Great Expectations is the leading tool for validating, documenting, and profiling your data to maintain quality and improve communication between teams. Head over to our getting started tutorial.
Software developers have long known that automated testing is essential for managing complex codebases. Great Expectations brings the same discipline, confidence, and acceleration to data science and data engineering teams.
One of the key statements we hear from data engineering teams that use Great Expectations is: “Our stakeholders would notice data issues before we did – which eroded trust in our data!”
With Great Expectations, you can assert what you expect from the data you load and transform, and catch data issues quickly – Expectations are basically unit tests for your data. Not only that, but Great Expectations also creates data documentation and data quality reports from those Expectations. Data science and data engineering teams use Great Expectations to:
Test data they ingest from other teams or vendors and ensure its validity. Validate data they transform as a step in their data pipeline in order to ensure the correctness of transformations. Prevent data quality issues from slipping into data products. Streamline knowledge capture from subject-matter experts and make implicit knowledge explicit. Develop rich, shared documentation of their data.