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:
-
SFTP Connection
- Secure credential management
- Robust error handling
- Connection pooling
-
Data Processing
- Flexible ticker symbol support
- Data validation and normalization
- Parallel processing capabilities
-
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
-
Scalability
- Automatic scaling with demand
- Easy addition of new tickers
- Cost-effective processing
-
Reliability
- Error handling and retries
- Monitoring and alerting
- Data validation
-
Maintainability
- Infrastructure as code
- Modular architecture
- Comprehensive logging
Implementation Process
-
Design
- Architecture planning
- AWS service selection
- Infrastructure modeling
-
Development
- Go Lambda implementation
- Terraform configuration
- Pipeline automation
-
Validation
- Performance testing
- Reliability verification
- Security assessment
Lessons Learned
- Go's concurrency features enhanced processing efficiency
- Terraform modules improved infrastructure maintainability
- S3 lifecycle policies optimized storage costs
- 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.
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