AWS sagemaker-lakehouse-architecture documentation change
Summary
Updated documentation to emphasize security controls, zero-ETL integrations, and Apache Iceberg compatibility. Removed standalone 'Key Capabilities' section while integrating security context into main content.
Security assessment
Added explicit references to securing data through fine-grained permissions enforced across analytics/ML tools, and mentions AWS Lake Formation permission checks. However, there is no evidence these changes address a specific vulnerability or incident - they appear to document existing security features rather than fix issues.
Diff
diff --git a/sagemaker-lakehouse-architecture/latest/userguide/what-is-smlh.md b/sagemaker-lakehouse-architecture/latest/userguide/what-is-smlh.md index 76790829e..88cd4f073 100644 --- a//sagemaker-lakehouse-architecture/latest/userguide/what-is-smlh.md +++ b//sagemaker-lakehouse-architecture/latest/userguide/what-is-smlh.md @@ -3 +3 @@ -[Documentation](/index.html)[Amazon SageMaker lakehouse architecture](/next-generation-sagemaker/index.html)[Amazon SageMaker lakehouse architecture User Guide](what-is-smlh.html) +[Documentation](/index.html)[Amazon SageMaker lakehouse architecture](/next-generation-sagemaker/index.html)[User Guide](what-is-smlh.html) @@ -5 +5 @@ -What is a data lakehouse?Key Capabilities +What is a data lakehouse? @@ -9 +9 @@ What is a data lakehouse?Key Capabilities -The lakehouse architecture of Amazon SageMaker is a unified data architecture built on AWS's cloud-native infrastructure that bridges Amazon S3 data lakes and Amazon Redshift data warehouses into a cohesive analytics platform. The architecture leverages Apache Iceberg table format for cross-service interoperability and implements a shared metadata catalog that provides consistent data access patterns across storage systems. +The lakehouse architecture of Amazon SageMaker unifies data across Amazon S3 data lakes and Amazon Redshift data warehouses so you can work with your data in one place. You can bring data from operational databases and business applications into your lakehouse in near real-time through zero-ETL integrations. Additionally, run federated queries on data stored across multiple external data sources to access and query your data in-place. The lakehouse architecture is compatible with the Apache Iceberg open standard, giving you the flexibility to use your preferred analytics engine. Secure your data in the lakehouse architecture by defining fine-grained permissions that are enforced across all analytics and machine learning (ML) tools and engines. @@ -11 +11,3 @@ The lakehouse architecture of Amazon SageMaker is a unified data architecture bu -This integrated approach enables organizations to perform analytics, machine learning, and AI workloads on a single data foundation without data movement or duplication. The architecture integrates with AWS machine learning and analytics services, enabling data scientists, analysts, and engineers to collaborate on the same datasets using their preferred tools and interfaces. +The lakehouse architecture works by creating a single catalog where you can discover and query all your data. When you run a query, AWS Lake Formation checks your permissions while the query engine processes data directly from its original storage location, whether that's Amazon S3 or Amazon Redshift. + +The lakehouse architecture leverages [Apache Iceberg](https://iceberg.apache.org/) table format for enhanced big data storage and analysis across multiple analytics engines. lakehouse architecture introduces Apache Iceberg REST API interface as part of the AWS Glue Data Catalog to support Iceberg compatible analytics query engines, both AWS and non-AWS. You can access both the Amazon S3 data lakes including Amazon S3 Tables and Amazon Redshift warehouse tables as Iceberg tables using the supported integrated engines, such as Amazon Athena and Spectrum. @@ -15 +17 @@ This integrated approach enables organizations to perform analytics, machine lea -A data lakehouse is an architectural pattern that unifies the scalability and cost-effectiveness of data lakes with the performance and reliability characteristics of data warehouses. This approach eliminates the traditional trade-offs between storing diverse data types and maintaining query performance for analytical workloads. +A data lakehouse is an architecture that unifies the scalability and cost-effectiveness of data lakes with the performance and reliability characteristics of data warehouses. This approach eliminates the traditional trade-offs between storing diverse data types and maintaining query performance for analytical workloads. @@ -17 +19 @@ A data lakehouse is an architectural pattern that unifies the scalability and co -The lakehouse architecture addresses the following key limitations of isolated systems: +The lakehouse architecture provides the following key benefits: @@ -23,2 +24,0 @@ The lakehouse architecture addresses the following key limitations of isolated s - * **Multi-format support** – Native handling of structured, semi-structured, and unstructured data - @@ -27 +27 @@ The lakehouse architecture addresses the following key limitations of isolated s - * **Open standards** – Vendor-neutral formats preventing data lock-in + * **Open standards** – Compatibility with Apache Iceberg open standard @@ -40,15 +39,0 @@ The lakehouse architecture addresses the following key limitations of isolated s -This architecture enables organizations to support business intelligence, advanced analytics, and machine learning workloads on the same data platform, reducing complexity and operational overhead while maintaining performance requirements for each use case. - -## Key Capabilities - -The lakehouse architecture of Amazon SageMaker provides the following key capabilities: - - * **Unified data access** – Query and access data across Amazon S3 data lakes, Amazon Redshift data warehouses, and other sources using [Apache Iceberg](https://iceberg.apache.org/) compatible tools and engines. This includes AWS services such as Amazon Athena, Amazon Redshift, Amazon EMR, Amazon SageMaker AI, as well as third-party engines, all of which you can use to query your data in-place. - - * **Integrated access control** – Fine-grained access control to your data with permissions that you can define and consistently apply across all analytics and ML tools and engines, regardless of the underlying storage formats or query engines used. - - * **Open source compatibility** – Leverages open-source [Apache Iceberg](https://iceberg.apache.org/), enabling data interoperability across various Apache Iceberg compatible query engines and tools. This gives you the flexibility to choose your preferred tools and engines. - - - -