Speeding up ETL processing with Apache Spark on Amazon Athena (DEV402)

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

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