Optimize data layout by bucketing with Amazon Athena and AWS Glue to accelerate downstream queries
Big Data Blog
This article demonstrates how to optimize data layout and query performance by using partitioning and bucketing techniques with Amazon Athena and AWS Glue. It covers a use case where analysts need to run queries on a large public dataset (NOAA Integrated Surface Database) and complete them within 10 seconds while optimizing costs.
Specifically, the article covers:
- Creating a baseline table and evaluating its query performance
- Optimizing data layout using Athena CTAS (Create Table As Select) with partitioning and bucketing
- Optimizing data layout using AWS Glue ETL with partitioning and Spark-based bucketing
- Optimizing data layout for Apache Iceberg tables with hidden partitioning and bucketing
- Comparing the query performance and data scan sizes across different table configurations
- Conclusion: Bucketing can contribute to accelerating query latency and reducing data scan size, further optimizing costs
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