = \"\u003Cmysecretpassword\u003E\";\r\n\r\nGRANT ROLE dbt_dev_role,dbt_prod_role TO USER dbt_user;\r\nGRANT ROLE dbt_dev_role,dbt_prod_role TO ROLE sysadmin;\r\n\r\n-------------------------------------------\r\n-- dbt objects\r\n-------------------------------------------\r\nUSE ROLE sysadmin;\r\n\r\nCREATE OR REPLACE WAREHOUSE dbt_dev_wh  WITH WAREHOUSE_SIZE = 'XSMALL' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\r\nCREATE OR REPLACE WAREHOUSE dbt_dev_heavy_wh  WITH WAREHOUSE_SIZE = 'LARGE' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\r\nCREATE OR REPLACE WAREHOUSE dbt_prod_wh WITH WAREHOUSE_SIZE = 'XSMALL' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\r\nCREATE OR REPLACE WAREHOUSE dbt_prod_heavy_wh  WITH WAREHOUSE_SIZE = 'LARGE' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE MIN_CLUSTER_COUNT = 1 MAX_CLUSTER_COUNT = 1 INITIALLY_SUSPENDED = TRUE;\r\n\r\nGRANT ALL ON WAREHOUSE dbt_dev_wh  TO ROLE dbt_dev_role;\r\nGRANT ALL ON WAREHOUSE dbt_dev_heavy_wh  TO ROLE dbt_dev_role;\r\nGRANT ALL ON WAREHOUSE dbt_prod_wh TO ROLE dbt_prod_role;\r\nGRANT ALL ON WAREHOUSE dbt_prod_heavy_wh  TO ROLE dbt_prod_role;\r\n\r\nCREATE OR REPLACE DATABASE dbt_hol_dev; \r\nCREATE OR REPLACE DATABASE dbt_hol_prod; \r\nGRANT ALL ON DATABASE dbt_hol_dev  TO ROLE dbt_dev_role;\r\nGRANT ALL ON DATABASE dbt_hol_prod TO ROLE dbt_prod_role;\r\nGRANT ALL ON ALL SCHEMAS IN DATABASE dbt_hol_dev   TO ROLE dbt_dev_role;\r\nGRANT ALL ON ALL SCHEMAS IN DATABASE dbt_hol_prod  TO ROLE dbt_prod_role;\r\n```\r\n\r\nAs result of these steps, we should have:\r\n-  two empty databases: PROD, DEV\r\n-  two pair of virtual warehouses: two for prod, two for dev workloads\r\n-  a pair of roles and one user\r\n\r\nPlease note, this set up is simplified for the purpose of the lab. \r\nThere are many ways environments, roles, credentials could be modeled to fit your final requirements. \r\n\r\nWe would suggest having a look at these articles for inspiration: [How we configure Snowflake by the dbt Labs Team](https://blog.getdbt.com/how-we-configure-snowflake/), [Model Structure by GitLab team](https://about.gitlab.com/handbook/business-technology/data-team/platform/dbt-guide/#model-structure)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## dbt Configuration \r\n\r\n### Initialize dbt project\r\n\r\nCreate a new dbt project in any local folder by running the following commands:\r\n\r\n```Shell\r\n$ dbt init dbt_hol\r\n$ cd dbt_hol\r\n```\r\n\r\n### Configure dbt/Snowflake profiles \r\n\r\n1.. Open  `~/.dbt/profiles.yml` in text editor and add the following section\r\n\r\n```yml\r\ndbt_hol:\r\n  target: dev\r\n  outputs:\r\n    dev:\r\n      type: snowflake\r\n      ######## Please replace with your Snowflake account name\r\n      account: \u003Cyour_snowflake_trial_account\u003E\r\n      \r\n      user: dbt_user\r\n      ######## Please replace with your Snowflake dbt user password\r\n      password: \u003Cmysecretpassword\u003E\r\n      \r\n      role: dbt_dev_role\r\n      database: dbt_hol_dev\r\n      warehouse: dbt_dev_wh\r\n      schema: public\r\n      threads: 200\r\n    prod:\r\n      type: snowflake\r\n      ######## Please replace with your Snowflake account name\r\n      account: \u003Cyour_snowflake_trial_account\u003E\r\n      \r\n      user: dbt_user\r\n      ######## Please replace with your Snowflake dbt user password\r\n      password: \u003Cmysecretpassword\u003E\r\n      \r\n      role: dbt_prod_role\r\n      database: dbt_hol_prod\r\n      warehouse: dbt_prod_wh\r\n      schema: public\r\n      threads: 200\r\n```\r\n\r\n 2..  Open `dbt_project.yml` (in dbt_hol folder) and update the following sections:\r\n\r\n![dbt_project.yml](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image3.png)  \r\n\r\n### Validate the configuration\r\nRun the following command (in dbt_hol folder): \r\n\r\n```Shell\r\n$ dbt debug\r\n```\r\nThe expected output should look like this, confirming that dbt was able to access the database: \r\n![dbt debug output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image4.png)  \r\n\r\n### Test run\r\nFinally, let's run the sample models that comes with dbt templates by default to validate everything is set up correctly. \r\nFor this, please run the following command (in dbt_hol folder):\r\n```Shell\r\n$ dbt run\r\n```\r\nThe expected output should look like this, confirming dbt was able to connect and successfully run sample models: \r\n![dbt run output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image5.png)  \r\nPlease note, this operation is completely rerunable and does not provide any harm to our next steps in the lab.\r\n\r\nYou can use Snowflake worksheets to validate that the sample view and the table are now available in DEV database: \r\n![Snowflake UI](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image6.png)  \r\n\r\n\r\nCongratulations! You just run your first dbt models on Snowflake! \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Architecture and Use Case Overview\r\n\r\nIn this lab, we are going to analyze 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 that is available in Snowflake Marketplace, plus few manual uploads. \r\n\r\nWe are going to set up the environments from scratch, build scalable pipelines in dbt, establish data tests, and Snowflake and promote code to production.  Finally we will use Snowsight to build a simple dashboard to visualize the results. \r\n\r\n![Architecture ](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image7.png)  \r\n\r\nJust to give you a sneak peek, this is where we are going to be in just 30 minutes.\r\n\r\nStay tuned!\r\n\r\n![dbt target view ](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image21.png)  \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Connect to Data Sources\r\n\r\nLet's go to the Snowflake Marketplace and find what we need. The Data Marketplace lives in the new UI called Snowsight (currently in Preview mode but feel free to test drive after the lab). Click on Preview App at the top of the UI\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image9.png)  \r\n\r\nClick Sign in to continue. You will need to use the same user and password that you used to login to your Snowflake account the first time.\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image11.png)  \r\n\r\nYou're now in the new UI - Snowsight. It's pretty cool - with charting and dashboards and context-sensitivity - but today we're just focused on getting to the Data Marketplace. Click on Data...\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image14.png)  \r\n\r\n...and then Marketplace...\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image12.png)  \r\n\r\n..and now you're in! Hundreds of providers have made datasets available for you to enrich your data. Today we're going to grab a Knoema Economy Atlas Data. Click the Ready to Query checkbox and then find the Knoema Economy Atlas Data tile. Once you find it, click on it.\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image13.png)  \r\nHere you'll find a description of the data, example queries, and other useful information. Let's get this data into our Snowflake account. You'll be amazed at how fast and easy this is. Click the \"Get Data\" button\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image15.png)  \r\n\r\nIn the pop-up, leave the database name as proposed by default (important!), check the \"I accept...\" box and then add PUBLIC role to the additional roles \r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image16.png)  \r\n\r\nWhat 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. Isn't that slick?\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image17.png)  \r\n\r\nNow let's go back to worksheets and after refreshing the database browser and notice you have a new shared database, ready to query and join with your data. Click on it and you'll see views under the ECONOMY schema. We'll use one of these next.\r\nPlease note, Knoema recently changed the database name from KNOEMA_ECONOMY_DATA_ATLAS to ECONOMY_DATA_ATLAS. All code snippets are now reflecting the new name, but please don't be confused if old name appear in some screenshots. The content is exactly the same!\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image18.png) \r\n\r\nAs you would see, this Economy Atlas comes with more than 300 datasets. In order to improve navigation, provider kindly supplied a table called DATASETS. Let's 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```SQL\r\nSELECT * \r\n  FROM \"ECONOMY_DATA_ATLAS\".\"ECONOMY\".\"DATASETS\"\r\n WHERE \"DatasetName\" ILIKE 'US Stock%'\r\n    OR \"DatasetName\" ILIKE 'Exchange%Rates%';\r\n```\r\n\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image19.png) \r\n\r\nFinally, let's try to query one of the datasets: \r\n```\r\nSELECT * \r\n  FROM ECONOMY_DATA_ATLAS.ECONOMY.USINDSSP2020\r\n WHERE \"Date\" = current_date();\r\n```\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image20.png) \r\n\r\nCongratulations! You successfully tapped into live data feed of Trade and FX rates data with NO ETL involved. As we promised. Isn't it cool? \r\nNow let's start building our pipelines. \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Building dbt Data Pipelines\r\n\r\nIn this section, we are going to start building our dbt pipelines:\r\n\r\n- Stock trading history\r\n- Currency exchange rates\r\n- Trading books\r\n- Profit & Loss calculation\r\n\r\n### Configuration\r\nWe are going to start by adding few more things to our dbt project configuration in order to improve maintainability. \r\n1.. **Model folders/layers**. From our dbt project folder location, let's run few command line commands to create separate folders for models, representing different logical levels in the pipeline: \r\n\r\n```cmd\r\nmkdir models/l10_staging\r\nmkdir models/l20_transform\r\nmkdir models/l30_mart\r\nmkdir models/tests\r\n```\r\n\r\nThen let's open our dbt_project.yml and modify the section below to reflect the model structure. \r\n\r\n```yml\r\nmodels:\r\n  dbt_hol:\r\n      # Applies to all files under models/example/\r\n      example:\r\n          materialized: view\r\n          +enabled: false\r\n      l10_staging:\r\n          schema: l10_staging\r\n          materialized: view\r\n      l20_transform:\r\n          schema: l20_transform\r\n          materialized: view\r\n      l30_mart:\r\n          schema: l30_mart\r\n          materialized: view\r\n```\r\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image22.png) \r\n\r\nAs you can see, this is allowing you to set multiple parameters on the layer level (like materialization in this example). Also, you would notice that we added ***+enabled: false*** to the ***examples*** section as we won't need to run those sample models in the final state.\r\n\r\n\r\n2.. **Custom schema naming macros.**\r\nBy default, dbt is [generating a schema name](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/using-custom-schemas) by appending it to the target schema environment name(dev, prod). In this lab we are going to show you a quick way to override this macro, making our schema names to look exactly the same between dev and prod databases. For this, let's create a file **macroscall_me_anything_you_want.sql** with the following content:\r\n\r\n```YAML\r\n{% macro generate_schema_name(custom_schema_name, node) -%}\r\n    {%- set default_schema = target.schema -%}\r\n    {%- if custom_schema_name is none -%}\r\n        {{ default_schema }}\r\n    {%- else -%}\r\n        {{ custom_schema_name | trim }}\r\n    {%- endif -%}\r\n{%- endmacro %}\r\n\r\n\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\n![Preview App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image23.png) \r\n\r\n3.. **Query Tag**. As you might notice, in the screenshot above there is another macro overridden in the file: **set_query_tag()**. This one provides the ability to add additional level of transparency by automatically setting Snowflake query_tag to the name of the model it associated with. \r\n\r\nSo if you go in Snowflake UI and click 'History' icon on top, you are going to see all SQL queries run on Snowflake account(successful, failed, running etc) and clearly see what dbt model this particular query is related to: \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image24.png) \r\n\r\n4.. **dbt plugins**. Last one, we promise! Alongside functionality coming out of the box with dbt core, dbt also provide capability to plug-in additional packages. Those could be published in the [dbt Hub](https://hub.getdbt.com) or straight out of GitHub repository. In our lab, we are going to demonstrate how to use some automation that the [dbt_utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/) package provides. \r\nLet's install it. For that, let's create a file called ***packages.yml*** in the root of your dbt project folder and add the following lines: \r\n\r\n```yml\r\npackages:\r\n  - package: dbt-labs/dbt_utils\r\n    version: 0.8.2\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image25.png) \r\n\r\nOnce this done, let's open a command line and run \r\n\r\n```cmd\r\ndbt deps\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image26.png) \r\n\r\nNow that we are fully armed. Let's start building data pipelines! \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## dbt pipelines - Stock trading history\r\n\r\n1.. We are going to start building our pipelines starts by declaring [dbt sources](https://docs.getdbt.com/docs/building-a-dbt-project/using-sources). \r\nFor this let's create a **models/l10_staging/sources.yml** file and add the following configuration:\r\n\r\n```yml\r\nversion: 2\r\n\r\nsources:\r\n  - name: economy_data_atlas\r\n    database: economy_data_atlas\r\n    schema: economy\r\n    tables:\r\n      - name: exratescc2018\r\n      - name: usindssp2020\r\n```\r\nAs you probably remember, these two objects were mentioned in Knoema Dataset Catalog table: daily exchange rates and daily US trading history accordingly. \r\n\r\n2.. **Base views** is the concept of models that act as a first-level transformation. While not mandatory, these could act as a level of abstraction, separating ultimate source structure from the entry point of dbt pipeline. Providing your project more options to react to an upstream structure change. You can read more about arguments on benefits provided by the base view concept [here](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355).\r\nWe are going to create a fairly simple pass-through pair of base views: \r\n- **models/l10_staging/base_knoema_fx_rates.sql**\r\n\r\n```sql\r\nSELECT \"Currency\"        currency\r\n     , \"Currency Unit\"   currency_unit\r\n     , \"Frequency\"       frequency\r\n     , \"Date\"            date\r\n     , \"Value\"           value\r\n     , 'Knoema.FX Rates' data_source_name\r\n     , src.*\r\n  FROM {{source('economy_data_atlas','exratescc2018')}}  src \r\n```\r\n\r\n- **models/l10_staging/base_knoema_stock_history.sql**\r\n\r\n```sql\r\nSELECT \"Company\"                    Company\r\n     , \"Company Name\"               Company_Name\r\n     , \"Company Ticker\"             Company_Ticker\r\n     , \"Stock Exchange\"             Stock_Exchange\r\n     , \"Stock Exchange Name\"        Stock_Exchange_Name\r\n     , \"Indicator\"                  Indicator\r\n     , \"Indicator Name\"             Indicator_Name\r\n     , \"Units\"                      Units\r\n     , \"Scale\"                      Scale\r\n     , \"Frequency\"                  Frequency\r\n     , \"Date\"                       Date\r\n     , \"Value\"                      Value\r\n     , 'Knoema.Stock History' data_source_name\r\n  FROM {{source('economy_data_atlas','usindssp2020')}}  src \r\n```\r\nAs you can see we used the opportunity to change case-sensitive & quoted name of the attributes to case insensitive to improve readability. Also as I am sure you noticed, this looks like SQL with the exception of macro **{{source()}}** that is used in \"FROM\" part of the query instead of fully qualified path (database.schema.table). This is one of the key concepts that is allowing dbt during compilation to replace this with target-specific name. As result, you as a developer, can promote **same** pipeline code to DEV, PROD and any other environments without any changes. \r\n\r\nLet's run it. Please notice how versatile **dbt run** parameters are. In this example we are going to run all models that are located in **models/l10_staging**. More details are in [documentation]((https://docs.getdbt.com/reference/node-selection/syntax)).\r\n\r\n```cmd\r\ndbt run --model l10_staging \r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image27.png) \r\n\r\nNow we can go and query this dataset to take a feel of what the data profile looks like.\r\n\r\n```sql\r\nSELECT * \r\n  FROM dbt_hol_dev.l10_staging.base_knoema_stock_history \r\n WHERE Company_Ticker ='AAPL' \r\n   AND date ='2021-03-01'\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image28.png) \r\n\r\nIn this dataset, different measures like Close, Open, High and Low price are represented as different rows. For our case this looks is a bit suboptimal - to simplify the use we would rather see that data transposed into columns, towards something like this: \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image30.png) \r\n\r\nTo achieve that, let's create few more models:\r\n\r\n- **models/l20_transform/tfm_knoema_stock_history.sql**\r\n\r\nIn this model, we use Snowflake's [PIVOT](https://docs.snowflake.com/en/sql-reference/constructs/pivot.html) function to transpose the dataset from rows to columns\r\n\r\n```SQL\r\nWITH cst AS\r\n(\r\nSELECT company_ticker, company_name, stock_exchange_name, indicator_name, date, value , data_source_name\r\n  FROM {{ref('base_knoema_stock_history')}} src\r\n WHERE indicator_name IN ('Close', 'Open','High','Low', 'Volume', 'Change %') \r\n)\r\nSELECT * \r\n  FROM cst\r\n  PIVOT(SUM(Value) for indicator_name IN ('Close', 'Open','High','Low', 'Volume', 'Change %')) \r\n  AS p(company_ticker, company_name, stock_exchange_name, date, data_source_name, close ,open ,high,low,volume,change)  \r\n```\r\n\r\n- **models/l20_transform/tfm_knoema_stock_history_alt.sql**\r\n\r\nWhile this model is more for illustration purposes on how similar could be achieved by leveraging **dbt_utils.pivot** \r\n```SQL\r\nSELECT\r\n  company_ticker, company_name, stock_exchange_name, date, data_source_name,\r\n  {{ dbt_utils.pivot(\r\n      column = 'indicator_name',\r\n      values = dbt_utils.get_column_values(ref('base_knoema_stock_history'), 'indicator_name'),\r\n      then_value = 'value'\r\n  ) }}\r\nFROM {{ ref('base_knoema_stock_history') }}\r\nGROUP BY company_ticker, company_name, stock_exchange_name, date, data_source_name\r\n```\r\n\r\n- **models/l20_transform/tfm_stock_history.sql**\r\n\r\nFinally we are going to create another model that abstracts source-specific transformations into a business view. In case there were multiple feeds providing datasets of the same class (stock history in this case), this view would be able to consolidate (UNION ALL) data from all of them. Thus becoming a one-stop-shop for all stock_history data. \r\n\r\n```SQL\r\nSELECT src.*\r\n  FROM {{ref('tfm_knoema_stock_history')}} src\r\n```\r\n\r\n3.. **Deploy**. The hard work is done. Let's go and deploy these. In this case we will automatically deploy tfm_stock_history and all of its ancestors. \r\n\r\n```cmd\r\ndbt run --model +tfm_stock_history\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image31.png) \r\n\r\nLet's we go to Snowflake UI to check the results\r\n\r\n```sql\r\nSELECT * \r\n  FROM dbt_hol_dev.l20_transform.tfm_stock_history\r\n WHERE company_ticker = 'AAPL'\r\n   AND date = '2021-03-01'\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image39.png) \r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## dbt pipelines - Currency exchange rates\r\nOur Stock history comes in USD. In this step, we are going to complement our landscape with additional models for currency exchange rates so we can see key measures in different currencies. \r\n\r\nLet's start by creating new models:\r\n\r\n- **models/l20_transform/tfm_fx_rates.sql**\r\n\r\nHere we are doing something new. dbt offers various [materialization options](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations) and in our **dbt_project.yml** we identified **view** as default option. In this model we are going to explicitly override the materialization, turning it into a **table**. When we deploy this model, dbt would automatically generate a new table (CTAS) replacing old content. As an example, we also add a tag that could help identifying subsets of models for processing. \r\n\r\n```SQL\r\n{{ \r\nconfig(\r\n   materialized='table'\r\n   , tags=[\"Reference Data\"]\r\n   ) \r\n}}\r\nSELECT src.* \r\n  FROM {{ref('base_knoema_fx_rates')}} src\r\n WHERE \"Indicator Name\" = 'Close' \r\n   AND \"Frequency\"      = 'D' \r\n   AND \"Date\"           \u003E '2016-01-01'\r\n```\r\n\r\n- **models/l20_transform/tfm_stock_history_major_currency.sql**\r\n\r\nThis model will start bringing FX and Trade history sets together. \r\n\r\n```SQL\r\nSELECT tsh.*\r\n     , fx_gbp.value * open          AS gbp_open      \r\n     , fx_gbp.value * high   AS gbp_high  \r\n     , fx_gbp.value * low           AS gbp_low      \r\n     , fx_gbp.value * close         AS gbp_close    \r\n     , fx_eur.value * open          AS eur_open      \r\n     , fx_eur.value * high   AS eur_high  \r\n     , fx_eur.value * low           AS eur_low      \r\n     , fx_eur.value * close         AS eur_close    \r\n  FROM {{ref('tfm_stock_history')}} tsh\r\n     , {{ref('tfm_fx_rates')}}      fx_gbp\r\n     , {{ref('tfm_fx_rates')}}      fx_eur\r\n WHERE fx_gbp.currency              = 'USD/GBP'     \r\n   AND fx_eur.currency              = 'USD/EUR'     \r\n   AND tsh.date                     = fx_gbp.date\r\n   AND tsh.date                     = fx_eur.date\r\n```\r\n\r\nNow, let's deploy newly built models:\r\n\r\n```cmd\r\ndbt run --model +tfm_stock_history_major_currency\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image32.png) \r\n\r\nAs we now have more models in play, it is a good moment to talk about [dbt documentation](https://docs.getdbt.com/docs/building-a-dbt-project/documentation). By a run of new following commands dbt will analyze all models in our project and generate a static webpage with a data dictionary/documentation. This is a fantastic way of sharing information with your engineering & user community as it has 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 how big project grows, it is super easy to understand whats happening. And as cherry-on-pie there is also a possibility to see the full lineage of models in the visual DAG: \r\n\r\n```cmd\r\ndbt docs generate\r\ndbt docs serve\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image33.png) \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image34.png) \r\n\r\nLet's we go to Snowflake UI to check the results\r\n\r\n```sql\r\nSELECT * \r\n  FROM dbt_hol_dev.l20_transform.tfm_stock_history_major_currency\r\n WHERE company_ticker = 'AAPL'\r\n   AND date = '2021-03-01'\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image38.png) \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## dbt pipelines - Trading books\r\n\r\nFollowing our use case story, we are going to manually upload two small datasets using \r\n[dbt seed](https://docs.getdbt.com/docs/building-a-dbt-project/seeds) representing trading books of two desks. As you would notice, they were buying and selling AAPL shares, but logging the cash paid/received in different currencies: USD and GBP. \r\n\r\nFor this let's create two csv files with the following content:  \r\n\r\n- **seeds/manual_book1.csv**\r\n\r\n```csv\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\n- **seeds/manual_book2.csv**\r\n\r\n```csv\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\nOnce created, let's run the following command to load the data into Snowflake. It is important to mention that whilst this approach is absolutely feasible to bring low hundred-thousands of rows it is suboptimal to integrate larger data and you should be using COPY/Snowpipe or other data integration options recommended for Snowflake. \r\n\r\n```cmd\r\ndbt seed\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image35.png) \r\n\r\nTo simplify usage, let's create a model that would combine data from all desks. In this example we are going to see how **dbt_utils.union_relations** macro helps to automate code automation: \r\n\r\n- **models/l20_transform/tfm_book.sql**\r\n\r\n```SQL\r\n{{ dbt_utils.union_relations(\r\n    relations=[ref('manual_book1'), ref('manual_book2')]\r\n) }}\r\n```\r\n\r\nOnce we deploy this model, let's have a look what it is compiled into. For this, please open **target/run/dbt_hol/models/l20_transform/tfm_book.sql**. As you can see dbt automatically scanned stuctures of the involved objects, aligned all possible attributes by name and type and combined all datasets via UNION ALL. Comparing this to the size of code we entered in the model itself, you can imagine the amount of time saved by such automation.\r\n\r\n```cmd\r\ndbt run -m tfm_book\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image36.png) \r\n\r\nOkay. 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 we need to have rows for the days shares were HOLD. For this let's introduce some more models:\r\n\r\n- **models/l20_transform/tfm_daily_position.sql**\r\n\r\n```SQL\r\nWITH cst_market_days AS\r\n(\r\n    SELECT DISTINCT date\r\n    FROM {{ref('tfm_stock_history_major_currency')}} hist\r\n    WHERE hist.date \u003E= ( SELECT min(date) AS min_dt FROM {{ref('tfm_book')}}  )\r\n)\r\nSELECT\r\n    cst_market_days.date,\r\n    trader,\r\n    stock_exchange_name,\r\n    instrument,\r\n    book,\r\n    currency,\r\n    sum(volume) AS total_shares\r\nFROM cst_market_days\r\n   , {{ref('tfm_book')}} book\r\nWHERE book.date \u003C= cst_market_days.date\r\nGROUP BY 1, 2, 3, 4, 5, 6 \r\n```\r\n\r\n- **models/l20_transform/tfm_daily_position_with_trades.sql**\r\n\r\n```SQL\r\nSELECT book\r\n     , date\r\n     , trader\r\n     , instrument\r\n     , action\r\n     , cost\r\n     , currency\r\n     , volume\r\n     , cost_per_share\r\n     , stock_exchange_name\r\n     , SUM(t.volume) OVER(partition BY t.instrument, t.stock_exchange_name, trader ORDER BY t.date rows UNBOUNDED PRECEDING ) total_shares\r\n  FROM {{ref('tfm_book')}}  t\r\nUNION ALL   \r\nSELECT book\r\n     , date\r\n     , trader\r\n     , instrument\r\n     , 'HOLD' as 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\nFROM {{ref('tfm_daily_position')}} \r\nWHERE (date,trader,instrument,book,stock_exchange_name) \r\n      NOT IN \r\n      (SELECT date,trader,instrument,book,stock_exchange_name\r\n         FROM {{ref('tfm_book')}}\r\n      )\r\n```\r\n```cmd\r\ndbt run -m tfm_book+\r\n```\r\n\r\nNow let's go back to Snowflake worksheets and run a query to see the results:\r\n```sql\r\nSELECT * \r\n  FROM dbt_hol_dev.l20_transform.tfm_daily_position_with_trades\r\n WHERE trader = 'Jeff A.'\r\n ORDER BY date\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image37.png) \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## dbt pipelines - PnL calculation\r\n\r\nThis section should bring the last models to complete the story. \r\nNow we have trading history of our desks and stock price history. Let's create a model to show how Market Value and PnL were changing over time. For this we are going to start by creating a model:\r\n\r\n- **models/l20_transform/tfm_trading_pnl.sql**\r\n\r\n```sql\r\nSELECT t.instrument, t.stock_exchange_name, \r\n       t.date, trader, t.volume,cost, cost_per_share,currency,\r\n       SUM(cost) OVER(partition BY t.instrument, t.stock_exchange_name, trader ORDER BY t.date rows UNBOUNDED PRECEDING ) cash_cumulative,\r\n       CASE WHEN t.currency = 'GBP' THEN gbp_close\r\n            WHEN t.currency = 'EUR' THEN eur_close\r\n            ELSE close\r\n       END                                                        AS close_price_matching_ccy,     \r\n       total_shares  * close_price_matching_ccy                   AS market_value, \r\n       total_shares  * close_price_matching_ccy + cash_cumulative AS PnL\r\n   FROM       {{ref('tfm_daily_position_with_trades')}}    t\r\n   INNER JOIN {{ref('tfm_stock_history_major_currency')}}  s \r\n      ON t.instrument = s.company_ticker \r\n     AND s.date = t.date \r\n     AND t.stock_exchange_name = s.stock_exchange_name\r\n```\r\n\r\n- **models/l30_mart/fct_trading_pnl.sql**\r\n\r\nThis model will be the one we created in the mart area, prepared to be used by many. With that in mind, it will be good idea to materialize this model as a table with incremental load mode. You can see that this materialization mode has a special macro that comes into action for the incremental runs (and ignored during initial run and full_refresh option). \r\n\r\n```sql\r\n{{ \r\nconfig(\r\n   materialized='incremental'\r\n   , tags=[\"Fact Data\"]\r\n   ) \r\n}}\r\nSELECT src.*\r\n  FROM {{ref('tfm_trading_pnl')}} src\r\n\r\n{% if is_incremental() %}\r\n  -- this filter will only be applied on an incremental run\r\n WHERE (trader, instrument, date, stock_exchange_name) NOT IN (select trader, instrument, date, stock_exchange_name from {{ this }})\r\n\r\n{% endif %}\r\n```\r\n\r\nFinally, for illustration purposes we are going to create a couple of views that could be extended further, representing different lens of interpreting PnL data between treasury, risk and finance departments.\r\n\r\n- **models/l30_mart/fct_trading_pnl_finance_view.sql**\r\n\r\n```sql\r\nSELECT * \r\n-- this is a placeholder for illustration purposes\r\n  FROM {{ref('fct_trading_pnl')}} src\r\n```\r\n\r\n- **models/l30_mart/fct_trading_pnl_risk_view.sql**\r\n\r\n```sql\r\nSELECT * \r\n-- this is a placeholder for illustration purposes\r\n  FROM {{ref('fct_trading_pnl')}} src\r\n```\r\n\r\n- **models/l30_mart/fct_trading_pnl_treasury_view.sql**\r\n\r\n```sql\r\nSELECT * \r\n-- this is a placeholder for illustration purposes\r\n  FROM {{ref('fct_trading_pnl')}} src\r\n```\r\n\r\nLet's deploy all of these models and run a query to check the final results:\r\n\r\n```cmd\r\ndbt run \r\ndbt docs serve\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image40.png) \r\n\r\nThe final lineage tree: \r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image42.png) \r\n\r\n```sql\r\nSELECT * \r\n  FROM dbt_hol_dev.l30_mart.fct_trading_pnl\r\n WHERE trader = 'Jeff A.'\r\n ORDER by date\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image41.png) \r\n\r\nNow, let's create a simple data visualization for this dataset. For that, let's click on the Preview App button once again: \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image9.png) \r\n\r\nThen **Worksheets -\u003E + Worksheet**\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image49.png) \r\n\r\nThen let's copy-paste the same query we run in classic Snowflake UI worksheets. Hit the run button and switch from a table view to chart: \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image50.png) \r\n\r\nBy default it shows a breakdown by Volume. Let's click on the measure and switch it into **PNL**. Then let's add another measure to our chart for displaying Market value and PnL side by side. \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image51.png) \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image52.png) \r\n\r\nAnd this is it! Now you have a worksheet that you can slice'n'dice, share with your colleagues or embed in the SnowSight dashboard as one of the tiles. As you can see, Snowsight offers a great capability to quickly visualize the insight and always there for you as a part of the Snowflake platform. For more details on SnowSight, please refer to the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/ui-web.html). \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image53.png) \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Testing, Deployment, Materializations\r\n\r\n### Establishing Testing\r\nTo build trust in your data solution, it is hard to underestimate the importance of testing. While there are many ways to organize automated testing, thankfully dbt tool comes with the great [data tests framework](https://docs.getdbt.com/docs/building-a-dbt-project/tests). Let's build an example.\r\n\r\nFirst, let's add the test configuration file and add the content below. dbt comes with a set of pre-defined data tests, such as uniqueness, not_null, check constraints, ref integrity etc. We are going to set up tests on the few models, however it is highly recommended to establish reasonable test coverage across the board. \r\n- **models/tests/data_quality_tests.yml**\r\n\r\n```yml\r\nversion: 2\r\n\r\nmodels:\r\n  - name: tfm_fx_rates\r\n    columns:\r\n      - name: currency||date\r\n        tests:\r\n          - unique\r\n          - not_null\r\n\r\n  - name: tfm_book\r\n    columns:\r\n      - name: instrument\r\n        tests:\r\n          - not_null\r\n          - relationships:\r\n              to: ref('tfm_stock_history')\r\n              field: company_ticker\r\n\r\n  - name: tfm_stock_history\r\n    columns:\r\n      - name: company_ticker||date\r\n        tests:\r\n          - not_null\r\n          - unique\r\n```\r\nNext, let's run these tests:\r\n\r\n```cmd\r\ndbt test\r\n```\r\n\r\nBoom! One of the tests failed! Let's try to understand why. dbt command line is kindly provided a link to the file with the SQL check that failed. Let's open it and copy-paste the content to Snowflake worksheet:  \r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image54.png) \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image55.png) \r\n\r\nLet's quickly check the full row width for one of the records failed by extending this check towards something like this:\r\n\r\n```sql\r\nWITH cst AS\r\n(\r\n    select\r\n        company_ticker||date conctat\r\n\r\n    from dbt_hol_dev.l20_transform.tfm_stock_history\r\n    where company_ticker||date is not null\r\n    group by company_ticker||date\r\n    having count(*) \u003E 1 \r\n    limit 1\r\n)\r\nSELECT * FROM dbt_hol_dev.l20_transform.tfm_stock_history\r\n WHERE company_ticker||date IN (SELECT conctat FROM cst) \r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image56.png) \r\n\r\nAha! There are shares which are traded on more than one stock exchanges. So we need to include **stock_exchange_name** attribute to your unique test key. Let's go back to **models/tests/data_quality_tests.yml** and update the test configuration for **tfm_stock_history** model :\r\n\r\n\r\n\r\n```yml\r\n  - name: tfm_stock_history\r\n    columns:\r\n      - name: company_ticker||date||stock_exchange_name\r\n        tests:\r\n          - not_null\r\n          - unique\r\n```\r\nAnd run the test again\r\n\r\n```cmd\r\ndbt test\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image57.png) \r\n\r\nFinishing testing note, it is also worth mentioning that alongside such tests, dbt framework also supports custom tests that are massively expanding scenarios(like regression testing) could be covered by data tests. And just to expand it even further, in dbt hub there is a package [dbt_expectations](https://hub.getdbt.com/calogica/dbt_expectations/latest/) that implements a lot of additional tests, inspired by popular[http://greatexpectations.io/](http://greatexpectations.io/) framework. \r\n\r\n### Deployment\r\n\r\nOkay, seems like we have everything in place: pipelines been developed and tested. The next step would be to promote this code up the chain through our SDLC environments(which in this lab is simplified to just DEV & PROD). \r\n\r\nIn real life, the project code we are working should be in source version control system like git and by now pushed into one of the feature branches and merged into dev/trunk branch. From there, typically users raise pull requests to master/release version and then perform a deployment in production environment. Thanks to the fact dbt pipelines are very readable it is possible to implement good code review practices as well as set up automatic testing with various stages as a part of CICD automation. \r\n\r\nWorking with git and branches is not in scope of this lab so we will just run the following command to deploy the very same codebase to PROD environment. \r\n\r\n```cmd\r\ndbt seed --target=prod\r\ndbt run  --target=prod\r\n```\r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image58.png)\r\n\r\nWe can check the UI that now we have data in **dbt_hol_prod** database:\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image59.png) \r\n\r\n### Materialization & Scaling\r\n\r\ndbt provides the ability to easily change the [materialization option](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations), taking away all the burden related to generating new version of DDL & DML. What does it means for modern data engineering? You no longer need to spend precious time performing upfront performance optimization and rather focus on building models, bringing more insights to your business. And when it comes to understand the usage patterns, models that are heavy and/or accessed frequently could be selectively materialized. \r\n\r\nDuring the lab you've probably seen how easily Snowflake could deal with many models materialized as views, provided the input data volume of stock history is \u003E200Mn records alone. We also explicitly configured one model to be materialized as 'table'(CTAS) and another one as 'incremental'(MERGE). Once you move into persisted methods of materialization you will be using Snowflake virtual warehouses as compute power to perform the materialization. \r\n\r\nLet's have a look on a couple of ways to manage compute size Snowflake will dedicate to a specific model(s).\r\n\r\n1.. Let's open **dbt_projects.yml** and add the additional line \r\n\r\n```yml\r\nmodels:\r\n  dbt_hol:\r\n      # Applies to all files under models/example/\r\n      example:\r\n          materialized: view\r\n          +enabled: false\r\n      l10_staging:\r\n          schema: l10_staging\r\n          materialized: view\r\n      l20_transform:\r\n          schema: l20_transform\r\n          materialized: view\r\n          +snowflake_warehouse: dbt_dev_heavy_wh\r\n      l30_mart:\r\n          schema: l30_mart\r\n          materialized: view\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image60.png) \r\n\r\n2.. Let's modify the content of **models/l30_mart/fct_trading_pnl.sql** changing config section to include pre and post run hooks: \r\n\r\n```sql\r\n{{ \r\nconfig(\r\n   materialized='incremental'\r\n   , tags=[\"Fact Data\"]\r\n   , pre_hook =\"ALTER WAREHOUSE dbt_dev_wh SET WAREHOUSE_SIZE ='XXLARGE'\" \r\n      , post_hook=\"ALTER WAREHOUSE dbt_dev_wh SET WAREHOUSE_SIZE ='XSMALL'\"\r\n   ) \r\n}}\r\nSELECT src.*\r\n  FROM {{ref('tfm_trading_pnl')}} src\r\n\r\n{% if is_incremental() %}\r\n  -- this filter will only be applied on an incremental run\r\n WHERE (trader, instrument, date, stock_exchange_name) NOT IN (select trader, instrument, date, stock_exchange_name from {{ this }})\r\n\r\n{% endif %}\r\n```\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image62.png) \r\n\r\nNow let's run the project again:\r\n\r\n```cmd\r\ndbt run\r\n```\r\nOnce finished, lets' go into Snowflake UI and look at the Query History page. As you can see, dbt automatically switched into a separate warehouse **dbt_dev_heavy_wh** (of a larger size) once it started working on the models in **l20_transform** folder. Once it reached the **l30_mart/fct_trading_pnl** model, the pipeline increased the size of the **dbt_dev_wh** to 2XL, processed the model faster, and then decreased the size of compute back to XS to keep it economical. \r\n\r\n![Query Tag](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-teams-with-dbt-core/image61.png) \r\n\r\nThese are just a couple of examples how you could leverage elasticity and workload isolation of Snowflake compute by switching between or resizing virtual warehouses as a simple DDL command, embedded in your pipelines. \r\n\r\nWith that, let's move to our final section for this lab! \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion & Next Steps\r\n\r\nCongratulations on completing this lab using dbt and Snowflake for building data pipelines to drive analytics! You’ve mastered the dbt and Snowflake basics and are ready to apply these fundamentals to your own data. Be sure to reference this guide if you ever need a refresher.\r\n\r\nWe encourage you to continue with your free trial by loading your own sample or production data and by using some of the more advanced capabilities of dbt and Snowflake not covered in this lab. \r\n### Additional Resources:\r\n\r\n- Read the [Definitive Guide to Maximizing Your Free Trial](/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/) document\r\n- Attend a [Snowflake virtual or in-person event](/about/events/) to learn more about our capabilities and customers\r\n- [Join the Snowflake community](https://community.snowflake.com/s/topic/0TO0Z000000wmFQWAY/getting-started-with-snowflake)\r\n- [Sign up for Snowflake University](https://community.snowflake.com/s/article/Getting-Access-to-Snowflake-University)\r\n- [Contact our Sales Team](/free-trial-contact-sales/) to learn more\r\n- [Join dbt community slack](https://community.getdbt.com/) where thousands of dbt on Snowflake users discussing their best practices\r\n\r\n### What we've covered:\r\n\r\n- How to set up dbt & Snowflake\r\n\r\n- How to leverage data in Snowflake's Data Marketplace \r\n\r\n- How to run a dbt project and develop pipelines\r\n\r\n- How to create data tests\r\n\r\n- How to leverage Snowflake elasticity and scalability to support dbt calculations at scale","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c6b718cbb4","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-57fa8ea9ad",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-4113f67075","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-05-01",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-94e5df970b","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-87fcb38190",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-faeb5dfb56",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-f61a83f7ed","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-b17d9ace3d","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/data-teams-with-dbt-core",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESnowflake Configuration\u003C/h2\u003E","\u003Ch2\u003Edbt Configuration\u003C/h2\u003E","\u003Ch2\u003EArchitecture and Use Case Overview\u003C/h2\u003E","\u003Ch2\u003EConnect to Data Sources\u003C/h2\u003E","\u003Ch2\u003EBuilding dbt Data Pipelines\u003C/h2\u003E","\u003Ch2\u003Edbt pipelines - Stock trading history\u003C/h2\u003E","\u003Ch2\u003Edbt pipelines - Currency exchange rates\u003C/h2\u003E","\u003Ch2\u003Edbt pipelines - Trading books\u003C/h2\u003E","\u003Ch2\u003Edbt pipelines - PnL calculation\u003C/h2\u003E","\u003Ch2\u003ETesting, Deployment, Materializations\u003C/h2\u003E","\u003Ch2\u003EConclusion & Next Steps\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-52d8ccb757","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/data-teams-with-dbt-core",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"],":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container"}},":itemsOrder":["quickstart_table_of_"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-d10eba9458","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:1fr 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class=\"sf-footer__column-title\"\u003EProduct\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003EPlatform\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca 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