AWS Big Data Blog
Tag: Performance
Accelerate Amazon Redshift Data Lake queries with AWS Glue Data Catalog Column Statistics
Over the last year, Amazon Redshift added several performance optimizations for data lake queries across multiple areas of query engine such as rewrite, planning, scan execution and consuming AWS Glue Data Catalog column statistics. In this post, we highlight the performance improvements we observed using industry standard TPC-DS benchmarks. Overall execution time of TPC-DS 3 TB benchmark improved by 3x. Some of the queries in our benchmark experienced up to 12x speed up.
Run Apache Spark workloads 3.5 times faster with Amazon EMR 6.9
In this post, we analyze the results from our benchmark tests running a TPC-DS application on open-source Apache Spark and then on Amazon EMR 6.9, which comes with an optimized Spark runtime that is compatible with open-source Spark. We walk through a detailed cost analysis and finally provide step-by-step instructions to run the benchmark. With Amazon EMR 6.9.0, you can now run your Apache Spark 3.x applications faster and at lower cost without requiring any changes to your applications. In our performance benchmark tests, derived from TPC-DS performance tests at 3 TB scale, we found the EMR runtime for Apache Spark 3.3.0 provides a 3.5 times (using total runtime) performance improvement on average over open-source Apache Spark 3.3.0.
How to get best price performance from your Amazon Redshift Data Sharing deployment
Amazon Redshift is a fast, scalable, secure, and fully-managed data warehouse that enables you to analyze all of your data using standard SQL easily and cost-effectively. Amazon Redshift Data Sharing allows customers to securely share live, transactionally consistent data in one Amazon Redshift cluster with another Amazon Redshift cluster across accounts and regions without needing to […]