AWS AmazonS3 documentation change
Summary
Updated integration details with AWS Glue ETL and fixed broken documentation links
Security assessment
The change adds AWS Glue ETL to the list of supported services and fixes formatting/linking issues. While the document discusses access control, no new security features or vulnerability mitigations are introduced.
Diff
diff --git a/AmazonS3/latest/userguide/s3-tables-access.md b/AmazonS3/latest/userguide/s3-tables-access.md index 8dd3b78e2..c2fbe9ef1 100644 --- a//AmazonS3/latest/userguide/s3-tables-access.md +++ b//AmazonS3/latest/userguide/s3-tables-access.md @@ -14 +14 @@ There are multiple ways to access tables in Amazon S3 table buckets, you can int -This is the recommended access method for working with tables in S3 table buckets. The integration gives you unified table management, centralized governance, and fine-grained access control across multiple AWS analytics services. +This is the recommended access method for working with tables in S3 table buckets. The integration gives you unified table management, centralized governance, and fine-grained access control across multiple AWS analytics services. After integration, you can query tables in services such as Athena and Amazon Redshift. @@ -27 +27 @@ To access tables the IAM identity you use needs access to your table resources a -You can integrate S3 table buckets with Amazon SageMaker Lakehouse to access tables from AWS analytics services, such as Amazon Athena, Amazon Redshift, and QuickSight. Amazon SageMaker Lakehouse unifies your data across Amazon S3 data lakes and Amazon Redshift data warehouses, so you can build analytics, machine learning (ML), and generative AI applications on a single copy of data. The integration populates the AWS Glue Data Catalog with your table resources, and federates access to these resources with AWS Lake Formation. For more information on integrating, see [Using Amazon S3 Tables with AWS analytics services](./s3-tables-integrating-aws.html). +You can integrate S3 table buckets with Amazon SageMaker Lakehouse to access tables from AWS analytics services, such as Amazon Athena, Amazon Redshift, and QuickSight. Amazon SageMaker Lakehouse unifies your data across Amazon S3 data lakes and Amazon Redshift data warehouses, so you can build analytics, machine learning (ML), and generative AI applications on a single copy of data. The integration populates the AWS Glue Data Catalog with your table resources, and federates access to these resources with AWS Lake Formation. For more information on integrating, see [](./.html#s3-tables-integrating-aws). @@ -42,0 +43,2 @@ The following AWS analytics services can access tables through this integration: + * [AWS Glue ETL](./s3-tables-integrating-glue.html) + @@ -103 +105 @@ Managing policies -Using S3 Tables with AWS analytics services +S3 Tables integration overview