Data Orchestration: How It Works And Where It Stops

This post details the concept of data orchestration, differentiating it from ETL, data integration, and workflow automation. It explains how orchestration coordinates data collection, transformation, and activation stages using DAGs. Crucially, it highlights the 'last mile' problem where orchestration alone doesn't solve issues like untrusted dashboards, metric inconsistency, or business users bypassing governed data. The post then introduces extending governance into the analytics layer through semantic layers for metric definitions, consistent access controls, lineage beyond pipeline execution, and version control for governance changes. It concludes by explaining how combining orchestrated data with a governed analytics layer reduces manual handoffs and enables effective self-serve analytics.