Configure dbt to set up custom schemas to allow pull requests to run data models and data tests in their individual containers.Create a dbt profile for the dbt CI job to validate your data models and tests.Running parallel dbt tests against production data and auto-canceling redundant workflows are made feasible by using CircleCI, dbt, and Snowflake. How to use CircleCI to run dbt tests in parallel and to enable auto-canceling For a large size data deployment, it can slow down all the other deployments for a full day.Īdditionally, dbt Cloud testing can’t auto-cancel redundant workflows when there are multiple commits in the pull requests, which can influence the testing speed for analytics development. It would work for a small data team that only has one or two active dbt contributors, but it shows its limits when our analytics department starts scaling and analysts start contributing and deploying in dbt more frequently. First, the dbt Cloud CI/CD process currently allows only one job at a time, which can slow deployment speed if there are multiple pull requests merged into production. However, there are a few problems with the current dbt Cloud CI/CD process. The testing job status will be directly available in the pull request to help make your review process more efficient. ![]() You can have it connected to your GitLab or GitHub repository, and configure testing jobs to be triggered on each new pull request. You can use dbt Cloud to set up a continuous integration and continuous delivery (CI/CD) pipeline for your data testing by using the dbt slim CI function. Additionally, it allows you to test your assumptions about the data to ensure data integrity before the data is published in production. It helps track your data dependencies and centralize your data transformations and documentation, ensuring a single source of truth for important business metrics. You can write your SQL in a modular way and configure data tests using parameterized queries or the native testing functions that dbt provides. Why is dbt useful in data engineering and analysis?ĭbt is a powerful data tool that allows you to iterate through your table changes without manually modifying UPSERT statements. In the CLI version, you have full control of your data project configuration and the ability to publish documentation as needed, while dbt Cloud provides a user interface that sets up a few configurations for you and generates dbt documentation automatically. ![]() You can interact with dbt through either dbt CLI (command line interface) or dbt Cloud. What is dbt?ĭbt is a data transformation tool that allows data folks to combine modular SQL with software engineering best practices to make data transformations that are reliable, iterative, and fast. If you are new to the platform, you can sign up for a free account and follow our quickstart guide to get set up. ![]() This tutorial assumes you are an active CircleCI user. In this post, we will walk you through how to use CircleCI and dbt to automatically test your data changes against a replica of production to ensure data integrity and improve your development velocity. To solve this, we set up CircleCI to automatically test and deploy our data changes so that we can deliver quality data model releases as fast as possible to our data consumers. Until recently we had been experiencing deployment bottlenecks caused by long test runs in dbt Cloud. The testing process can be time-consuming and prone to unexpected errors.įor example, at CircleCI, our data team uses dbt at scale. The data world has adopted software development practices in recent years to test data changes before deployment. One difficult challenge in the software development cycle is increasing the speed of development while ensuring the quality of the code remains the same.
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