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. High-performance queries for business reporting tools using Redshift and a scalable data warehouse infrastructureĪmazon Athena vs Redshift: Base Comparison.
Quick query access for troubleshooting performance issues with an application using Athena.The benefits of using both services together are: You can register the staging database with Amazon Athena to query the same data. You can use Amazon S3 to create a staging database before loading the data into Redshift. Note that because of their different use cases, it can be good idea to use both Amazon Athena and Redshift together. You simply define a table in Athena and use standard SQL to query data without worrying about the format of the data. Amazon Athena: Main Use CasesĪmazon Athena is a query service that doesn’t require you to setup or manage any infrastructure.Īthena lets you easily query data stored in the cloud on Amazon’s S3 data storage service.Ī query service like Athena is a faster option for getting information from your data. Nodes are servers that can communicate with each other inside a Redshift cluster. Redshift requires you to setup and manage servers in collections known as clusters. Redshift is optimized for high-performance, allowing you to quickly run complex queries that join large numbers of huge database tables. For example, you can combine data from inventory systems, financial systems, and retail sales systems into a common format for business intelligence and reporting. Amazon Redshift: Main Use CasesĪmazon Redshift is a data warehouse best suited to pulling together data from a disparate variety of sources. By understanding the main uses of each and comparing them under key headings, you can come to a more informed decision in choosing the right tools for your company’s data needs. The following article provides a brief comparison of the Amazon Athena and Redshift data services.
#Redshift foreign key portable
Athena provides a cheaper and more portable way to query data while Redshift offers unrivalled performance and scalability. Tools such as Amazon Athena and Amazon Redshift have changed data warehouse technology, catering for a move towards interactive, real-time, analytical solutions.īoth Amazon Athena and Redshift offer their own unique benefits and use cases. Modern cloud-based data services have revolutionized the way companies manage their data.
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