Cloud Migration of Airflow Data Pipeline for Stock Market Data
A comprehensive data engineering project focused on migrating a containerized Apache Airflow data pipeline to the cloud, enabling scalable, event-driven, and highly available data processing for stock market data.
Technologies Used
Key Features
- • Event-Driven Pipeline
- • Containerized DAGs
- • Automated Monitoring
Infrastructure
- • AWS IAM Roles
- • CloudWatch Alerts
- • Scalable Architecture
Project Overview
In this data engineering project, I migrated a containerized Apache Airflow data pipeline from an on-premise environment to the cloud. The objective was to enable scalable, event-driven, and highly available data processing for stock market data movement.
Key Objectives
- • Migrate an on-premise Airflow data pipeline to a cloud-based server environment
- • Implement an event-based ETL pipeline triggered by file-based events
- • Ensure modularity and scalability for seamless deployment and monitoring
Implementation Details
1. Pipeline Configuration & Infrastructure Setup
- • Configured pipeline layers including Data Layer, Scripts Layer, and Infrastructure Layer
- • Containerized Airflow DAGs using Docker to orchestrate data movement
- • Deployed Airflow on an AWS EC2 instance, ensuring scalability and fault tolerance
- • Established AWS IAM roles and policies for secure data access and execution control
2. ETL Pipeline Development & Orchestration
- • Designed a modular and server-based ETL pipeline to extract, transform, and load stock market data
- • Implemented Apache Airflow DAGs to orchestrate the data workflow from source to target
- • Ensured real-time processing by integrating AWS Lambda to trigger DAG executions
- • Configured Amazon S3 as a staging area for stock data before transformation and storage
3. Event-Based Pipeline Automation
- • Configured the pipeline to execute when a .SUCCESS file is dropped into an AWS-monitored S3 bucket
- • Integrated AWS Lambda to listen for S3 events and trigger the Airflow DAG API
- • Set up CloudWatch alerts to monitor pipeline health and execution status
- • Used Amazon RDS as the final storage layer, optimizing data for querying and analytics
Outcome & Impact
This migration transformed the existing on-premise solution into a scalable, automated, and cloud-based data pipeline. The new architecture enhanced data availability, processing speed, and operational efficiency while leveraging AWS best practices for security and performance optimization.
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