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 methodology: defining objectives, selecting appropriate tests (null checks, uniqueness, referential integrity, regex, cardinality), automating testing, documenting findings, and iterating. The guide also covers continuous monitoring strategies such as real-time alerts, regular audits, and stakeholder engagement.
Table of contents
IntroductionUnderstand Data Quality FundamentalsIdentify Tools for Data Quality TestingExecute Data Quality Tests MethodicallyMonitor and Maintain Data Quality ContinuouslyConclusionFrequently Asked QuestionsSort: