Great Expectations has become a sensible default for our teams in the data quality space, which is why we recommend adopting it — not only for the lack of better alternatives but also because our teams have reported great results in several client projects. Great Expectations is a framework that allows you to craft built-in controls that flag anomalies or quality issues in data pipelines. Just as unit tests run in a build pipeline, Great Expectations makes assertions during the execution of a data pipeline. We like its simplicity and ease of use — the rules stored in JSON can be modified by our data domain experts without necessarily needing data engineering skills.
We wrote about Great Expectations in the previous edition of the Radar. We continue to like it and have moved it to Trial in this edition. Great Expectations is a framework that enables you to craft built-in controls that flag anomalies or quality issues in data pipelines. Just as unit tests run in a build pipeline, Great Expectations makes assertions during execution of a data pipeline. We like its simplicity and ease of use — the rules stored in JSON can be modified by our data domain experts without necessarily needing data engineering skills.
With the rise of CD4ML, operational aspects of data engineering and data science have received more attention. Automated data governance is one aspect of this development. Great Expectations is a framework that enables you to craft built-in controls that flag anomalies or quality issues in data pipelines. Just as unit tests run in a build pipeline, Great Expectations makes assertions during execution of a data pipeline. This is useful not only for implementing a sort of Andon for data pipelines but also for ensuring that model-based algorithms remain within the operating range determined by their training data. Automated controls like these can help distribute and democratize data access and custodianship. Great Expectations also ships with a profiler tool to help understand the qualities of a particular data set and to set appropriate limits.
