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Today, we are pleased to announce that AWS Compute Optimizer now supports exporting recommendations to Amazon Simple Storage Service (S3).
With this new feature, customers can export multiple EC2 instance type recommendations, including those that are part of EC2 Auto Scaling groups, as a csv file to an S3 bucket. Customers can specify the type of recommendations they want to export, the columns and filtering criteria, and the target S3 bucket and object name of the export file. Integrated with AWS Organizations, customers can also use their master account to export recommendations from multiple member accounts within their organizations to a single csv file. This feature is available in all Regions AWS Compute Optimizer supports.
There’s no additional cost to export EC2 instance type recommendations to Amazon S3. Customers only pay for the S3 storage cost needed to store the export file. You can create export jobs through the AWS Compute Optimizer Console, CLI, or SDK.
You can now write the results of an Amazon Redshift query to an external table in Amazon S3 either in text or Apache Parquet formats. The external table metadata will be automatically updated and can be stored in AWS Glue, AWS Lake Formation, or your Hive Metastore data catalog. This enables you to easily share your data in the data lake and have it immediately available for analysis with Amazon Redshift Spectrum and other AWS services such as Amazon Athena, Amazon EMR, and Amazon SageMaker. Amazon Redshift Spectrum enables you to power a lake house architecture to directly query and join data across your data warehouse and data lake.
To start writing to external tables, simply run CREATE EXTERNAL TABLE AS SELECT to write to a new external table, or run INSERT INTO to insert data into an existing external table. This enables you to simplify and accelerate your data processing pipelines using familiar SQL and seamless integration with your existing ETL and BI tools. You can use the PARTITIONED BY option to automatically partition the data and take advantage of partition pruning to improve query performance and minimize cost. For example, you can write your marketing data to your external table and choose to partition it by year, month, and day columns. For more information, refer to the Amazon Redshift documentation for CREATE EXTERNAL TABLE and INSERT.
Amazon Redshift write to external tables feature is supported with Redshift release version 1.0.15582 or later. Refer to the AWS Region Table for Amazon Redshift availability.