Automated Testing For Data Engineers

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.

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.

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.