Athena also supports open source columnar formats and compressed data formats, such as Snappy, Zlib, LZO, and GZIP. Therefore, primary keys should be declared if your ETL process or some other process in your application enforces their integrity.įor Amazon Athena, duplication can occur where the underlying Amazon S3 datasets contain duplicate values.Īmazon Athena supports many data formats, including CSV, TSV, and JSON. Primary keys and foreign keys are used as planning hints. Redshift doesn’t enforce a Primary Key constraint for the data you load, meaning inaccurate results due to duplication are a possibility. Partitioning the external tables improves performance, because the Amazon Redshift query optimizer eliminates partitions that don’t contain data. It is possible to partition external tables on one or more columns. It’s vital to choose the right keys for each table to ensure the best performance in Redshift.Īmazon has recently added the ability to perform table partitioning using Amazon Spectrum. Rather, Redshift uses defined distribution styles to optimize tables for parallel processing. Redshift does not support table partitioning by default. You can use any key to partition data with Athena-the maximum partitions per table is 20,000. So, intelligently partitioning data leads to cost benefits in Athena. In Athena, the price you get charged for the service depends on the bytes scanned. Partitioning improves performance by ensuring queries only run on relevant data grouped into smaller tables. You need to prepare a cluster, chose the right settings for it, and load data into tables. Athena requires zero infrastructure-it directly queries data already stored on Amazon S3. You can get Athena up and running in minutes.
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