Amazon Athena updates data source connectors to improve performance and reduce cost
Amazon Athena has updated its data source connectors with new optimizations that can improve performance and reduce cost when querying data that does not reside in Amazon S3. Available today, the updated connectors use dynamic filtering and new predicate push down optimizations to perform more operations in the underlying data source, rather than in Athena, which can reduce query processing time and reduce data scanned.
Amazon Athena has updated its data source connectors with new optimizations that can improve performance and reduce cost when querying data that does not reside in Amazon S3. Available today, the updated connectors use dynamic filtering and new predicate push down optimizations to perform more operations in the underlying data source, rather than in Athena, which can reduce query processing time and reduce data scanned.
Athena uses connectors to run federated queries on 30 different data sources. Connectors process data where it’s stored, so there’s no need for ETL scripts to extract data from underlying data sources before it can be used for interactive analysis, business intelligence reporting, and other use cases. Today’s launch expands the set of query operations that are pushed down to the data source for processing.
To get started, be sure you’re using Athena engine version 3 and then upgrade your data source connectors to the latest version available. For information on how to upgrade a connector, see Updating a data source connector in the Amazon Athena User Guide.