Global Manufacturer Eliminates Unreliable Factory Reporting with Event-Driven Orchestration on AWS

Event-Driven Orchestration

Coforge replaced fixed-interval Airflow scheduling with event-driven execution. AWS Lambda monitored data availability at the source and triggered downstream processing only after confirmation of readiness. This directly eliminated the empty and incomplete runs that had been generating backlog, volume spikes, and cascading failures across the factory reporting stack.

Pipeline Gating via AWS Glue

Coforge introduced AWS Glue to manage data ingestion from the Enterprise Landing zone to the data warehouse, with Lambda gating Airflow DAG execution on confirmed Glue job success. A configurable delay mechanism was built to handle variability in source data arrival from factory shifts and on-premises machine systems, ensuring no downstream process ran on incomplete data.

BI Refresh Tied to Pipeline Completion

Coforge decoupled Power BI dataset refreshes from fixed schedules and tied them directly to pipeline completion. Dashboards were gated on upstream success, ensuring factory operations data published to leadership reflected fully processed, accurate figures. Full-volume refreshes were replaced with event-triggered execution, reducing compute overhead without compromising data fidelity.

Resilience, Observability, and Security

Coforge built centralized logging, status-based alerting, and robust error handling into the pipeline to reduce incident mean time to resolution. Idempotent, retry-capable triggers were implemented to handle late-arriving factory and machine data. IP whitelisting enforced least-privilege access to Airflow's external API, closing a security gap in the existing architecture.