ETL Modernization with Apache Airflow
I migrated legacy ETL processes from cron jobs and stored procedures to Apache Airflow, improving reliability, observability, and maintainability.
At a glance
- •ETL success rate: 87% → 99.5%
- •MTTR: 4 hours → 20 minutes
- •Data quality incidents: -70%
Problem Statement
A data team was juggling 50+ ETL processes across cron jobs, SQL Server Agent jobs, and custom Python scripts. This mess led to poor visibility into job status, painful debugging, no dependency management, and constant data quality issues. There was no centralized logging or alerting.
Architecture & Design
I migrated everything to Apache Airflow running on a Kubernetes cluster. Organized DAGs by business domain with clear dependency chains. Integrated dbt for transformation logic to separate it from orchestration. Implemented a medallion architecture (bronze/silver/gold layers) in the data warehouse for better quality management.
Implementation Details
I built reusable Airflow operators for common patterns like database extraction, file transfer, and data quality checks. Migrated legacy stored procedures to Python and dbt models. Set up comprehensive logging and alerting via Slack and PagerDuty. Created CI/CD pipelines for DAG deployment with automated testing. Built monitoring dashboards showing DAG success rates and execution times.
Results & Outcome
ETL reliability jumped from 87% to 99.5% success rate. Mean time to resolution dropped from 4 hours to 20 minutes through better observability. New data engineers onboarded in 2 days instead of 2 weeks. Data quality incidents fell by 70% with automated validation checks.