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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn 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Raudszus","quickstartHeroForkRepoLink":{"id":"button-82447816f5","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/getting-started-with-snowflake-devops"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Getting Started with Snowflake DevOps","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/getting-started-with-snowflake-devops","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-aa542907fd","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-658a56c991",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-15393c70de","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EA data pipeline is a series of automated processes that ingest, process, and move data from one place to another. It's a crucial component in data management and analytics, enabling organizations to make use of the vast amounts of data available to them.\u003C/p\u003E\n","\u003Cp\u003EThe data engineer’s work doesn’t end when the pipeline is up and running for the first time. As businesses grow and change, so do their data needs. Data schemas as well as the pipeline as a whole need to evolve to capture new data sources or analyst requirements. In software engineering, DevOps techniques are applied to emphasize collaboration and communication to make changes rapidly and reliably. Similarly, DevOps best practices should be followed to ensure that changes to tables, data pipelines, and other database objects are made in a controlled, repeatable, and safe manner.\u003C/p\u003E\n","\u003Cp\u003EAre you interested in bringing your database game to the next level by unleashing the power of Snowflake’s many DevOps capabilities, empowering you to build safe and robust data pipelines? Well then, this Quickstart is for you!\u003C/p\u003E\n","\u003Cp\u003EThis quickstart will cover a lot of ground. By the end of this guide, you will have built a robust CI/CD pipeline that automates the deployment of changes to a data pipeline from Git, promotes the separation of dev and prod databases and facilitates monitoring. You'll also learn how to use Snowflake's marketplace to enrich your data without incurring storage costs, get to know the new developer CLI tool and the Visual Studio Code extension!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/devops-overview.png?v=4185b8a0\" alt=\"DevOps Overview\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe data pipeline used as an example in this quickstart makes use of data available on the Snowflake marketplace to come up with the perfect spot for a family vacation. Let's get started!\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: The Git integration, Create-or-Alter and Jinja templating are still in preview.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake\u003C/li\u003E\u003Cli\u003EFundamentals of data engineering. You can use \u003Ca href=\"/en/developers/guides/data-engineering-with-snowpark-python-intro/\"\u003EIntro to Data Engineering with Snowpark Python\u003C/a\u003E to refresh your memory\u003C/li\u003E\u003Cli\u003EFamiliarity with Git repositories and GitHub\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat you will learn\u003C/h3\u003E\n","\u003Cp\u003EYou will learn about the following Snowflake features in this Quickstart:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENative Git Integration\u003C/li\u003E\u003Cli\u003EDeclarative Database Change Management\u003C/li\u003E\u003Cli\u003EParametrization of SQL scripts with Jinja templating\u003C/li\u003E\u003Cli\u003EGitHub Actions (CI/CD) integration\u003C/li\u003E\u003Cli\u003ESnowflake’s Visual Studio Code extension\u003C/li\u003E\u003Cli\u003ESnowflakeCLI tool\u003C/li\u003E\u003Cli\u003ESnowflake's Python APIs\u003C/li\u003E\u003Cli\u003EData Sharing/Marketplace (instead of ETL)\u003C/li\u003E\u003Cli\u003ELLM interference with Snowflake Cortex\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat you will need\u003C/h3\u003E\n","\u003Cp\u003EYou will need the following things before starting:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake Account\u003C/a\u003E\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA Snowflake user with ACCOUNTADMIN permissions\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnaconda Terms & Conditions accepted\u003C/strong\u003E. See \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started\"\u003EUsing Third-Party Packages\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EGitHub\n\u003Cul\u003E\u003Cli\u003EA GitHub account. If you don't already have one, you can \u003Ca href=\"https://github.com/\"\u003Ecreate one for free\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat you will build\u003C/h3\u003E\n","\u003Cp\u003EDuring this Quickstart you will accomplish the following things:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEstablish a connection between Snowflake and a GitHub repository\u003C/li\u003E\u003Cli\u003ESetup access to Snowflake Marketplace data\u003C/li\u003E\u003Cli\u003ECreate a vectorized Python UDF making use of dynamic file access\u003C/li\u003E\u003Cli\u003ECreate a data engineering pipeline to process data from multiple sources\u003C/li\u003E\u003Cli\u003EOrchestrate the pipeline with tasks\u003C/li\u003E\u003Cli\u003EMake declarative changes to the pipeline with Create-or-Alter and Python APIs\u003C/li\u003E\u003Cli\u003ESeparate dev and prod environments with Jinja templating\u003C/li\u003E\u003Cli\u003EDeploy the pipeline via an automated CI/CD pipeline\u003C/li\u003E\u003Cli\u003EEnrich your data with Cortex LLM functions\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Environment\u003C/h2\u003E\n","\u003Ch3\u003EFork the Quickstart Repository\u003C/h3\u003E\n","\u003Cp\u003EYou'll need to create a fork of the repository for this Quickstart in your GitHub account. Visit the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-devops\"\u003Eassociated GitHub repository\u003C/a\u003E and click on the "Fork" button near the top right. Complete any required fields and click "Create Fork".\u003C/p\u003E\n","\u003Ch3\u003ECreate GitHub Codespace\u003C/h3\u003E\n","\u003Cp\u003EFor this Quickstart we will be using \u003Ca href=\"https://docs.github.com/en/codespaces/overview\"\u003EGitHub Codespaces\u003C/a\u003E as our development environment. Codespaces offer a hosted development environment with a hosted, web-based VS Code environment. The free offering should be more than enough for this quickstart.\u003C/p\u003E\n","\u003Cp\u003ETo create a GitHub Codespace, click on the green “<> Code” button from the forked repository’s homepage. In the Code popup, click on the green “Create codespace on main”.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/start-codespace.png?v=4185b8a0\" alt=\"Start GitHub Codespace\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will open a new tab and begin setting up your codespace. This will take a few minutes as it sets up the entire environment for this Quickstart.\u003C/p\u003E\n","\u003Cp\u003EOnce the codespace has been created and started, you should see a hosted web-based version of VS Code with your forked repository set up! Snowflake’s VS Code extension and the Snowflake CLI have already been installed for you.\u003C/p\u003E\n","\u003Ch3\u003ESnowflake Extension for VS Code\u003C/h3\u003E\n","\u003Cp\u003EYou can run SQL queries against Snowflake in many different ways (through the Snowsight UI, SnowSQL, etc.) but for this Quickstart we'll be using the Snowflake extension for VS Code. For a brief overview of Snowflake's native extension for VS Code, please check out our \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc\"\u003EVS Code Marketplace Snowflake extension page\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ESnowflake CLI\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/index\"\u003ESnowflake CLI\u003C/a\u003E is an open-source command-line tool explicitly designed for developer-centric workloads in addition to SQL operations. With Snowflake CLI, developers can create, manage, update, and view apps running on Snowflake across workloads such as Streamlit in Snowflake, the Snowflake Native App Framework, Snowpark Container Services, and Snowpark.\u003C/p\u003E\n","\u003Cp\u003EHere we will make use of the CLI to quickly setup a CI/CD pipeline.\u003C/p\u003E\n","\u003Ch3\u003EConnecting to Snowflake\u003C/h3\u003E\n","\u003Cp\u003EBoth the VS Code extension and the CLI manage their Snowflake connections in a shared file. Open \u003Ccode\u003E.snowflake/config.toml\u003C/code\u003E and add your \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier\"\u003Eaccount identifier\u003C/a\u003E, username and password in the corresponding placeholders. Note that the user must have ACCOUNTADMIN privileges. Make sure to not commit this file to Git.\u003C/p\u003E\n","\u003Cp\u003ENow use these credentials to login with the VS Code extension, by clicking the Snowflake logo in the activity bar to the left of the editor. The dialog should have been pre-filled from the config file. Press "Sign in" and you are good to go.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Snowflake\u003C/h2\u003E\n","\u003Cp\u003ETo get started we need to create some basic Snowflake objects like databases, stages and a warehouse that we will use later on. Open \u003Ccode\u003Esteps/01_setup_snowflake.sql\u003C/code\u003E to review what statements will be executed.\u003C/p\u003E\n","\u003Ch3\u003ECreate and Connect a Git Repository\u003C/h3\u003E\n","\u003Cp\u003EThis quickstart makes extensive use of the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/git/git-overview\"\u003EGIT REPOSITORY\u003C/a\u003E object. This object acts similarly to an external stage except that it is backed by a GitHub repository. It enables the access to version controlled files at a certain commit, branch or tag. This way your database scripts and object definitions can be evolved with software engineering best practices such as branching, pull requests and code reviews.\u003C/p\u003E\n","\u003Cp\u003EBefore continuing, \u003Cstrong\u003Ereplace the two placeholders\u003C/strong\u003E in \u003Ccode\u003Esteps/01_setup_snowflake.sql\u003C/code\u003E with your GitHub username and the url of the forked repository.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE API INTEGRATION git_api_integration\n  API_PROVIDER = git_https_api\n  API_ALLOWED_PREFIXES = ('https://github.com/<insert GitHub username>')\n  ENABLED = TRUE;\n\nCREATE OR REPLACE GIT REPOSITORY quickstart_common.public.quickstart_repo\n  API_INTEGRATION = git_api_integration\n  ORIGIN = '<insert URL of forked GitHub repo>';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun the Script\u003C/h3\u003E\n","\u003Cp\u003EAfter replacing the git-related placeholders, it is time to execute the script to create all the objects we'll need for this quickstart.\u003C/p\u003E\n","\u003Cp\u003EStart by opening the \u003Ccode\u003Esteps/01_setup_snowflake.sql\u003C/code\u003E script. Now you can run all the queries in this script, by using the "Execute All Statements" button in the upper right corner of the editor window. If you want to run them in chunks instead, you can highlight the ones you want to run and press “CMD/CTRL+Enter”.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Data from the Marketplace\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we will be "loading" the raw data we need to find the perfect vacation spot to Snowflake. But "loading" is really the wrong word here. Because as we're using Snowflake's unique data sharing capability, we don't actually need to copy any data to our Snowflake account. Instead, we can directly access the data shared by various data providers in the Snowflake Data Marketplace.\u003C/p\u003E\n","\u003Cp\u003EAs we are looking to find the perfect vacation spot, we are interested in:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe weather forecast\u003C/li\u003E\u003Cli\u003EFlight connections\u003C/li\u003E\u003Cli\u003EPoints of interest\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESnowflake Data Marketplace\u003C/h3\u003E\n","\u003Cp\u003EAll this data is freely available on Snowflake’s marketplace, and we won’t incur any storage costs as long as we don’t materialize it in our account. The other great thing about the marketplace is that we don't have to do anything to keep this data updated. The provider will do that for us, and data sharing means we are always seeing whatever they have published. How amazing is that? Just think of all the additional insights you could extract from your data if only you had the necessary, always up-to-date supplementary data available.\u003C/p\u003E\n","\u003Cp\u003EFor this quickstart we will add a number of data sets to our account. The process is always the same:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELogin to Snowsight\u003C/li\u003E\u003Cli\u003EClick on the “Data Products” link in the left navigation bar\u003C/li\u003E\u003Cli\u003EEnter the name of the data set in the search box and click return\u003C/li\u003E\u003Cli\u003EClick on the corresponding listing tile\u003C/li\u003E\u003Cli\u003EClick the blue "Get" button on the top right\u003C/li\u003E\u003Cli\u003EOn the popup, click the blue "Get" button\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENow, perform these steps for the following data listings:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E“OAG: Flight Emissions Data (Sample)” by OAG\u003C/li\u003E\u003Cli\u003E“OAG: Flight Status Data (Sample)” by OAG\u003C/li\u003E\u003Cli\u003E“Global Weather & Climate Data for BI” by Weather Source\u003C/li\u003E\u003Cli\u003E“Snowflake Public Data (Free)” by Snowflake Public Data Products\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EInspect Imported Listings\u003C/h3\u003E\n","\u003Cp\u003EThat’s all! The imported listings are now available in our account and can be accessed like any other table. Use the following sample queries to inspect the imported data and verify that all data sets are accessible. You can also find the sample queries in \u003Ccode\u003Esteps/02_access_marketplace_data.sql\u003C/code\u003E of the Git repository.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM oag_flight_emissions_data_sample.public.estimated_emissions_schedules_sample LIMIT 100;\nSELECT * FROM oag_flight_status_data_sample.public.flight_status_latest_sample LIMIT 100;\nSELECT * FROM global_weather__climate_data_for_bi.standard_tile.forecast_day LIMIT 100;\nSELECT * FROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.datacommons_timeseries LIMIT 100;\nSELECT * FROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.point_of_interest_index LIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHarmonize Marketplace Data\u003C/h2\u003E\n","\u003Cp\u003EThe step will establish a data pipeline that will help us answer the question of what the perfect vacation spot is by joining together the different data sources from the market place, aggregating data points and picking only the columns and rows we need.\u003C/p\u003E\n","\u003Cp\u003EWhile most parts of our data pipeline are authored in SQL, \u003Ccode\u003Esteps/03_harmonize_data.py\u003C/code\u003E is written in Python. Snowflake supports seamlessly switching between SQL and Python when authoring data pipelines. Here, \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-python-api/snowflake-python-overview\"\u003ESnowflake's first class PythonAPI's\u003C/a\u003E are used to manage creations, alterations, or drops of Snowflake objects just like you would otherwise do with SQL commands.\u003C/p\u003E\n","\u003Cp\u003EWe will skip the details of how the pipeline is constructed to focus on how we can employ DevOps practices to later safely evolve and manage the pipeline. If you want to learn more about the Python APIs check the tutorials \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-python-api/overview-tutorials\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ERun the Script\u003C/h3\u003E\n","\u003Cp\u003ETo establish the pipeline, we need to run the commands in the Python file \u003Ccode\u003Esteps/03_harmonize_data.py\u003C/code\u003E. Open the file in VS Code. Next, click on “Run Python File” to run the script.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOrchestrate Jobs\u003C/h2\u003E\n","\u003Cp\u003EDuring this step we will be orchestrating our new pipeline with Snowflake's native orchestration feature named “tasks”. In this step, we will create two tasks and chain them together. We will then deploy or run the tasks to operationalize the data pipeline.\u003C/p\u003E\n","\u003Cp\u003EThe job of the first task “vacation_spots_update” is joining the views created in the previous step and merge the result into the output table “vacation_spots”. The second task “email_notification” runs after the update task has finished. It selects a number of suitable places for a summer vacation and feeds the results into an LLM to get a final verdict and turn the recommendation into an entire vacation plan. The answer the LLM comes up with is sent via email, informing you of the pipelines completion and providing you with the ultimate vacation plan. No more stressful vacation planning, just book your flights and hotel, share the email with friends and family and off you go!\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will run through the commands in the Python file \u003Ccode\u003Esteps/04_orchestrate_jobs.sql\u003C/code\u003E. Open the file in VS Code and \u003Cstrong\u003Eadd your email address to the three placeholders\u003C/strong\u003E. Then execute the file by clicking on on “Execute All Statements” to run all the commands sequentially.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: The Snowflake Cortex LLM functions are currently not available in all regions. Check the \u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/llm-functions#availability\"\u003Edocs\u003C/a\u003E to verify your region is supported. If not, it is up to you to pick your favorite vacation spot from the results.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ETip: The query used in the “email_notification” task selects suitable places for a summer vacation. If you are running the pipeline in the colder months, it might not return any results. In that case, adapt the filters and off you go on your winter vacation! You might want to add snowfall days to your weather metrics.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EDeclarative Create-or-Alter Command\u003C/h3\u003E\n","\u003Cp\u003EWhen creating the target table and the tasks orchestrating the pipeline, we make use of the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-table#create-or-alter-table\"\u003ECREATE OR ALTER\u003C/a\u003E command. CREATE OR ALTER behaves similarly to CREATE OR REPLACE except that an existing object is not replaced, but rather altered to the target state.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or alter table vacation_spots (\n  city varchar,\n  airport varchar,\n  co2_emissions_kg_per_person float,\n  punctual_pct float,\n  avg_temperature_air_f float,\n  avg_relative_humidity_pct float,\n  avg_cloud_cover_pct float,\n  precipitation_probability_pct float\n) data_retention_time_in_days = 1;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: The Create-or-Alter command is currently not publicly available for all object types. Check the docs for currently supported objects types and contact your account manager if you want to join the extended preview.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EVerify Pipeline Completion\u003C/h3\u003E\n","\u003Cp\u003EBy this time the pipeline should has completed processing. You should have received an email with all the details for your dream vacation. If you haven’t received an email, use the following query to see what is going on:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM TABLE(QUICKSTART_PROD.INFORMATION_SCHEMA.TASK_HISTORY(\n    SCHEDULED_TIME_RANGE_START=>DATEADD('DAY',-1,CURRENT_TIMESTAMP()),\n    RESULT_LIMIT => 100))\nORDER BY SCHEDULED_TIME DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck out \u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003Ethis\u003C/a\u003E guide to learn more about the powerful Snowflake features enabling you to monitor the execution of your tasks.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDatabase Change Management\u003C/h2\u003E\n","\u003Cp\u003EJust as business needs change, so do data needs. Over time our data pipeline will need to adapt to capture new or differently formatted data. It is our responsibility as data engineers to make sure that these changes can be made in a safe manner. In DevOps, Infrastructure as Code (IaC) is a common pattern applied when making infrastructure changes. Managing our database resources in a common, versioned source allows us to effectively collaborate and have a source off truth that is consistent across all deployments. IaC if often done in a declarative manner using tools such as \u003Ca href=\"/en/developers/guides/devops-dcm-terraform-github/\"\u003Eterraform\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EDeclarative definitions are concise, idempotent and easy to understand as we don’t need to understand the entire stack of historic changes leading up to the current state as in tools like \u003Ca href=\"/en/developers/guides/devops-dcm-schemachange-github/\"\u003Eschemachange\u003C/a\u003E. Rolling back multiple ALTER statements to get back to a previous state can be a pain.\u003C/p\u003E\n","\u003Ch3\u003EDeclarative Pipeline Evolution\u003C/h3\u003E\n","\u003Cp\u003EThis is where the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-table#create-or-alter-table\"\u003ECREATE OR ALTER\u003C/a\u003E command shines. It allows us to have one single source of truth for the current state of the object. Potential rollbacks are super easy to do thanks to the straightforward integration with version control tools such as Git. Just apply an older version of the CREATE OR ALTER. This makes CREATE OR ALTER a great choice to manage data pipelines.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: The Create-or-Alter command is currently not publicly available for all object types. Check the docs for currently supported objects types and contact your account manager if you want to join the extended preview.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAdding a new Data Source to the Pipeline\u003C/h3\u003E\n","\u003Cp\u003EUntil now our vacation spot pipeline did only consider the weather and the reachability of potential locations. But what about activities during the stay? After all, what is a vacation without a visit to at least one Korean restaurant? And it’s a family vacation, so how about a visit to a zoo or an aquarium?\u003C/p\u003E\n","\u003Cp\u003ELet’s add data on these points of interest to our pipeline! \u003Cstrong\u003EFollow along and perform the described steps\u003C/strong\u003E yourself in VS Code.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn DevOps it is always a good practice to make our changes on a development branch to properly test them before merging to main. Let’s create a new branch for our changes. Open the terminal of your VS Code codespace and run:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit checkout -b dev\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EOpen \u003Ccode\u003Esteps/03_harmonize_data.py\u003C/code\u003E and add the following view definition at the end of the file. This view makes use of the “US Points of Interest & Addresses” data set we previously imported from the marketplace to find the number of Korean restaurants, zoos and aquariums for each potential vacation spot.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003EView(\n    name="attractions",\n    columns=[\n        ViewColumn(name="geo_id"),\n        ViewColumn(name="geo_name"),\n        ViewColumn(name="aquarium_cnt"),\n        ViewColumn(name="zoo_cnt"),\n        ViewColumn(name="korean_restaurant_cnt"),\n    ],\n    query="""\n    select\n        city.geo_id,\n        city.geo_name,\n        count(case when category_main = 'Aquarium' THEN 1 END) aquarium_cnt,\n        count(case when category_main = 'Zoo' THEN 1 END) zoo_cnt,\n        count(case when category_main = 'Korean Restaurant' THEN 1 END) korean_restaurant_cnt,\n    from SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.point_of_interest_index poi\n    join SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.point_of_interest_addresses_relationships poi_add \n        on poi_add.poi_id = poi.poi_id\n    join SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.us_addresses address \n        on address.address_id = poi_add.address_id\n    join major_us_cities city on city.geo_id = address.id_city\n    where true\n        and category_main in ('Aquarium', 'Zoo', 'Korean Restaurant')\n        and id_country = 'country/USA'\n    group by city.geo_id, city.geo_name\n    """,\n),\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ENext, let’s update the schema of our target table to account for the new columns. Head over to \u003Ccode\u003Esteps/04_orchestrate_jobs.sql\u003C/code\u003E and modify the CREATE OR ALTER on top by appending the following columns to its column list:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E, aquarium_cnt int\n, zoo_cnt int\n, korean_restaurant_cnt int\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EThen update the MERGE statement in task “vacation_spots_update” by adding a JOIN with the new view.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ejoin silver.attractions att on att.geo_name = city.geo_name\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EAdd the new columns to the WHEN MATCHED clause of the same MERGE statement.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E, vacation_spots.aquarium_cnt = harmonized_vacation_spots.aquarium_cnt\n, vacation_spots.zoo_cnt = harmonized_vacation_spots.zoo_cnt\n, vacation_spots.korean_restaurant_cnt = harmonized_vacation_spots.korean_restaurant_cnt\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EDon’t forget to also add the new columns to the MERGE statement’s WHEN NOT MATCHED clause.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E, harmonized_vacation_spots.aquarium_cnt\n, harmonized_vacation_spots.zoo_cnt\n, harmonized_vacation_spots.korean_restaurant_cnt\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EFinally, update the WHERE clause of the filter query in task “email_notification” to filter for vacation spots that have a Korean restaurant as well as either a zoo or an aquarium for the kids to enjoy.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eand korean_restaurant_cnt > 0\nand (zoo_cnt > 0 or aquarium_cnt > 0)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDeploy Pipeline Changes\u003C/h3\u003E\n","\u003Cp\u003EAfter accounting for the new data in our pipeline it is time to deploy the new version. First commit your changes to the Git repository. Run the following commands in the VS Code terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit add steps/*\ngit commit -m "added attractions to pipeline"\ngit push --set-upstream origin dev\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, deploy the new version of the data pipeline manually. Open the terminal in VS Code and run \u003Ccode\u003Esnow git fetch quickstart_common.public.quickstart_repo\u003C/code\u003E to fetch the changes from GitHub to Snowflake. Finally, run \u003Ccode\u003Esnow git execute @quickstart_common.public.quickstart_repo/branches/dev/steps/0[134]_*\u003C/code\u003E. The command reexecutes \u003Ccode\u003Esteps/01_setup_snowflake.sql\u003C/code\u003E to apply changes to the foundational objects, \u003Ccode\u003Esteps/03_harmonize_data.py\u003C/code\u003E to update the views used to transform the marketplace data and \u003Ccode\u003Esteps/04_orchestrate_jobs.sql\u003C/code\u003E to apply our changes to the target table and update and execute the two tasks.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow git fetch quickstart_common.public.quickstart_repo\n+------------------------+\n| scope  | name | result |\n|--------+------+--------|\n| Branch | dev  | FORCED |\n+------------------------+\n\n$ snow git execute '@quickstart_common.public.quickstart_repo/branches/dev/steps/0[134]_*'\nSUCCESS - @quickstart_common.public.quickstart_repo/branches/dev/steps/01_setup_snowflake.sql\nSUCCESS - @quickstart_common.public.quickstart_repo/branches/dev/steps/03_harmonize_data.py\nSUCCESS - @quickstart_common.public.quickstart_repo/branches/dev/steps/04_orchestrate_jobs.sql\n+--------------------------------------------------------------------------------------------------------+\n| File                                                                                 | Status  | Error |\n|--------------------------------------------------------------------------------------+---------+-------|\n| @quickstart_common.public.quickstart_repo/branches/dev/steps/01_setup_snowflake.sql  | SUCCESS | None  |\n| @quickstart_common.public.quickstart_repo/branches/dev/steps/03_harmonize_data.py    | SUCCESS | None  |\n| @quickstart_common.public.quickstart_repo/branches/dev/steps/04_orchestrate_jobs.sql | SUCCESS | None  |\n+--------------------------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf done correctly, you will shortly receive another email with your updated vacation plan.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESeparate Dev and Prod Environments\u003C/h2\u003E\n","\u003Cp\u003EIn the previous section we did not pause to verify our changes before deploying them to Snowflake. In this section, we will discuss how to properly test the changes before deploying them to our production database.\u003C/p\u003E\n","\u003Cp\u003EDevOps emphasizes the use of separate environments for development, testing, and production to minimize the risk of bugs or regressions affecting live systems. By maintaining separate dev and prod databases, teams can isolate development activities from the production environment, reducing the likelihood of unintended consequences and data corruption. A common pattern to separate production from development environments is using separate databases.\u003C/p\u003E\n","\u003Cp\u003EThere is a problem preventing us from doing so: Our pipeline definitions contain hard references to the production database “quickstart_prod”. We need to parametrize these references to enable a CI/CD pipeline to choose the deployment target.\u003C/p\u003E\n","\u003Ch3\u003EJinja Templating in Snowflake\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://jinja.palletsprojects.com/en/3.1.x/\"\u003EJinja\u003C/a\u003E is a popular templating language with features ranging from simple script parametrization to extensive scripting in a language similar to Python. Snowflake supports the execution of templated SQL scripts with the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/execute-immediate-from\"\u003EEXECUTE IMMEDIATE FROM\u003C/a\u003E command.\u003C/p\u003E\n","\u003Cp\u003EFor the use case of changing the deployment target, variable replacement is all we need from Jinja. Jinja variables in SQL scripts are marked by \u003Ccode\u003E{{...}}\u003C/code\u003E. We can provide a value for a variable in the USING clause of EXECUTE IMMEDIATE FROM. Template variables can be used everywhere is a SQL script. We can use variables to inject object names, property values or even entire statements.\u003C/p\u003E\n","\u003Ch3\u003EParameterize the Deployment Target\u003C/h3\u003E\n","\u003Cp\u003EA number of small changes are necessary to parametrize which database the data pipeline gets deployed to. To get more control over our storage costs, we will also parametrize the retention time for the data in our target table.\u003C/p\u003E\n","\u003Cp\u003ELet’s perform the necessary changes! Follow along and perform the described steps yourself in VS Code.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen \u003Ccode\u003Esteps/01_setup_snowflake.sql\u003C/code\u003E and parametrize part the database name in the CREATE OR REPLACE DATABASE QUICKSTART_PROD command.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR ALTER DATABASE QUICKSTART_{{environment}}; \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EOpen \u003Ccode\u003Esteps/03_harmonize_data.py\u003C/code\u003E and read the environment variable.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-py\"\u003Esilver_schema = root.databases[f"quickstart_{os.environ['environment']}"].schemas["silver"]\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EOpen \u003Ccode\u003Esteps/04_orchestrate_jobs.sql\u003C/code\u003E and parametrize the USE SCHEMA command.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse schema quickstart_{{environment}}.gold;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EFinally, in the same file, parametrize the value of the “data_retention_time_in_days” parameter in the CREATE OR ALTER TABLE command.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Edata_retention_time_in_days = {{retention_time}};\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeployment Automation\u003C/h2\u003E\n","\u003Cp\u003EIn this section we will use the Jinja templating to automatically deploy the pipeline using GitHub Actions. We build a CI/CD pipeline to deploy the data pipeline to the “prod” database when changes are pushed to the repository’s “main” branch. Thanks to templating, commits to the “dev” branch are deployed to the “dev” database instead.\u003C/p\u003E\n","\u003Ch3\u003EConfiguring Your Forked GitHub Project\u003C/h3\u003E\n","\u003Cp\u003EBy default, GitHub disables any workflows (or CI/CD pipelines) when forking a repository. The forked repository contains a CI/CD workflow to deploy your data pipeline to dev and prod environments. Enable the workflows by opening your forked repository in GitHub, clicking on the “Actions” tab near the top middle of the page, and then clicking on the “I understand my workflows, go ahead and enable them” green button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/enable-workflows.png?v=4185b8a0\" alt=\"Enable GitHub Actions\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn order for your GitHub Actions workflow to connect to your Snowflake account you will need to store your Snowflake credentials in GitHub. Action Secrets in GitHub are used to securely store values/variables for use in CI/CD pipelines.\u003C/p\u003E\n","\u003Cp\u003EIn GitHub, click on the “Settings” tab near the top of the page. From the Settings page, click on the “Secrets and variables” then “Actions” tab in the left hand navigation. The “Secrets” tab should be selected. For each secret listed below click on the green “New repository secret” and enter the name given below along with the appropriate value (adjusting as appropriate).\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESecret name\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESecret value\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESNOWFLAKE_ACCOUNT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Emyaccount\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESNOWFLAKE_USER\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Emyusername\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESNOWFLAKE_PRIVATE_KEY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Econtents of private key file (follow \u003Ca href=\"https://docs.snowflake.com/en/user-guide/key-pair-auth\"\u003Ethis guide\u003C/a\u003E to setup key-pair auth)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: For more details on how to structure the account name in SNOWFLAKE_ACCOUNT secret, see the account name discussion in the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-api#usage-notes-for-the-account-parameter-for-the-connect-method\"\u003ESnowflake Python Connector install guide\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWhen you're finished adding all the secrets, the page should look like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/action-secrets.png?v=4185b8a0\" alt=\"GitHub Actions Secrets\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EDeploy to Development Environment with CI/CD Pipeline\u003C/h3\u003E\n","\u003Cp\u003EThe CI/CD pipeline is ready to use. Let’s push our changes to GitHub and let the CI/CD pipeline do its thing. Open the terminal of the VS Code codespace and execute the following commands.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit add steps/*\ngit commit -m "parametrize pipeline"\ngit push\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe CI/CD pipeline will pick us the changes and run the workflow defined in \u003Ccode\u003E.github/workflows/deploy_pipeline.yml\u003C/code\u003E. To view the results go back to the homepage of your GitHub repository and do the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFrom the repository, click on the Actions tab near the top middle of the page\u003C/li\u003E\u003Cli\u003EClick on the name of the most recent run (which should have the name “parametrize pipeline”)\u003C/li\u003E\u003Cli\u003EFrom the run overview page click on the “deploy” job and browse through the output from the various steps. In particular you might want to review the output of the “Deploy data pipeline to dev” step.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/pipeline-output.png?v=4185b8a0\" alt=\"CI/CD Pipeline Output\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe workflow should indicate a successful execution. If this is the case, you will receive the familiar email containing the vacation plan after about a minute. Great! We just successfully tested our parametrization changes in a dedicated test environment.\u003C/p\u003E\n","\u003Ch3\u003EDeploy to Production Environment with CI/CD Pipeline\u003C/h3\u003E\n","\u003Cp\u003EAfter validating the changes, we can confidently merge them to our production database. Normally, at this point we would open a pull request to get our changes reviewed. Let’s speed this up and merge directly to main by executing the following commands in the VS Code terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit checkout main\ngit merge dev\ngit push\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETime to check GitHub’s “Actions” tab again. Another run of the “Deploy data pipeline” has appeared. Since we have dutifully tested our changes beforehand, we can confidently expect the vacation plan email to arrive shortly indicating a successful run.\u003C/p\u003E\n","\u003Cp\u003ETo save storage costs, we also used templating to parametrize the data retention time of the target table “vacation_spots”. The CI/CD pipeline did set the retention time to 0 days for the dev environment, while the prod environment stayed at the default of 1 day. Confirm this with the following commands. Note, that as the production pipeline might still be running, the second command might throw an error initially.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eshow parameters like 'data_retention_time_in_days' in table quickstart_dev.gold.vacation_spots;\n> returns 0\nshow parameters like 'data_retention_time_in_days' in table quickstart_prod.gold.vacation_spots;\n> returns 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECI/CD Pipeline using SnowflakeCLI\u003C/h3\u003E\n","\u003Cp\u003EWhile the pipeline is running, let’s understand how it works. Open \u003Ccode\u003E.github/workflows/deploy_pipeline.yml\u003C/code\u003E in VS Code. The workflow is triggered on pushes to “main” or “dev”, sets up the SnowflakeCLI and stores the GitHub Actions secrets in environment variables. This environment is then used to execute two commands on Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Ename: Deploy data pipeline\n\n# Action will run on pushes to selected branches\non:\n  push:\n    branches:\n      - dev\n      - main\n\n  # Allows you to run this workflow manually from the Actions tab\n  workflow_dispatch:\n\njobs:\n  deploy:\n    runs-on: ubuntu-22.04\n\n    env:\n      REPO_NAME: "quickstart_common.public.quickstart_repo"\n      SNOWFLAKE_CONNECTIONS_DEFAULT_AUTHENTICATOR: "SNOWFLAKE_JWT"\n      SNOWFLAKE_CONNECTIONS_DEFAULT_PRIVATE_KEY_FILE: ".snowflake/snowflake_rsa_key"\n      # Read connection secrets\n      SNOWFLAKE_CONNECTIONS_DEFAULT_ACCOUNT: ${{ secrets.SNOWFLAKE_ACCOUNT }}\n      SNOWFLAKE_CONNECTIONS_DEFAULT_USER: ${{ secrets.SNOWFLAKE_USER }}\n\n    steps:\n      # Checkout step is necessary if you want to use a config file from your repo\n      - name: Checkout repository\n        uses: actions/checkout@v4\n\n      # Write private key secret as file\n      - name: Configure Private Key\n        run: |\n          echo "${{ secrets.SNOWFLAKE_PRIVATE_KEY }}" > .snowflake/snowflake_rsa_key\n\n      # Install Snowflake CLI GitHub Action and point to config file\n      - name: Install snowflake-cli\n        uses: snowflakedb/snowflake-actions@v1.5\n        with:\n          cli-version: "latest"\n          default-config-file-path: ".snowflake/config.toml"\n\n      # Update Snowflake's copy of the repository\n      - name: Fetch repository changes\n        run: snow git fetch "${REPO_NAME}"\n\n      # Deploy pipeline with branch specific parameters\n      - name: Deploy data pipeline to ${{ github.ref_name }}\n        run: |\n          BRANCH_NAME=${{ github.ref_name }}\n          if [ "${BRANCH_NAME}" == "main" ]; then\n            RETENTION_TIME=1\n          else\n            RETENTION_TIME=0\n          fi\n          snow git execute \\\n            "@${REPO_NAME}/branches/${BRANCH_NAME}/steps/0[134]_*" \\\n            -D "environment='${BRANCH_NAME}'" \\\n            -D "retention_time=${RETENTION_TIME}"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth commands are executed using the \u003Ccode\u003Esnow\u003C/code\u003E command. This command is part of the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli-v2/index\"\u003ESnowflakeCLI\u003C/a\u003E tool, not to be confused with its predecessor \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql\"\u003ESnowSQL\u003C/a\u003E. SnowflakeCLI is an open-source command-line tool explicitly designed for developer-centric workloads. Also provided is a \u003Ca href=\"https://github.com/snowflakedb/snowflake-actions\"\u003Enative GitHub Action\u003C/a\u003E, simplifying the setup and connection management. It is a flexible and extensible tool that can accommodate modern development practices and technologies. This makes it a perfect choice for DevOps.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EOnce you're finished with the Quickstart and want to clean things up, you can drop all the objects you created by running the cleanup script. Open \u003Ccode\u003Esteps/08_cleanup.sql\u003C/code\u003E in the VS Code extension and run all command using the "Execute All Statements" button in the upper right corner of the editor window.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EWow, we have covered a lot of ground during this Quickstart! Not only can you now skip this year’s vacation planning, but you have also learned how to manage a data pipeline with DevOps practices. You followed the Infrastructure as Code principle and used Snowflake’s version control capabilities in combination with declarative Create-or-Alters to make database change management much less error-prone and less painful. With the help of SnowflakeCLI and Jinja templating you verified your changes in a test environment before automatically rolling them out to production with the help of a CI/CD pipeline.\u003C/p\u003E\n","\u003Cp\u003EBut we've really only just scratched the surface of what's possible with Snowflake DevOps. You now have the building blocks, and examples, you need to get started integrating DevOps best practices into your own data engineering pipelines. So, what will you DevOps-ify first?\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EYou have learned a ton in this guide. Here are the highlights:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHow to connect Snowflake to Git with the native Git integration\u003C/li\u003E\u003Cli\u003EHow to simplify database change management with declarative Create-or-Alters\u003C/li\u003E\u003Cli\u003EHow to verify your changes in a test environment with Jinja templating\u003C/li\u003E\u003Cli\u003EHow to use Snowflake’s Visual Studio Code extension\u003C/li\u003E\u003Cli\u003EHow to build CI/CD pipelines with SnowflakeCLI and GitHub Actions\u003C/li\u003E\u003Cli\u003EHow to interact with Snowflake's Python APIs\u003C/li\u003E\u003Cli\u003EHow to gain access to the powerful data on Snowflake's Marketplace\u003C/li\u003E\u003Cli\u003EHow to enhance your data with Snowflake Cortex LLM functions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-devops\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/devops-in-snowflake-how-git-and-database-change-management-enable-a-file-based-object-lifecycle-1f61a0d5257c\"\u003EDevOps in Snowflake: How Git and Database Change Management enable a file-based object lifecycle\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/devops-dcm-terraform-github/\"\u003EDevOps: Database Change Management with Terraform and GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EAdvanced: Data Engineering Pipelines with Snowpark Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ERelated Tools\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/vscode-ext\"\u003ESnowflake Visual Studio Code Extension\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli-v2/index\"\u003ESnowflakeCLI Tool\u003C/a\u003E and \u003Ca href=\"https://github.com/snowflakedb/snowflake-actions\"\u003EGitHub Action\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"Getting Started with Snowflake DevOps","title":"Getting Started with Snowflake DevOps","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\n\nA data pipeline is a series of automated processes that ingest, process, and move data from one place to another. It's a crucial component in data management and analytics, enabling organizations to make use of the vast amounts of data available to them.\n\nThe data engineer’s work doesn’t end when the pipeline is up and running for the first time. As businesses grow and change, so do their data needs. Data schemas as well as the pipeline as a whole need to evolve to capture new data sources or analyst requirements. In software engineering, DevOps techniques are applied to emphasize collaboration and communication to make changes rapidly and reliably. Similarly, DevOps best practices should be followed to ensure that changes to tables, data pipelines, and other database objects are made in a controlled, repeatable, and safe manner.\n\nAre you interested in bringing your database game to the next level by unleashing the power of Snowflake’s many DevOps capabilities, empowering you to build safe and robust data pipelines? Well then, this Quickstart is for you!\n\nThis quickstart will cover a lot of ground. By the end of this guide, you will have built a robust CI/CD pipeline that automates the deployment of changes to a data pipeline from Git, promotes the separation of dev and prod databases and facilitates monitoring. You'll also learn how to use Snowflake's marketplace to enrich your data without incurring storage costs, get to know the new developer CLI tool and the Visual Studio Code extension!\n\n![DevOps Overview](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/devops-overview.png?v=4185b8a0)\n\nThe data pipeline used as an example in this quickstart makes use of data available on the Snowflake marketplace to come up with the perfect spot for a family vacation. Let's get started!\n\n\u003E \n\u003E Note: The Git integration, Create-or-Alter and Jinja templating are still in preview.\n\n### Prerequisites\n\n- Familiarity with Snowflake\n- Fundamentals of data engineering. You can use [Intro to Data Engineering with Snowpark Python](/en/developers/guides/data-engineering-with-snowpark-python-intro/) to refresh your memory\n- Familiarity with Git repositories and GitHub\n\n### What you will learn\n\nYou will learn about the following Snowflake features in this Quickstart:\n\n- Native Git Integration\n- Declarative Database Change Management\n- Parametrization of SQL scripts with Jinja templating\n- GitHub Actions (CI/CD) integration\n- Snowflake’s Visual Studio Code extension\n- SnowflakeCLI tool\n- Snowflake's Python APIs\n- Data Sharing/Marketplace (instead of ETL)\n- LLM interference with Snowflake Cortex\n\n### What you will need\n\nYou will need the following things before starting:\n\n- Snowflake\n  - **A [Snowflake Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)**\n  - **A Snowflake user with ACCOUNTADMIN permissions**\n  - **Anaconda Terms & Conditions accepted**. See [Using Third-Party Packages](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started).\n- GitHub\n  - A GitHub account. If you don't already have one, you can [create one for free](https://github.com/).\n\n### What you will build\n\nDuring this Quickstart you will accomplish the following things:\n\n- Establish a connection between Snowflake and a GitHub repository\n- Setup access to Snowflake Marketplace data\n- Create a vectorized Python UDF making use of dynamic file access\n- Create a data engineering pipeline to process data from multiple sources\n- Orchestrate the pipeline with tasks\n- Make declarative changes to the pipeline with Create-or-Alter and Python APIs\n- Separate dev and prod environments with Jinja templating\n- Deploy the pipeline via an automated CI/CD pipeline\n- Enrich your data with Cortex LLM functions \n\n\u003C!-- ------------------------ --\u003E\n## Setup Environment\n\n\n### Fork the Quickstart Repository\n\nYou'll need to create a fork of the repository for this Quickstart in your GitHub account. Visit the [associated GitHub repository](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-devops) and click on the \"Fork\" button near the top right. Complete any required fields and click \"Create Fork\".\n\n### Create GitHub Codespace\n\nFor this Quickstart we will be using [GitHub Codespaces](https://docs.github.com/en/codespaces/overview) as our development environment. Codespaces offer a hosted development environment with a hosted, web-based VS Code environment. The free offering should be more than enough for this quickstart.\n\nTo create a GitHub Codespace, click on the green “\u003C\u003E Code” button from the forked repository’s homepage. In the Code popup, click on the green “Create codespace on main”.\n\n![Start GitHub Codespace](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/start-codespace.png?v=4185b8a0)\n\nThis will open a new tab and begin setting up your codespace. This will take a few minutes as it sets up the entire environment for this Quickstart.\n\nOnce the codespace has been created and started, you should see a hosted web-based version of VS Code with your forked repository set up! Snowflake’s VS Code extension and the Snowflake CLI have already been installed for you.\n\n### Snowflake Extension for VS Code\n\nYou can run SQL queries against Snowflake in many different ways (through the Snowsight UI, SnowSQL, etc.) but for this Quickstart we'll be using the Snowflake extension for VS Code. For a brief overview of Snowflake's native extension for VS Code, please check out our [VS Code Marketplace Snowflake extension page](https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc).\n\n### Snowflake CLI\n\n[Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index) is an open-source command-line tool explicitly designed for developer-centric workloads in addition to SQL operations. With Snowflake CLI, developers can create, manage, update, and view apps running on Snowflake across workloads such as Streamlit in Snowflake, the Snowflake Native App Framework, Snowpark Container Services, and Snowpark.\n\nHere we will make use of the CLI to quickly setup a CI/CD pipeline.\n\n### Connecting to Snowflake\n\nBoth the VS Code extension and the CLI manage their Snowflake connections in a shared file. Open `.snowflake/config.toml` and add your [account identifier](https://docs.snowflake.com/en/user-guide/admin-account-identifier), username and password in the corresponding placeholders. Note that the user must have ACCOUNTADMIN privileges. Make sure to not commit this file to Git.\n\nNow use these credentials to login with the VS Code extension, by clicking the Snowflake logo in the activity bar to the left of the editor. The dialog should have been pre-filled from the config file. Press \"Sign in\" and you are good to go.\n\n\u003C!-- ------------------------ --\u003E\n## Setup Snowflake\n\n\nTo get started we need to create some basic Snowflake objects like databases, stages and a warehouse that we will use later on. Open `steps/01_setup_snowflake.sql` to review what statements will be executed.\n\n### Create and Connect a Git Repository\n\nThis quickstart makes extensive use of the [GIT REPOSITORY](https://docs.snowflake.com/en/developer-guide/git/git-overview) object. This object acts similarly to an external stage except that it is backed by a GitHub repository. It enables the access to version controlled files at a certain commit, branch or tag. This way your database scripts and object definitions can be evolved with software engineering best practices such as branching, pull requests and code reviews.\n\nBefore continuing, **replace the two placeholders** in `steps/01_setup_snowflake.sql` with your GitHub username and the url of the forked repository.\n\n```sql\nCREATE OR REPLACE API INTEGRATION git_api_integration\n  API_PROVIDER = git_https_api\n  API_ALLOWED_PREFIXES = ('https://github.com/\u003Cinsert GitHub username\u003E')\n  ENABLED = TRUE;\n\nCREATE OR REPLACE GIT REPOSITORY quickstart_common.public.quickstart_repo\n  API_INTEGRATION = git_api_integration\n  ORIGIN = '\u003Cinsert URL of forked GitHub repo\u003E';\n```\n\n### Run the Script\n\nAfter replacing the git-related placeholders, it is time to execute the script to create all the objects we'll need for this quickstart.\n\nStart by opening the `steps/01_setup_snowflake.sql` script. Now you can run all the queries in this script, by using the \"Execute All Statements\" button in the upper right corner of the editor window. If you want to run them in chunks instead, you can highlight the ones you want to run and press “CMD/CTRL+Enter”.\n\n\u003C!-- ------------------------ --\u003E\n## Load Data from the Marketplace\n\n\nIn this step, we will be \"loading\" the raw data we need to find the perfect vacation spot to Snowflake. But \"loading\" is really the wrong word here. Because as we're using Snowflake's unique data sharing capability, we don't actually need to copy any data to our Snowflake account. Instead, we can directly access the data shared by various data providers in the Snowflake Data Marketplace.\n\nAs we are looking to find the perfect vacation spot, we are interested in:\n\n- The weather forecast\n- Flight connections\n- Points of interest\n\n### Snowflake Data Marketplace\n\nAll this data is freely available on Snowflake’s marketplace, and we won’t incur any storage costs as long as we don’t materialize it in our account. The other great thing about the marketplace is that we don't have to do anything to keep this data updated. The provider will do that for us, and data sharing means we are always seeing whatever they have published. How amazing is that? Just think of all the additional insights you could extract from your data if only you had the necessary, always up-to-date supplementary data available.\n\nFor this quickstart we will add a number of data sets to our account. The process is always the same:\n\n1. Login to Snowsight\n2. Click on the “Data Products” link in the left navigation bar\n3. Enter the name of the data set in the search box and click return\n4. Click on the corresponding listing tile\n5. Click the blue \"Get\" button on the top right\n6. On the popup, click the blue \"Get\" button\n\nNow, perform these steps for the following data listings:\n\n1. “OAG: Flight Emissions Data (Sample)” by OAG\n2. “OAG: Flight Status Data (Sample)” by OAG\n3. “Global Weather & Climate Data for BI” by Weather Source\n4. “Snowflake Public Data (Free)” by Snowflake Public Data Products\n\n### Inspect Imported Listings\n\nThat’s all! The imported listings are now available in our account and can be accessed like any other table. Use the following sample queries to inspect the imported data and verify that all data sets are accessible. You can also find the sample queries in `steps/02_access_marketplace_data.sql` of the Git repository.\n\n```sql\nSELECT * FROM oag_flight_emissions_data_sample.public.estimated_emissions_schedules_sample LIMIT 100;\nSELECT * FROM oag_flight_status_data_sample.public.flight_status_latest_sample LIMIT 100;\nSELECT * FROM global_weather__climate_data_for_bi.standard_tile.forecast_day LIMIT 100;\nSELECT * FROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.datacommons_timeseries LIMIT 100;\nSELECT * FROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.point_of_interest_index LIMIT 100;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Harmonize Marketplace Data\n\n\nThe step will establish a data pipeline that will help us answer the question of what the perfect vacation spot is by joining together the different data sources from the market place, aggregating data points and picking only the columns and rows we need.\n\nWhile most parts of our data pipeline are authored in SQL, `steps/03_harmonize_data.py` is written in Python. Snowflake supports seamlessly switching between SQL and Python when authoring data pipelines. Here, [Snowflake's first class PythonAPI's](https://docs.snowflake.com/en/developer-guide/snowflake-python-api/snowflake-python-overview) are used to manage creations, alterations, or drops of Snowflake objects just like you would otherwise do with SQL commands.\n\nWe will skip the details of how the pipeline is constructed to focus on how we can employ DevOps practices to later safely evolve and manage the pipeline. If you want to learn more about the Python APIs check the tutorials [here](https://docs.snowflake.com/en/developer-guide/snowflake-python-api/overview-tutorials).\n\n### Run the Script\n\nTo establish the pipeline, we need to run the commands in the Python file `steps/03_harmonize_data.py`. Open the file in VS Code. Next, click on “Run Python File” to run the script.\n\n\u003C!-- ------------------------ --\u003E\n## Orchestrate Jobs\n\n\nDuring this step we will be orchestrating our new pipeline with Snowflake's native orchestration feature named “tasks”. In this step, we will create two tasks and chain them together. We will then deploy or run the tasks to operationalize the data pipeline.\n\nThe job of the first task “vacation_spots_update” is joining the views created in the previous step and merge the result into the output table “vacation_spots”. The second task “email_notification” runs after the update task has finished. It selects a number of suitable places for a summer vacation and feeds the results into an LLM to get a final verdict and turn the recommendation into an entire vacation plan. The answer the LLM comes up with is sent via email, informing you of the pipelines completion and providing you with the ultimate vacation plan. No more stressful vacation planning, just book your flights and hotel, share the email with friends and family and off you go!\n\nIn this step, we will run through the commands in the Python file `steps/04_orchestrate_jobs.sql`. Open the file in VS Code and **add your email address to the three placeholders**. Then execute the file by clicking on on “Execute All Statements” to run all the commands sequentially.\n\n\u003E \n\u003E Note: The Snowflake Cortex LLM functions are currently not available in all regions. Check the [docs](https://docs.snowflake.com/user-guide/snowflake-cortex/llm-functions#availability) to verify your region is supported. If not, it is up to you to pick your favorite vacation spot from the results.\n\n\n\u003E \n\u003E Tip: The query used in the “email_notification” task selects suitable places for a summer vacation. If you are running the pipeline in the colder months, it might not return any results. In that case, adapt the filters and off you go on your winter vacation! You might want to add snowfall days to your weather metrics.\n\n### Declarative Create-or-Alter Command\n\nWhen creating the target table and the tasks orchestrating the pipeline, we make use of the [CREATE OR ALTER](https://docs.snowflake.com/en/sql-reference/sql/create-table#create-or-alter-table) command. CREATE OR ALTER behaves similarly to CREATE OR REPLACE except that an existing object is not replaced, but rather altered to the target state.\n\n```sql\ncreate or alter table vacation_spots (\n  city varchar,\n  airport varchar,\n  co2_emissions_kg_per_person float,\n  punctual_pct float,\n  avg_temperature_air_f float,\n  avg_relative_humidity_pct float,\n  avg_cloud_cover_pct float,\n  precipitation_probability_pct float\n) data_retention_time_in_days = 1;\n```\n\n\u003E \n\u003E Note: The Create-or-Alter command is currently not publicly available for all object types. Check the docs for currently supported objects types and contact your account manager if you want to join the extended preview.\n\n### Verify Pipeline Completion\n\nBy this time the pipeline should has completed processing. You should have received an email with all the details for your dream vacation. If you haven’t received an email, use the following query to see what is going on:\n\n```sql\nSELECT *\nFROM TABLE(QUICKSTART_PROD.INFORMATION_SCHEMA.TASK_HISTORY(\n    SCHEDULED_TIME_RANGE_START=\u003EDATEADD('DAY',-1,CURRENT_TIMESTAMP()),\n    RESULT_LIMIT =\u003E 100))\nORDER BY SCHEDULED_TIME DESC;\n```\n\nCheck out [this](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/) guide to learn more about the powerful Snowflake features enabling you to monitor the execution of your tasks.\n\n\u003C!-- ------------------------ --\u003E\n## Database Change Management\n\n\nJust as business needs change, so do data needs. Over time our data pipeline will need to adapt to capture new or differently formatted data. It is our responsibility as data engineers to make sure that these changes can be made in a safe manner. In DevOps, Infrastructure as Code (IaC) is a common pattern applied when making infrastructure changes. Managing our database resources in a common, versioned source allows us to effectively collaborate and have a source off truth that is consistent across all deployments. IaC if often done in a declarative manner using tools such as [terraform](/en/developers/guides/devops-dcm-terraform-github/).\n\nDeclarative definitions are concise, idempotent and easy to understand as we don’t need to understand the entire stack of historic changes leading up to the current state as in tools like [schemachange](/en/developers/guides/devops-dcm-schemachange-github/). Rolling back multiple ALTER statements to get back to a previous state can be a pain.\n\n### Declarative Pipeline Evolution\n\nThis is where the [CREATE OR ALTER](https://docs.snowflake.com/en/sql-reference/sql/create-table#create-or-alter-table) command shines. It allows us to have one single source of truth for the current state of the object. Potential rollbacks are super easy to do thanks to the straightforward integration with version control tools such as Git. Just apply an older version of the CREATE OR ALTER. This makes CREATE OR ALTER a great choice to manage data pipelines.\n\n\u003E \n\u003E Note: The Create-or-Alter command is currently not publicly available for all object types. Check the docs for currently supported objects types and contact your account manager if you want to join the extended preview.\n\n### Adding a new Data Source to the Pipeline\n\nUntil now our vacation spot pipeline did only consider the weather and the reachability of potential locations. But what about activities during the stay? After all, what is a vacation without a visit to at least one Korean restaurant? And it’s a family vacation, so how about a visit to a zoo or an aquarium?\n\nLet’s add data on these points of interest to our pipeline! **Follow along and perform the described steps** yourself in VS Code.\n\n1. In DevOps it is always a good practice to make our changes on a development branch to properly test them before merging to main. Let’s create a new branch for our changes. Open the terminal of your VS Code codespace and run:\n\n```bash\ngit checkout -b dev\n```\n\n2. Open `steps/03_harmonize_data.py` and add the following view definition at the end of the file. This view makes use of the “US Points of Interest & Addresses” data set we previously imported from the marketplace to find the number of Korean restaurants, zoos and aquariums for each potential vacation spot.\n\n```py\nView(\n    name=\"attractions\",\n    columns=[\n        ViewColumn(name=\"geo_id\"),\n        ViewColumn(name=\"geo_name\"),\n        ViewColumn(name=\"aquarium_cnt\"),\n        ViewColumn(name=\"zoo_cnt\"),\n        ViewColumn(name=\"korean_restaurant_cnt\"),\n    ],\n    query=\"\"\"\n    select\n        city.geo_id,\n        city.geo_name,\n        count(case when category_main = 'Aquarium' THEN 1 END) aquarium_cnt,\n        count(case when category_main = 'Zoo' THEN 1 END) zoo_cnt,\n        count(case when category_main = 'Korean Restaurant' THEN 1 END) korean_restaurant_cnt,\n    from SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.point_of_interest_index poi\n    join SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.point_of_interest_addresses_relationships poi_add \n        on poi_add.poi_id = poi.poi_id\n    join SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.us_addresses address \n        on address.address_id = poi_add.address_id\n    join major_us_cities city on city.geo_id = address.id_city\n    where true\n        and category_main in ('Aquarium', 'Zoo', 'Korean Restaurant')\n        and id_country = 'country/USA'\n    group by city.geo_id, city.geo_name\n    \"\"\",\n),\n```\n\n3. Next, let’s update the schema of our target table to account for the new columns. Head over to `steps/04_orchestrate_jobs.sql` and modify the CREATE OR ALTER on top by appending the following columns to its column list:\n\n```sql\n, aquarium_cnt int\n, zoo_cnt int\n, korean_restaurant_cnt int\n```\n\n4. Then update the MERGE statement in task “vacation_spots_update” by adding a JOIN with the new view.\n\n```sql\njoin silver.attractions att on att.geo_name = city.geo_name\n```\n\n5. Add the new columns to the WHEN MATCHED clause of the same MERGE statement.\n\n```sql\n, vacation_spots.aquarium_cnt = harmonized_vacation_spots.aquarium_cnt\n, vacation_spots.zoo_cnt = harmonized_vacation_spots.zoo_cnt\n, vacation_spots.korean_restaurant_cnt = harmonized_vacation_spots.korean_restaurant_cnt\n```\n\n6. Don’t forget to also add the new columns to the MERGE statement’s WHEN NOT MATCHED clause.\n\n```sql\n, harmonized_vacation_spots.aquarium_cnt\n, harmonized_vacation_spots.zoo_cnt\n, harmonized_vacation_spots.korean_restaurant_cnt\n```\n\n7. Finally, update the WHERE clause of the filter query in task “email_notification” to filter for vacation spots that have a Korean restaurant as well as either a zoo or an aquarium for the kids to enjoy.\n\n```sql\nand korean_restaurant_cnt \u003E 0\nand (zoo_cnt \u003E 0 or aquarium_cnt \u003E 0)\n```\n\n### Deploy Pipeline Changes\n\nAfter accounting for the new data in our pipeline it is time to deploy the new version. First commit your changes to the Git repository. Run the following commands in the VS Code terminal:\n\n```bash\ngit add steps/*\ngit commit -m \"added attractions to pipeline\"\ngit push --set-upstream origin dev\n```\n\nNext, deploy the new version of the data pipeline manually. Open the terminal in VS Code and run `snow git fetch quickstart_common.public.quickstart_repo` to fetch the changes from GitHub to Snowflake. Finally, run `snow git execute @quickstart_common.public.quickstart_repo/branches/dev/steps/0[134]_*`. The command reexecutes `steps/01_setup_snowflake.sql` to apply changes to the foundational objects, `steps/03_harmonize_data.py` to update the views used to transform the marketplace data and `steps/04_orchestrate_jobs.sql` to apply our changes to the target table and update and execute the two tasks.\n\n```bash\n$ snow git fetch quickstart_common.public.quickstart_repo\n+------------------------+\n| scope  | name | result |\n|--------+------+--------|\n| Branch | dev  | FORCED |\n+------------------------+\n\n$ snow git execute '@quickstart_common.public.quickstart_repo/branches/dev/steps/0[134]_*'\nSUCCESS - @quickstart_common.public.quickstart_repo/branches/dev/steps/01_setup_snowflake.sql\nSUCCESS - @quickstart_common.public.quickstart_repo/branches/dev/steps/03_harmonize_data.py\nSUCCESS - @quickstart_common.public.quickstart_repo/branches/dev/steps/04_orchestrate_jobs.sql\n+--------------------------------------------------------------------------------------------------------+\n| File                                                                                 | Status  | Error |\n|--------------------------------------------------------------------------------------+---------+-------|\n| @quickstart_common.public.quickstart_repo/branches/dev/steps/01_setup_snowflake.sql  | SUCCESS | None  |\n| @quickstart_common.public.quickstart_repo/branches/dev/steps/03_harmonize_data.py    | SUCCESS | None  |\n| @quickstart_common.public.quickstart_repo/branches/dev/steps/04_orchestrate_jobs.sql | SUCCESS | None  |\n+--------------------------------------------------------------------------------------------------------+\n```\n\nIf done correctly, you will shortly receive another email with your updated vacation plan.\n\n\u003C!-- ------------------------ --\u003E\n## Separate Dev and Prod Environments\n\n\nIn the previous section we did not pause to verify our changes before deploying them to Snowflake. In this section, we will discuss how to properly test the changes before deploying them to our production database.\n\nDevOps emphasizes the use of separate environments for development, testing, and production to minimize the risk of bugs or regressions affecting live systems. By maintaining separate dev and prod databases, teams can isolate development activities from the production environment, reducing the likelihood of unintended consequences and data corruption. A common pattern to separate production from development environments is using separate databases.\n\nThere is a problem preventing us from doing so: Our pipeline definitions contain hard references to the production database “quickstart_prod”. We need to parametrize these references to enable a CI/CD pipeline to choose the deployment target.\n\n### Jinja Templating in Snowflake\n\n[Jinja](https://jinja.palletsprojects.com/en/3.1.x/) is a popular templating language with features ranging from simple script parametrization to extensive scripting in a language similar to Python. Snowflake supports the execution of templated SQL scripts with the [EXECUTE IMMEDIATE FROM](https://docs.snowflake.com/en/sql-reference/sql/execute-immediate-from) command.\n\nFor the use case of changing the deployment target, variable replacement is all we need from Jinja. Jinja variables in SQL scripts are marked by `{{...}}`. We can provide a value for a variable in the USING clause of EXECUTE IMMEDIATE FROM. Template variables can be used everywhere is a SQL script. We can use variables to inject object names, property values or even entire statements.\n\n### Parameterize the Deployment Target\n\nA number of small changes are necessary to parametrize which database the data pipeline gets deployed to. To get more control over our storage costs, we will also parametrize the retention time for the data in our target table.\n\nLet’s perform the necessary changes! Follow along and perform the described steps yourself in VS Code.\n\n1. Open `steps/01_setup_snowflake.sql` and parametrize part the database name in the CREATE OR REPLACE DATABASE QUICKSTART_PROD command.\n\n```sql\nCREATE OR ALTER DATABASE QUICKSTART_{{environment}}; \n```\n\n2. Open `steps/03_harmonize_data.py` and read the environment variable.\n\n```py\nsilver_schema = root.databases[f\"quickstart_{os.environ['environment']}\"].schemas[\"silver\"]\n```\n\n3. Open `steps/04_orchestrate_jobs.sql` and parametrize the USE SCHEMA command.\n\n```sql\nuse schema quickstart_{{environment}}.gold;\n```\n\n4. Finally, in the same file, parametrize the value of the “data_retention_time_in_days” parameter in the CREATE OR ALTER TABLE command.\n\n```sql\ndata_retention_time_in_days = {{retention_time}};\n```\n\n\u003C!-- ------------------------ --\u003E\n## Deployment Automation\n\n\nIn this section we will use the Jinja templating to automatically deploy the pipeline using GitHub Actions. We build a CI/CD pipeline to deploy the data pipeline to the “prod” database when changes are pushed to the repository’s “main” branch. Thanks to templating, commits to the “dev” branch are deployed to the “dev” database instead.\n\n### Configuring Your Forked GitHub Project\n\nBy default, GitHub disables any workflows (or CI/CD pipelines) when forking a repository. The forked repository contains a CI/CD workflow to deploy your data pipeline to dev and prod environments. Enable the workflows by opening your forked repository in GitHub, clicking on the “Actions” tab near the top middle of the page, and then clicking on the “I understand my workflows, go ahead and enable them” green button.\n\n![Enable GitHub Actions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/enable-workflows.png?v=4185b8a0)\n\nIn order for your GitHub Actions workflow to connect to your Snowflake account you will need to store your Snowflake credentials in GitHub. Action Secrets in GitHub are used to securely store values/variables for use in CI/CD pipelines.\n\nIn GitHub, click on the “Settings” tab near the top of the page. From the Settings page, click on the “Secrets and variables” then “Actions” tab in the left hand navigation. The “Secrets” tab should be selected. For each secret listed below click on the green “New repository secret” and enter the name given below along with the appropriate value (adjusting as appropriate).\n\n| Secret name | Secret value |\n|-------------|--------------|\n| SNOWFLAKE_ACCOUNT | myaccount |\n| SNOWFLAKE_USER | myusername |\n| SNOWFLAKE_PRIVATE_KEY | contents of private key file (follow [this guide](https://docs.snowflake.com/en/user-guide/key-pair-auth) to setup key-pair auth) |\n\n\u003E \n\u003E Note: For more details on how to structure the account name in SNOWFLAKE_ACCOUNT secret, see the account name discussion in the [Snowflake Python Connector install guide](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-api#usage-notes-for-the-account-parameter-for-the-connect-method).\n\nWhen you're finished adding all the secrets, the page should look like this:\n\n![GitHub Actions Secrets](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/action-secrets.png?v=4185b8a0)\n\n### Deploy to Development Environment with CI/CD Pipeline\n\nThe CI/CD pipeline is ready to use. Let’s push our changes to GitHub and let the CI/CD pipeline do its thing. Open the terminal of the VS Code codespace and execute the following commands.\n\n```bash\ngit add steps/*\ngit commit -m \"parametrize pipeline\"\ngit push\n```\n\nThe CI/CD pipeline will pick us the changes and run the workflow defined in `.github/workflows/deploy_pipeline.yml`. To view the results go back to the homepage of your GitHub repository and do the following:\n\n- From the repository, click on the Actions tab near the top middle of the page\n- Click on the name of the most recent run (which should have the name “parametrize pipeline”)\n- From the run overview page click on the “deploy” job and browse through the output from the various steps. In particular you might want to review the output of the “Deploy data pipeline to dev” step.\n\n![CI/CD Pipeline Output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowflake-devops/pipeline-output.png?v=4185b8a0)\n\nThe workflow should indicate a successful execution. If this is the case, you will receive the familiar email containing the vacation plan after about a minute. Great! We just successfully tested our parametrization changes in a dedicated test environment.\n\n### Deploy to Production Environment with CI/CD Pipeline\n\nAfter validating the changes, we can confidently merge them to our production database. Normally, at this point we would open a pull request to get our changes reviewed. Let’s speed this up and merge directly to main by executing the following commands in the VS Code terminal:\n\n```bash\ngit checkout main\ngit merge dev\ngit push\n```\n\nTime to check GitHub’s “Actions” tab again. Another run of the “Deploy data pipeline” has appeared. Since we have dutifully tested our changes beforehand, we can confidently expect the vacation plan email to arrive shortly indicating a successful run.\n\nTo save storage costs, we also used templating to parametrize the data retention time of the target table “vacation_spots”. The CI/CD pipeline did set the retention time to 0 days for the dev environment, while the prod environment stayed at the default of 1 day. Confirm this with the following commands. Note, that as the production pipeline might still be running, the second command might throw an error initially.\n\n```sql\nshow parameters like 'data_retention_time_in_days' in table quickstart_dev.gold.vacation_spots;\n\u003E returns 0\nshow parameters like 'data_retention_time_in_days' in table quickstart_prod.gold.vacation_spots;\n\u003E returns 1\n```\n\n### CI/CD Pipeline using SnowflakeCLI\n\nWhile the pipeline is running, let’s understand how it works. Open `.github/workflows/deploy_pipeline.yml` in VS Code. The workflow is triggered on pushes to “main” or “dev”, sets up the SnowflakeCLI and stores the GitHub Actions secrets in environment variables. This environment is then used to execute two commands on Snowflake.\n\n```yaml\nname: Deploy data pipeline\n\n# Action will run on pushes to selected branches\non:\n  push:\n    branches:\n      - dev\n      - main\n\n  # Allows you to run this workflow manually from the Actions tab\n  workflow_dispatch:\n\njobs:\n  deploy:\n    runs-on: ubuntu-22.04\n\n    env:\n      REPO_NAME: \"quickstart_common.public.quickstart_repo\"\n      SNOWFLAKE_CONNECTIONS_DEFAULT_AUTHENTICATOR: \"SNOWFLAKE_JWT\"\n      SNOWFLAKE_CONNECTIONS_DEFAULT_PRIVATE_KEY_FILE: \".snowflake/snowflake_rsa_key\"\n      # Read connection secrets\n      SNOWFLAKE_CONNECTIONS_DEFAULT_ACCOUNT: ${{ secrets.SNOWFLAKE_ACCOUNT }}\n      SNOWFLAKE_CONNECTIONS_DEFAULT_USER: ${{ secrets.SNOWFLAKE_USER }}\n\n    steps:\n      # Checkout step is necessary if you want to use a config file from your repo\n      - name: Checkout repository\n        uses: actions/checkout@v4\n\n      # Write private key secret as file\n      - name: Configure Private Key\n        run: |\n          echo \"${{ secrets.SNOWFLAKE_PRIVATE_KEY }}\" \u003E .snowflake/snowflake_rsa_key\n\n      # Install Snowflake CLI GitHub Action and point to config file\n      - name: Install snowflake-cli\n        uses: snowflakedb/snowflake-actions@v1.5\n        with:\n          cli-version: \"latest\"\n          default-config-file-path: \".snowflake/config.toml\"\n\n      # Update Snowflake's copy of the repository\n      - name: Fetch repository changes\n        run: snow git fetch \"${REPO_NAME}\"\n\n      # Deploy pipeline with branch specific parameters\n      - name: Deploy data pipeline to ${{ github.ref_name }}\n        run: |\n          BRANCH_NAME=${{ github.ref_name }}\n          if [ \"${BRANCH_NAME}\" == \"main\" ]; then\n            RETENTION_TIME=1\n          else\n            RETENTION_TIME=0\n          fi\n          snow git execute \\\n            \"@${REPO_NAME}/branches/${BRANCH_NAME}/steps/0[134]_*\" \\\n            -D \"environment='${BRANCH_NAME}'\" \\\n            -D \"retention_time=${RETENTION_TIME}\"\n```\n\nBoth commands are executed using the `snow` command. This command is part of the [SnowflakeCLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli-v2/index) tool, not to be confused with its predecessor [SnowSQL](https://docs.snowflake.com/en/user-guide/snowsql). SnowflakeCLI is an open-source command-line tool explicitly designed for developer-centric workloads. Also provided is a [native GitHub Action](https://github.com/snowflakedb/snowflake-actions), simplifying the setup and connection management. It is a flexible and extensible tool that can accommodate modern development practices and technologies. This makes it a perfect choice for DevOps.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\n\nOnce you're finished with the Quickstart and want to clean things up, you can drop all the objects you created by running the cleanup script. Open `steps/08_cleanup.sql` in the VS Code extension and run all command using the \"Execute All Statements\" button in the upper right corner of the editor window.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\n\nWow, we have covered a lot of ground during this Quickstart! Not only can you now skip this year’s vacation planning, but you have also learned how to manage a data pipeline with DevOps practices. You followed the Infrastructure as Code principle and used Snowflake’s version control capabilities in combination with declarative Create-or-Alters to make database change management much less error-prone and less painful. With the help of SnowflakeCLI and Jinja templating you verified your changes in a test environment before automatically rolling them out to production with the help of a CI/CD pipeline.\n\nBut we've really only just scratched the surface of what's possible with Snowflake DevOps. You now have the building blocks, and examples, you need to get started integrating DevOps best practices into your own data engineering pipelines. So, what will you DevOps-ify first?\n\n### What You Learned\n\nYou have learned a ton in this guide. Here are the highlights:\n\n- How to connect Snowflake to Git with the native Git integration\n- How to simplify database change management with declarative Create-or-Alters\n- How to verify your changes in a test environment with Jinja templating\n- How to use Snowflake’s Visual Studio Code extension\n- How to build CI/CD pipelines with SnowflakeCLI and GitHub Actions\n- How to interact with Snowflake's Python APIs\n- How to gain access to the powerful data on Snowflake's Marketplace\n- How to enhance your data with Snowflake Cortex LLM functions\n\n### Related Resources\n\n- [Source Code on GitHub](https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-devops)\n- [DevOps in Snowflake: How Git and Database Change Management enable a file-based object lifecycle](https://medium.com/snowflake/devops-in-snowflake-how-git-and-database-change-management-enable-a-file-based-object-lifecycle-1f61a0d5257c)\n- [DevOps: Database Change Management with Terraform and GitHub](/en/developers/guides/devops-dcm-terraform-github/)\n- [Advanced: Data Engineering Pipelines with Snowpark Python](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\n- Related Tools\n  - [Snowflake Visual Studio Code Extension](https://docs.snowflake.com/en/user-guide/vscode-ext)\n  - [SnowflakeCLI Tool](https://docs.snowflake.com/en/developer-guide/snowflake-cli-v2/index) and [GitHub Action](https://github.com/snowflakedb/snowflake-actions)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-ba43ec9fae","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-64e62fd496",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c62f6d884b","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-15",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b328c01938","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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