AWS Glue Data Catalog supports multi engine views with AWS Analytics Engines

AWS Glue Data Catalog now supports the creation, management, and access control of SQL views that support multiple engines. Customers can create SQL views in AWS Glue Data Catalog and query them from SQL engines including Amazon Athena, Amazon Redshift, and Spark with Amazon EMR on EC2. Today, when customers create a view and want to access that view across multiple SQL engines, they need to create separate views for each engine. Additionally, data owners need to grant access to each view and the underlying tables of the view for each engine. The view cannot be used to filter content from base tables because consumers must have direct access to those tables. Using AWS Glue Data Catalog views customers can create a single view object that can be queried from multiple engines without consumers having access to base tables of the view. Administrators can use AWS Glue Data Catalog multi-engine views to control what parts of underlying data consumers can access using the rich SQL dialects provided by SQL engines. Access to views can be controlled by the same AWS Lake Formation permissions resources, columns, and tags used to grant access to other resources in the data lake. This feature is available for preview in multiple AWS Regions including Northern Virginia, Ohio, Oregon, Ireland, and Tokyo. To get started with this feature, refer to [views documentation](https://docs.aws.amazon.com/lake-formation/latest/dg/working-with-views.html).