How Data Observability Prevents Billion-Dollar Fire Drills

What this episode covers

Data observability is the practice of monitoring data pipelines for quality, freshness, and lineage in real-time. In this episode, Lucas and Luna explore how companies like Uber and Snowflake use observability tools to catch data quality issues before they cascade into expensive outages. The hosts break down the three pillars of observability—freshness, volume, and schema—and discuss why traditional monitoring falls short in modern data stacks. They also examine the case of a major bank that avoided a $100 million trading error by catching a schema drift in their risk models. The conversation touches on open-source projects like Great Expectations and how they compare to commercial platforms. Listeners will learn why data observability is quickly becoming a must-have for any data-driven organization. #DataObservability #DataEngineering #DataQuality #DataPipelines #GreatExpectations #Snowflake #Uber #DataFreshness #SchemaDrift #DataLineage #DataCulture #DataOps #BusinessTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure #DataMonitoring #RealTimeData Keep every episode free: buymeacoffee.com/fexingo

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