Real-Time Data Pipeline Engine

Scalable ETL pipeline framework processing streaming and batch data workloads. Features automated schema evolution, data quality monitoring, and self-healing capabilities.

Python

Apache Airflow

Apache Spark

PostgreSQL

Docker

AWS S3

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Overview

A production-grade ETL framework that unifies streaming and batch data processing under a single orchestration layer. Built for data teams that need reliable, observable, and self-healing data 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

Key Achievements

Processes 800GB daily across 200+ pipeline jobs
Automated schema evolution handling 50+ schema changes/month
Self-healing pipeline recovery reduces manual intervention by 90%
Built-in data lineage tracking for compliance requirements