Engine1 Financial Data ETL Pipeline

Overview

Engine1, a financial technology company, needed a flexible and scalable ETL pipeline to process stock market data from SFTP sources. This case study explores how we built a modern, serverless pipeline using AWS services and Go that could handle any ticker symbol while maintaining high reliability and performance.

Challenge

• Regular ingestion of stock data from SFTP servers needed
• Support for dynamic addition of new ticker symbols
• Strict data accuracy and timeliness requirements
• Cost-effective processing at scale
• Infrastructure as code requirements

Solution Architecture

AWS Infrastructure Components

We implemented a serverless ETL architecture using:

• AWS Lambda - Scheduled data fetching
• Amazon S3 - Data lake storage
• AWS Glue - Data catalog and ETL jobs
• Amazon Athena - SQL querying and analysis
• AWS Secrets Manager - SFTP credentials
• Amazon CloudWatch - Monitoring and logging

Pipeline Implementation

Data Ingestion

The Go-based Lambda function handles data fetching:

1. SFTP Connection

   • Secure credential management
   • Robust error handling
   • Connection pooling

2. Data Processing

   • Flexible ticker symbol support
   • Data validation and normalization
   • Parallel processing capabilities

3. S3 Storage

   • Organized data partitioning
   • Efficient compression
   • Version control

Infrastructure as Code

All infrastructure managed via Terraform:

• Modular Design - Reusable components
• Environment Parity - Consistent deployments
• State Management - Remote state storage
• Security Controls - IAM policies and encryption

Results

The pipeline delivered significant benefits:

• 99.9% data processing reliability
• Support for 1000+ ticker symbols
• 75% reduction in processing costs
• Zero manual intervention needed

Key Benefits

1. Scalability

   • Automatic scaling with demand
   • Easy addition of new tickers
   • Cost-effective processing

2. Reliability

   • Error handling and retries
   • Monitoring and alerting
   • Data validation

3. Maintainability

   • Infrastructure as code
   • Modular architecture
   • Comprehensive logging

Implementation Process

1. Design

   • Architecture planning
   • AWS service selection
   • Infrastructure modeling

2. Development

   • Go Lambda implementation
   • Terraform configuration
   • Pipeline automation

3. Validation

   • Performance testing
   • Reliability verification
   • Security assessment

Lessons Learned

1. Go's concurrency features enhanced processing efficiency
2. Terraform modules improved infrastructure maintainability
3. S3 lifecycle policies optimized storage costs
4. Athena provided valuable data insights

Conclusion

The ETL pipeline built for Engine1 demonstrates how modern AWS services, Go, and infrastructure as code can create a robust, scalable solution for financial data processing. The flexible architecture continues to support their growing needs while maintaining high reliability and performance.