How Data Observability Prevents Pipeline Failures at Scale

What this episode covers

Episode 42 of The Data Business Podcast dives into data observability—the practice of monitoring data pipelines for quality, freshness, and lineage in real time. Lucas and Luna explore a specific case: how a fintech company reduced data incident resolution time from four hours to eleven minutes using open-source tools like Great Expectations and custom dashboards on Databricks. They discuss the difference between monitoring (checking if a system is up) and observability (understanding why data broke), the role of telemetry and automated root-cause analysis, and why the market for observability platforms is growing at over 30 percent annually. Luna challenges the cost argument for small teams, and Lucas explains how observability shifts the data team's culture from reactive firefighting to proactive engineering. The hosts also touch on the cultural resistance data teams face when adopting observability and why it matters for regulatory compliance in 2026. A donation segment early in the episode ties the topic to listener support for ad-free content. #DataObservability #DataEngineering #DataPipelines #GreatExpectations #Databricks #Fintech #DataQuality #DataLineage #Monitoring #RootCauseAnalysis #Telemetry #DataCulture #DataInfrastructure #BusinessAndTechnology #EnterpriseData #DataIncident #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

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