Apollo Labs ETL Pipeline Improvements

Overview

Apollo Labs, an Arizona-based cannabis testing laboratory, needed to modernize and optimize their data processing pipeline to handle increasing test volumes while maintaining regulatory compliance. This case study explores how we rebuilt their ETL pipeline using AWS services to create a robust, scalable solution.

Challenge

• Legacy ETL pipeline was error-prone and required manual intervention
• Growing test volume exceeded existing pipeline capacity
• Data quality issues impacting reporting accuracy
• Limited visibility into pipeline status and failures
• Compliance reporting requirements becoming more stringent

Solution Architecture

AWS Infrastructure Components

We implemented a modern serverless ETL architecture using:

• AWS Glue - For ETL job orchestration and data catalog
• AWS Batch - Heavy data processing workloads
• AWS Lambda - Event-driven data transformations
• AWS Step Functions - Workflow orchestration
• Amazon Athena - Ad-hoc data analysis
• Amazon S3 - Data lake storage
• Amazon CloudWatch - Monitoring and alerting

Pipeline Workflow

1. Data Ingestion

   • Automated ingestion from laboratory instruments
   • Data validation and standardization
   • Raw data storage in S3

2. Data Processing

   • Parallel processing using AWS Batch
   • Quality control checks
   • Data enrichment and transformation

3. Data Analytics

   • Athena queries for compliance reporting
   • Business intelligence dashboards
   • Automated report generation

Results

The new pipeline delivered significant improvements:

• 90% reduction in manual interventions
• 70% faster data processing
• 99.9% pipeline reliability
• 100% compliance reporting accuracy

Key Benefits

1. Automation

   • Fully automated data processing
   • Self-healing error handling
   • Automated quality checks

2. Scalability

   • Elastic resource scaling
   • Cost-effective processing
   • Handles peak load efficiently

3. Visibility

   • Real-time pipeline monitoring
   • Comprehensive audit trails
   • Error tracking and alerting

Lessons Learned

1. Early focus on data validation prevented downstream issues
2. Step Functions provided crucial orchestration capabilities
3. Athena's flexibility enhanced reporting capabilities
4. Infrastructure as Code simplified maintenance

Conclusion

The modernized ETL pipeline transformed Apollo Labs' data processing capabilities, enabling them to scale their testing operations while maintaining strict quality and compliance standards. The AWS-based solution provides a foundation for future growth and additional analytical capabilities.