Ensuring data quality in big data environments can be challenging due to the sheer scale and complexity of the data. However, using automated data quality checks and data profiling processes can help address data quality issues in a timely and efficient manner. Deequ, a library built on top of Apache Spark, enables organizations to define “unit tests for data” which measure data quality in large datasets. It offers a complete testing toolbox covering dataframe properties, integrity constraints, and checks over values. After the checks are defined and integrated into a test suite, reports are generated highlighting whether checks have been successful or not. Companies can use the check result as an early detection system to block the dataframe write if it doesn’t fulfill the required constraints. Companies can also create their procedures from scratch or use Deequ as a ready-to-use solution. By leveraging advanced quality checks, organizations can make informed decisions, improve processes, and gain a competitive advantage.
