Data Pipeline Automation Framework

Scalable ETL pipeline for processing large-scale datasets with automated workflows and monitoring.

Overview

Built a comprehensive data pipeline automation framework that processes millions of records daily, transforming raw data into actionable insights. The system leverages cloud infrastructure and orchestration tools to ensure reliability, scalability, and maintainability.

Key Features

• Automated ETL Workflows: Apache Airflow DAGs for scheduled data processing
• Cloud-Native Architecture: AWS S3, Lambda, and Glue integration
• Data Quality Checks: Automated validation and anomaly detection
• Monitoring Dashboard: Real-time pipeline health and performance metrics
• Error Recovery: Automatic retry logic and failure notifications
• Scalable Processing: Parallel execution for large datasets

Tech Stack

• Orchestration: Apache Airflow
• Cloud Platform: AWS (S3, Lambda, Glue, RDS)
• Language: Python
• Data Processing: Pandas, PySpark
• Monitoring: CloudWatch, Custom dashboards
• Version Control: Git, CI/CD with GitHub Actions

Technical Highlights

• Designed modular ETL pipeline supporting multiple data sources
• Implemented incremental loading for efficient processing
• Created custom Airflow operators for business-specific tasks
• Built data quality framework with configurable rules
• Optimized SQL queries reducing processing time by 70%
• Implemented comprehensive logging and alerting
• Deployed infrastructure as code using Terraform

Results

• Processed 10M+ records daily with 99.9% uptime
• Reduced manual data processing time by 85%
• Cut data pipeline costs by 40% through optimization
• Improved data freshness from hours to minutes
• Enabled real-time business intelligence and reporting

Challenges & Solutions

Challenge: Handling data schema evolution
Solution: Implemented schema versioning and backward compatibility checks

Challenge: Managing pipeline dependencies
Solution: Used Airflow's DAG structure with proper dependency management

Challenge: Cost optimization for cloud resources
Solution: Implemented spot instances and lifecycle policies for storage

Challenge: Data quality at scale
Solution: Built automated validation framework with configurable rules