A large manufacturing enterprise with a high-velocity analytics function needed a data pipeline built for the demands of enterprise scale, which could handle different data at various speeds, validate every record before it reached production, and give the team complete control over the data pipeline.

Transformation Assessed. Engineered. Delivered.

Before

Data was refreshed once daily through a manual process, creating latency between source and insight.
Without a validation layer, out-of-range or incomplete records could enter production undetected.
No automated alerting. Pipeline failures were identified reactively.

What we built

Fully automated Fabric Pipeline  runs at 10 AM and 1 PM daily, fully hands-off.
Data quality scripts validate each row before load: completeness, date range, nulls.
Real-time alerts that include the run status and a comprehensive record summary.

After

Clean, current data delivered twice daily, fully automated.
100% of validated records reaching the semantic layer through the semantic layer.
Zero blind spots with automated alerts and a full JSON audit trail on every run.

Business Impact Engineered to Perform

Fully automated removal of stale records at the source, eliminating human error and manual file management

Enhanced cross-team transparency through instant record summaries provided after every ingestion

100% records validated before load by baking quality checks directly into the data pipeline

Complete pipeline visibility with automated email summaries and a built-in audit trail.

Technologies & Tools Built for Intelligent Enterprise

Microsoft Fabric

Dataflow Gen2

Python Notebook

Lakehouse

JSON Logs