Datafold

At Datafold, we build tools for data practitioners to automate the most error-prone and time-consuming parts of the data engineering workflow: testing data to guarantee its quality. While data quality (just like software quality) is a complex and multifaceted problem, we draw from decades of our team’s combined experience in the data domain to build opinionated tools our users love. Specifically, we believe that:

• Data quality is a byproduct of a great data engineering workflow: That means, rather than building yet-another-app for data practitioners to switch to and from, we insert our tools in the existing workflows, for example, in CI/CD for deployment testing and IDEs for testing during development.

• Data quality issues should be addressed before deploying the code: Most data quality issues are bugs in the code that processes data, and applying a proactive, shift-left approach is the most effective way to achieve high shipping velocity and data quality simultaneously.

• Lack of metadata (data about data) is the biggest gap in the data engineering workflow. We bring powerful tools such as data diffing and column-level lineage to every data engineer’s workflow to help them validate the code and underlying data and fully understand the dependencies in complex data pipelines.