Datafold, a data quality platform that automates the most tedious parts of data engineering workflows, is partnering with dbt Labs, the pioneer in analytics engineering, to provide a new integration to deliver trusted data faster.
Datafold has automated test coverage for analytics engineers which can now be added into a company’s CI/CD workflow in one click with dbt Cloud or with a Python SDK for dbt Core.
Datafold automates writing thousands of regression tests, so engineers know exactly what will happen to the data before they merge their update. Datafold embeds a summary of these automated tests directly in GitHub and GitLab, so engineers can see the impact in every pull request.
“Improved data quality is one of the primary benefits of standardizing on dbt. Datafold’s data diff in continuous integration checks and fine-grained column-level lineage on top of dbt models augments this experience for analytics engineers,” said Julia Schottenstein, product manager at dbt Labs. “We’re excited to further our partnership with Datafold and help customers gain confidence in their data.”
dbt enabled the data community to build useful models easily in data warehouses. This created a strong foundation to build things on top of the warehouse.
Companies went from only building dashboards to building notebooks, apps, ML/AI, and reverse ETL on the warehouse, all within the past few years. Due to this huge increase in leverage of the warehouse, data quality has become a focus.
Datafold built column-level lineage at scale which it uses to give analytics engineers complete visibility into how their work impacts their pipelines. It allows analytics engineers to fix data quality issues before they ever get to production. Working together, dbt and Datafold deliver trusted data faster.
For more information about this news, visit www.datafold.com or www.getdbt.com.