AWS glue documentation change
Summary
Removed redundant sentence about SageMaker Lakehouse data access flexibility
Security assessment
Purely editorial change removing duplicated content about data access. No modifications to security statements about fine-grained access controls or data sharing permissions.
Diff
diff --git a/glue/latest/dg/zero-etl-using.md b/glue/latest/dg/zero-etl-using.md index f8ad7408d..214eec416 100644 --- a//glue/latest/dg/zero-etl-using.md +++ b//glue/latest/dg/zero-etl-using.md @@ -15 +15 @@ Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service -Lakehouse architecture of Amazon SageMaker unifies all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. Lakehouse architecture of Amazon SageMaker gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines. With SageMaker Lakehouse, you also have the flexibility to access and query your data in-place with Apache Iceberg compatible tools and engines. Additionally, you can secure your data with integrated, fine-grained access controls, that are enforced across all your data in all analytic tools and engines. Define permissions once and confidently share data across your organization. +Lakehouse architecture of Amazon SageMaker unifies all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. Lakehouse architecture of Amazon SageMaker gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines. Additionally, you can secure your data with integrated, fine-grained access controls, that are enforced across all your data in all analytic tools and engines. Define permissions once and confidently share data across your organization.