Optimizing ETL Processes with Apache Spark on Amazon Athena

Key Takeaways

• Long-running data pipelines often get longer over time, leading to operational instability and a negative feedback loop
• Rethinking the data pipeline problem, optimizing data structure, and leveraging parallelization are key strategies to improve performance
• Apache Spark and Amazon Athena are powerful tools that can help speed up ETL processes, especially in the early stages of data transformation

Customer Case Study

• The customer was an ad-media company with hundreds of terabytes of data and a legacy on-premise Hadoop solution
• Their daily data pipeline was taking over 26 hours to complete, causing operational issues and a negative feedback loop
• The main causes of the slow pipeline were:
  • Processing more data than necessary
  • Poorly structured data
  • Lack of parallelization

Optimization Case 1: Rethinking the Data Pipeline

• The team identified an unnecessary step in the pipeline that was computing aggregate statistics across all historical data
• By creating a daily aggregate table and incrementally loading new data, they were able to reduce the processing time from 6 hours to 8 minutes

Apache Spark and Amazon Athena

• Athena is a serverless, interactive SQL query service that can be used for data transformation
• Spark is a fast, in-memory data processing framework that is well-suited for data engineering tasks
• Spark on Athena combines the benefits of both, providing a serverless, interactive environment for running Spark workloads

Optimization Case 2: Leveraging Spark on Athena

• The team used Spark on Athena to rewrite a pandas-based data processing step that was taking 3 hours to run
• By leveraging Spark's distributed processing capabilities, they were able to reduce the processing time to just 2 minutes
• Key tips for using Spark on Athena:
  • Packaging custom libraries and dependencies into a zip file for deployment
  • Ensuring the correct Python environment version is used
  • Running Spark jobs asynchronously as part of a production pipeline

Conclusion

• Rethinking the data pipeline, optimizing data structure, and leveraging parallelization are key strategies to improve ETL performance
• Apache Spark and Amazon Athena provide a powerful, serverless platform for accelerating data processing tasks
• Continuous optimization and a willingness to challenge existing processes are essential for maintaining high-performing data pipelines