Real-Time Data Pipeline EngineReal-Time Data Pipeline Engine

Overview

An ETL framework that unifies streaming and batch data processing under a single orchestration layer, with a focus on observability and reliability.

Problem

Data teams often need batch and streaming workflows to share quality checks, schema handling, and recovery patterns instead of living as separate one-off pipelines.

Architecture

• Orchestration: Apache Airflow DAGs with dynamic task generation
• Processing: Spark clusters for heavy transformations, Python for lightweight ETL
• Storage: S3 data lake with PostgreSQL metadata store
• Monitoring: Custom data quality framework with anomaly detection

Engineering Tradeoffs

• Used Airflow for workflow visibility and retries instead of hiding orchestration inside scripts
• Kept schema metadata in PostgreSQL so pipeline changes could be inspected and audited
• Sent heavy transforms to Spark while keeping simpler jobs in Python for operational speed

Key Achievements

• Supports daily processing across many pipeline jobs
• Includes automated schema evolution handling
• Adds recovery workflows to reduce manual intervention
• Provides built-in data lineage tracking for compliance support

Validation Focus

• Run backfill and replay workflows to verify recovery behavior
• Track data freshness, anomaly rates, and failed-task retries
• Confirm lineage metadata updates when schemas evolve