AWS AmazonS3 documentation change
Summary
Updated service name references from 'Quick Suite' to 'Quick' in two entries about S3 table bucket integration and S3 Tables features
Security assessment
The change only updates product names without modifying security-related content. The existing security mention ('fine-grained access control through Lake Formation') remains unchanged with no evidence of vulnerability fixes.
Diff
diff --git a/AmazonS3/latest/userguide/WhatsNew.md b/AmazonS3/latest/userguide/WhatsNew.md index b7415820e..fd05d262a 100644 --- a//AmazonS3/latest/userguide/WhatsNew.md +++ b//AmazonS3/latest/userguide/WhatsNew.md @@ -40 +40 @@ Access points for directory buckets are available in AWS Local Zones| Directory -S3 table bucket integration with Amazon SageMaker Lakehouse is now generally available| You can integrate S3 table buckets with Amazon SageMaker Lakehouse to access tables from AWS analytics services, such as Amazon Athena, Amazon Redshift, and Quick Suite. 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. The integration enables fine-grained access control through Lake Formation to provide additional security. For more information about integrating, see [Using Amazon S3 Tables with AWS analytics services](https://docs.aws.amazon.com//AmazonS3/latest/userguide/s3-tables-integrating-aws.html). If you set up the integration with the preview release, you can continue to use your current integration. However, the updated integration process provides performance improvements, so we recommend migrating. To migrate to the updated integration, see [Migrating to the updated integration process](https://docs.aws.amazon.com//AmazonS3/latest/userguide/s3-tables-integrating-aws.html#migrate-integrate-console).| March 13, 2025 +S3 table bucket integration with Amazon SageMaker Lakehouse is now generally available| You can integrate S3 table buckets with Amazon SageMaker Lakehouse to access tables from AWS analytics services, such as Amazon Athena, Amazon Redshift, and Quick. 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. The integration enables fine-grained access control through Lake Formation to provide additional security. For more information about integrating, see [Using Amazon S3 Tables with AWS analytics services](https://docs.aws.amazon.com//AmazonS3/latest/userguide/s3-tables-integrating-aws.html). If you set up the integration with the preview release, you can continue to use your current integration. However, the updated integration process provides performance improvements, so we recommend migrating. To migrate to the updated integration, see [Migrating to the updated integration process](https://docs.aws.amazon.com//AmazonS3/latest/userguide/s3-tables-integrating-aws.html#migrate-integrate-console).| March 13, 2025 @@ -46 +46 @@ S3 Metadata preview| Amazon S3 Metadata helps you easily discover and understand -S3 Tables| Amazon S3 Tables provide S3 storage that’s optimized for analytics workloads, with features that improve query performance, reduce storage costs for tables, and simplify the operation of data lakes at scale. S3 Tables introduces a new bucket type: table buckets, which are purpose-built for storing Apache Iceberg tables as subresources. Table buckets provide higher transactions per second (TPS) and better query throughput compared to self-managed tables in S3 general purpose buckets. You can automatically integrate your table buckets with AWS analytics services, such as Athena, Amazon Redshift Quick Suite, and more. For more information, see [Working with Amazon S3 Tables and table buckets ](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-tables.html). | December 3, 2024 +S3 Tables| Amazon S3 Tables provide S3 storage that’s optimized for analytics workloads, with features that improve query performance, reduce storage costs for tables, and simplify the operation of data lakes at scale. S3 Tables introduces a new bucket type: table buckets, which are purpose-built for storing Apache Iceberg tables as subresources. Table buckets provide higher transactions per second (TPS) and better query throughput compared to self-managed tables in S3 general purpose buckets. You can automatically integrate your table buckets with AWS analytics services, such as Athena, Amazon Redshift Quick, and more. For more information, see [Working with Amazon S3 Tables and table buckets ](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-tables.html). | December 3, 2024