A structured guide covering how data engineers can test and maintain data quality. It explains the five core dimensions of data quality (accuracy, completeness, consistency, timeliness, uniqueness), reviews popular tools including Great Expectations, Apache Deequ, Monte Carlo, and Decube, and walks through a five-step testing
Table of contents
IntroductionUnderstand Data Quality FundamentalsIdentify Tools for Data Quality TestingExecute Data Quality Tests MethodicallyMonitor and Maintain Data Quality ContinuouslyConclusionFrequently Asked QuestionsSort: