Lessons from Cal-ITP’s data-infra project on scaling dbt using data best practices
Research by Even Wei, article by Dave Flynn.
dbt adoption is growing, projects are getting bigger, and more fingers are now in your data pie than ever. Your job of maintaining data quality and prod stability in such circumstances isn’t easy, which is bringing the topic of dbt/data project best practices to the forefront.
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dbt best practices in action
The best way to QA updates to your dbt project is to standardize the process of reviewing and integrating code, and therefore data, changes into your project. The pull/merge request is at the center of the review process.
What does a pull request in a dbt project look like when a data team uses best practices to manage that process? What things do they do? and what CI automation do they run to assist in the process?