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Get the guideThis tutorial will guide you through setting up continuous integration (CI) jobs in dbt. CI jobs run when someone opens a new pull request (PR) in your dbt Git repository. By running and testing only modified models, dbt Cloud ensures these jobs are as efficient and resource conscientious as possible on your data platform.
A CI job in dbt is a task that is triggered to run when a new pull request is opened in your dbt Git repository. It tests only the modified models, ensuring efficiency and resource conscientiousness. CI jobs are recommended to be created in a dedicated dbt Cloud deployment environment that's connected to a staging database for better isolation between your temporary CI schema builds and your production data builds.
Before setting up a CI job, ensure you have a dbt Cloud account. For the Concurrent CI checks and Smart cancellation of stale builds features, your dbt Cloud account must be on the Team or Enterprise plan. Also, set up a connection with your Git provider. This integration lets dbt Cloud run jobs on your behalf for job triggering.
To create a CI job, navigate to your deployment environment page and click Create job > Continuous integration job. Many options on the CI job page are set to default values that dbt Labs recommends. However, you can change them according to your needs. These options include Job settings and Execution settings.
Advanced settings are optional and include Environment variables, Target name, Run timeout, dbt version, Threads, and Run source freshness. These settings allow you to customize the behavior of your project when the CI job runs.
One common challenge is the automatic deletion of the temporary schema. This is because automatic deletion relies on incoming webhooks from Git providers, which is only available through the native integrations. To overcome this, you can use the Administrative API to trigger a CI job to run.
It's recommended to create your CI job in a dedicated dbt Cloud deployment environment that's connected to a staging database. This provides better isolation between your temporary CI schema builds and your production data builds. Also, it's advisable to use the default settings provided by dbt Labs for creating a CI job.
You can explore more about CI jobs in dbt by learning about the Create Job API endpoint, the Trigger Job Run API endpoint, and how to set up a connection with your Git provider.
In this tutorial, we've learned about CI jobs in dbt, how to set them up, and the best practices to follow. We've also discussed the common challenges and their solutions, and provided resources for further learning.
Secoda's integration with dbt provides a robust solution for data analysis and delivery of results. This integration allows users to monitor, debug, and deploy models, and automatically update analytics with new data and insights. It also helps users visualize data flows, detect inconsistencies, and simplify troubleshooting.
With Secoda's dbt integration, data teams can leverage the power of dbt in a more user-friendly and secure environment, enhancing their data analysis capabilities.
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