What is the significance of data observability in engineering pipelines?
Data observability refers to the ability to monitor and understand the health and behavior of data systems. It includes tracking data quality, lineage, freshness, and anomalies across pipelines. Implementing observability ensures early detection of broken transformations, schema changes, or delayed data flows. Tools like Monte Carlo, Great Expectations, and OpenLineage are commonly used. With proper observability, engineers can ensure reliable, consistent, and trustworthy data for analytics and AI. It reduces operational overhead, improves data governance, and boosts confidence in data-driven decisions across organizations.
https://spiralmantra.com/d...
Data observability refers to the ability to monitor and understand the health and behavior of data systems. It includes tracking data quality, lineage, freshness, and anomalies across pipelines. Implementing observability ensures early detection of broken transformations, schema changes, or delayed data flows. Tools like Monte Carlo, Great Expectations, and OpenLineage are commonly used. With proper observability, engineers can ensure reliable, consistent, and trustworthy data for analytics and AI. It reduces operational overhead, improves data governance, and boosts confidence in data-driven decisions across organizations.
https://spiralmantra.com/d...
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07:34 AM - May 19, 2025 (UTC)
