AWS sagemaker-lakehouse-architecture documentation change
Summary
Updated documentation phrasing to use 'The lakehouse architecture of Amazon SageMaker' instead of 'SageMaker lakehouse architecture' throughout, and changed section header from 'Document history' to 'Getting started'
Security assessment
Changes are primarily branding/terminology updates and section reorganization. No security vulnerabilities or new security features are mentioned. The existing reference to 'fine-grained permissions' remains unchanged but does not indicate a security fix or new security documentation.
Diff
diff --git a/sagemaker-lakehouse-architecture/latest/userguide/lakehouse-iceberg.md b/sagemaker-lakehouse-architecture/latest/userguide/lakehouse-iceberg.md index a79cf8150..a187f659a 100644 --- a//sagemaker-lakehouse-architecture/latest/userguide/lakehouse-iceberg.md +++ b//sagemaker-lakehouse-architecture/latest/userguide/lakehouse-iceberg.md @@ -5 +5 @@ -# Apache Iceberg support in Amazon SageMaker lakehouse architecture +# Apache Iceberg support in The lakehouse architecture of Amazon SageMaker @@ -7 +7 @@ -Amazon SageMaker lakehouse architecture provides comprehensive support for [Apache Iceberg](https://docs.aws.amazon.com/prescriptive-guidance/latest/apache-iceberg-on-aws/introduction.html), enabling organizations to unify data across Amazon S3 data lakes and Amazon Redshift data warehouses while building powerful analytics and AI/ML applications on a unified data layer. +The lakehouse architecture of Amazon SageMaker provides comprehensive support for [Apache Iceberg](https://docs.aws.amazon.com/prescriptive-guidance/latest/apache-iceberg-on-aws/introduction.html), enabling organizations to unify data across Amazon S3 data lakes and Amazon Redshift data warehouses while building powerful analytics and AI/ML applications on a unified data layer. @@ -9 +9 @@ Amazon SageMaker lakehouse architecture provides comprehensive support for [Apac -With SageMaker lakehouse architecture, you gain the flexibility to access and query your data in-place using all Apache Iceberg compatible tools and engines, including open-source Apache Spark. This integration leverages the AWS Glue Iceberg REST Catalog, which provides a standardized REST API interface for managing Iceberg table metadata and enables seamless connectivity with third-party engines. For more information, see [how to use AWS Glue Iceberg Rest Catalog for accessing Iceberg tables in Amazon S3](https://docs.aws.amazon.com/glue/latest/dg/connect-glu-iceberg-rest.html). +With The lakehouse architecture, you gain the flexibility to access and query your data in-place using all Apache Iceberg compatible tools and engines, including open-source Apache Spark. This integration leverages the AWS Glue Iceberg REST Catalog, which provides a standardized REST API interface for managing Iceberg table metadata and enables seamless connectivity with third-party engines. For more information, see [how to use AWS Glue Iceberg Rest Catalog for accessing Iceberg tables in Amazon S3](https://docs.aws.amazon.com/glue/latest/dg/connect-glu-iceberg-rest.html). @@ -11 +11 @@ With SageMaker lakehouse architecture, you gain the flexibility to access and qu -Through fine-grained permissions enforced across all analytics and ML tools, SageMaker lakehouse architecture ensures secure data access while supporting advanced Iceberg features like ACID transactions, schema evolution, time travel queries, and efficient row-level operations—all essential capabilities for modern data-driven organizations seeking to process and analyze vast amounts of information efficiently. +Through fine-grained permissions enforced across all analytics and ML tools, The lakehouse architecture ensures secure data access while supporting advanced Iceberg features like ACID transactions, schema evolution, time travel queries, and efficient row-level operations—all essential capabilities for modern data-driven organizations seeking to process and analyze vast amounts of information efficiently. @@ -13 +13 @@ Through fine-grained permissions enforced across all analytics and ML tools, Sag -SageMaker lakehouse architecture also supports multiple table optimization options with Glue Catalog to enhance the management and performance of Apache Iceberg tables that the AWS analytical engines and ETL jobs uses. These optimizers provide efficient storage utilization, improved query performance, and effective data management. For more information, see [Optimizing Iceberg tables](https://docs.aws.amazon.com/glue/latest/dg/table-optimizers.html). +The lakehouse architecture also supports multiple table optimization options with Glue Catalog to enhance the management and performance of Apache Iceberg tables that the AWS analytical engines and ETL jobs uses. These optimizers provide efficient storage utilization, improved query performance, and effective data management. For more information, see [Optimizing Iceberg tables](https://docs.aws.amazon.com/glue/latest/dg/table-optimizers.html). @@ -15 +15 @@ SageMaker lakehouse architecture also supports multiple table optimization optio -With SageMaker lakehouse architecture, you can calculate and update number of distinct values (NDVs) for each column in Iceberg tables with Glue Catalog. These statistics can facilitate better query optimization, data management, and performance efficiency for data engineers and scientists working with large-scale datasets. For more information, see [Optimizing query performance for Iceberg tables](https://docs.aws.amazon.com/glue/latest/dg/iceberg-column-statistics.html). +With The lakehouse architecture, you can calculate and update number of distinct values (NDVs) for each column in Iceberg tables with Glue Catalog. These statistics can facilitate better query optimization, data management, and performance efficiency for data engineers and scientists working with large-scale datasets. For more information, see [Optimizing query performance for Iceberg tables](https://docs.aws.amazon.com/glue/latest/dg/iceberg-column-statistics.html). @@ -25 +25 @@ Data connections -Document history +Getting started