In the previous post, we started discussing a continuous integration/continuous delivery (CI/CD) process of a dbt project by introducing two GitHub Actions workflows - slim-ci and deploy. The former is triggered when a pull request is created to the main branch, and it builds only modified models and its first-order children in a ci dataset, followed by performing tests on them. The second workflow gets triggered once a pull request is merged. Beginning with running unit tests, it packages the dbt project as a Docker container and publishes to Artifact Registry. In this post, we focus on how to deploy a dbt project in multiple environments while walking through the entire CI/CD process step-by-step.

Continuous integration (CI) is the process of ensuring new code integrates with the larger code base, and it puts a great emphasis on testing automation to check that the application is not broken whenever new commits are integrated into the main branch. Continuous delivery (CD) is an extension of continuous integration since it automatically deploys all code changes to a testing and/or production environment after the build stage. CI/CD helps development teams avoid bugs and code failures while maintaining a continuous cycle of software development and updates. In this post, we discuss how to set up a CI/CD pipeline for a data build tool (dbt) project using GitHub Actions where BigQuery is used as the target data warehouse.