Engine1 Financial Data ETL Pipeline

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

Solution Architecture

AWS Infrastructure Components

We implemented a serverless ETL architecture using:

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:

Results

The pipeline delivered significant benefits:

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.

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