Posted On: Feb 11, 2020
Amazon Aurora with PostgreSQL compatibility is now available with machine learning capabilities, an option to export data into Amazon S3, and compatibility with updated PostgreSQL versions.
You can use Aurora to add machine learning (ML) based predictions to your applications, using a simple, optimized, and secure integration with Amazon SageMaker and Amazon Comprehend. Aurora machine learning is based on the familiar SQL programming language, so you don’t need to build custom integrations, move data around, learn separate tools, or have prior machine learning experience. This functionality is also available for Aurora with MySQL 5.7 compatibility.
Aurora machine learning supports any ML model available in SageMaker, or you can run sentiment analysis using Comprehend. It’s available for PostgreSQL 10 and 11, with no additional charge beyond the price of the AWS services that you are using. For more information, read the launch blog, the Aurora ML feature page, and the Aurora documentation.
The SELECT into S3 statement allows you to query data from Aurora PostgreSQL and export it directly into an Amazon S3 bucket. It’s available for PostgreSQL 10 and 11 via a new aws_s3 PostgreSQL extension. See the Aurora documentation for detailed information.
Aurora PostgreSQL with PostgreSQL 11 compatibility has been updated to version 3.1, with machine learning, S3 export, and PostgreSQL 11.6 compatibility. Aurora PostgreSQL with PostgreSQL 10 compatibility has been updated to version 2.4, with machine learning, S3 export, and PostgreSQL 10.11 compatibility. Aurora PostgreSQL with PostgreSQL 9.6 compatibility has been updated to version 1.6, compatible with PostgreSQL 9.6.16. Please see the Release Notes for other new features and enhancements.
Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. It provides up to five times better performance than the typical MySQL database and three times the performance of the typical PostgreSQL database, together with increased scalability, durability, and security. For more information, please visit the Amazon Aurora product page, and view the AWS Region Table for regional availability.