AWS AmazonS3 documentation change
Summary
Updated documentation to emphasize integration with AWS analytics services (SageMaker LakeHouse, Glue Data Catalog, Lake Formation) and Iceberg-compatible tools. Added permissions section reference and removed some redundant content.
Security assessment
Added a new 'Permissions for querying metadata tables' section link, which indicates documentation about access control requirements. While this relates to security configuration, there is no evidence of addressing a specific vulnerability or incident.
Diff
diff --git a/AmazonS3/latest/userguide/metadata-tables-querying.md b/AmazonS3/latest/userguide/metadata-tables-querying.md index 354e658e3..39fc63c21 100644 --- a//AmazonS3/latest/userguide/metadata-tables-querying.md +++ b//AmazonS3/latest/userguide/metadata-tables-querying.md @@ -9 +9,9 @@ Query pricing considerations -Amazon S3 Metadata lets you analyze your S3 managed metadata tables with any query engine that supports the Apache Iceberg format. When you integrate your [S3 table buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-tables.html) with AWS analytics services, you can use services like Amazon Athena, Amazon Redshift, and others to help you do the following with your metadata tables: +Your Amazon S3 Metadata tables are stored in an AWS managed S3 table bucket, which provides storage that's optimized for tabular data. To query your metadata, you can integrate your table bucket with Amazon SageMaker Lakehouse. This integration, which uses the AWS Glue Data Catalog and AWS Lake Formation, allows AWS analytics services to automatically discover and access your table data. + +After your table bucket is integrated with the AWS Glue Data Catalog, you can directly query your metadata tables with AWS analytics services such as Amazon Athena, Amazon EMR, and Amazon Redshift. You can also create interactive dashboards with your query data by using Amazon QuickSight. + +For more information about integrating your AWS managed S3 table bucket with Amazon SageMaker Lakehouse, see [Using Amazon S3 Tables with AWS analytics services](./s3-tables-integrating-aws.html). + +You can also query your metadata tables with Apache Spark, Apache Trino, and any other application that supports the Apache Iceberg format by using the AWS Glue Iceberg REST endpoint, Amazon S3 Tables Iceberg REST endpoint, or the Amazon S3 Tables Catalog for Apache Iceberg client catalog. For more information about accessing your metadata tables, see [Accessing table data](./s3-tables-access.html). + +You can analyze your metadata tables with any query engine that supports the Apache Iceberg format. For example, you can query your metadata tables to do the following: @@ -24,3 +32 @@ Amazon S3 Metadata lets you analyze your S3 managed metadata tables with any que -After your table buckets are [integrated with AWS analytics services](./s3-tables-integrating-aws.html), you can query your metadata tables. This includes joining S3 managed metadata tables and custom metadata tables, allowing you to query across multiple datasets as long as the metadata tables are stored in the same table bucket. - -From there, you can create interactive dashboards with your query data by using Amazon QuickSight. +You can also join S3 managed metadata tables and custom metadata tables, allowing you to query across multiple datasets. @@ -35,0 +42,2 @@ For information on making your queries more cost effective, see [Optimizing meta + * [Permissions for querying metadata tables](./metadata-tables-bucket-query-permissions.html) + @@ -44,4 +51,0 @@ For information on making your queries more cost effective, see [Optimizing meta - * [Joining custom metadata with S3 metadata tables](./metadata-tables-join-custom-metadata.html) - - * [Visualizing metadata table data with Amazon QuickSight](./metadata-tables-quicksight-dashboards.html) - @@ -59 +63 @@ Deleting metadata tables -Querying metadata tables with AWS analytics services +Permissions for querying metadata tables