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How Data Teams Are Using Data Profiling for Pipeline Quality

How Data Teams Are Using Data Profiling for Pipeline Quality

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In Episode 90 of The Data Business Podcast, Lucas and Luna dive into the emerging practice of proactive data profiling—running automated checks on raw data before it enters transformation pipelines. They use a concrete case: a mid-sized e-commerce company that reduced data breakage incidents by 60 percent in three months by implementing pre-ingestion profiling checks using an open-source framework called Great Expectations. The hosts discuss why traditional monitor-and-fix approaches are insufficient, how profiling shifts data teams from reactive firefighting to preventive quality control, and the specific metrics teams should track (null rate thresholds, distribution drift, schema change detection). They also touch on the trade-offs: added latency versus prevented downstream chaos, and how this fits into the broader data contract movement covered in earlier episodes. A practical, example-driven look at a technique that's quietly becoming table stakes for serious data teams. #DataProfiling #DataQuality #GreatExpectations #PipelineQuality #DataEngineering #ProactiveMonitoring #DataObservability #SchemaDrift #DataValidation #ETL #DataInfrastructure #BusinessIntelligence #DataTeam #PreIngestion #DataGovernance #FexingoBusiness #BusinessPodcast #DataBusiness Keep every episode free: buymeacoffee.com/fexingo
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