![]() ![]() ![]() Data testing is a must-have to catch specific, known problems that surface in your data pipelines, and there are great tools to help you do it. In 2021, as companies ingest more and more data and pipelines become increasingly complex, this single point-of-failure approach doesn’t cut it any more.ĭon’t get us wrong: you SHOULD be testing your most important data. Here’s how some of the best data teams are taking a more comprehensive approach to tackling both of them at scale.įor the past several years, data teams have leveraged the equivalent of unit testing to detect data quality issues. There are two types of data quality issues in this world: those you can predict (known unknowns) and those you can’t (unknown unknowns). ![]()
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