AWS next-generation-sagemaker documentation change
Summary
Updated terminology from 'SageMaker lakehouse architecture' to 'SageMaker Lakehouse' and minor phrasing adjustments for consistency. No functional changes to security controls or procedures.
Security assessment
The changes are purely terminological (e.g., 'architecture' removed from product name) and editorial in nature. While the documentation discusses security features like access controls and permissions, these were already present in the original text. There is no evidence of addressing a specific security vulnerability or adding new security-related content.
Diff
diff --git a/next-generation-sagemaker/latest/userguide/lakehouse-athena-federated-queries.md b/next-generation-sagemaker/latest/userguide/lakehouse-athena-federated-queries.md index 80546b557..5ec390070 100644 --- a//next-generation-sagemaker/latest/userguide/lakehouse-athena-federated-queries.md +++ b//next-generation-sagemaker/latest/userguide/lakehouse-athena-federated-queries.md @@ -7 +7 @@ What you'll learnPrerequisitesStep 1: Set up federated catalogsStep 2: Set up fi -# Get started using SageMaker lakehouse architecture integrated access controls for Athena federated queries in Amazon SageMaker Unified Studio +# Get started with SageMaker Lakehouse integrated access controls for Athena federated queries in Amazon SageMaker Unified Studio @@ -20 +20 @@ Scaling data infrastructure creates challenges with data silos, fragmented acces -To address the challenges of data silos and fragmented access, [SageMaker lakehouse architecture](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse.html) with integrated access controls for [Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/what-is.html) (Athena) federated queries offers: +To address the challenges of data silos and fragmented access, [SageMaker Lakehouse](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse.html) with integrated access controls for [Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/what-is.html) (Athena) federated queries offers: @@ -35 +35 @@ To address the challenges of data silos and fragmented access, [SageMaker lakeho -SageMaker lakehouse architecture provides a unified environment for accessing, discovering, preparing, and analyzing data from various sources for machine learning (ML) and analytics workloads. Athena complements this as a serverless query service that analyzes data lake and federated data sources such as [Amazon DynamoDB](https://aws.amazon.com/dynamodb/) and PostgreSQL, through using SQL without extract, transform, and load (ETL) scripts. Federated connections in SageMaker lakehouse architecture establish secure links to external data sources, enabling access without data movement. [Federated catalogs](https://docs.aws.amazon.com/lake-formation/latest/dg/federated-catalog-data-connection.html) organize metadata about these connected data sources, making them discoverable and queryable through the SageMaker lakehouse architecture interface. Federated queries use these connections to run SQL statements across multiple data sources simultaneously, breaking down data silos for comprehensive analysis. +SageMaker Lakehouse provides a unified environment for accessing, discovering, preparing, and analyzing data from various sources for machine learning (ML) and analytics workloads. Athena complements this as a serverless query service that analyzes data lake and federated data sources such as [Amazon DynamoDB](https://aws.amazon.com/dynamodb/) and PostgreSQL, through using SQL without extract, transform, and load (ETL) scripts. Federated connections in SageMaker Lakehouse establish secure links to external data sources, enabling access without data movement. [Federated catalogs](https://docs.aws.amazon.com/lake-formation/latest/dg/federated-catalog-data-connection.html) organize metadata about these connected data sources, making them discoverable and queryable through the SageMaker Lakehouse interface. Federated queries use these connections to run SQL statements across multiple data sources simultaneously, breaking down data silos for comprehensive analysis. @@ -39 +39 @@ SageMaker lakehouse architecture provides a unified environment for accessing, d -This guide shows you how to use SageMaker lakehouse architecture with integrated access controls for Athena federated queries. In this guide, you create an environment where data analysts can discover and query data across sources while administrators maintain consistent governance and appropriate security controls. This guide includes the following steps: +This guide shows you how to use SageMaker Lakehouse with integrated access controls for Athena federated queries. In this guide, you create an environment where data analysts can discover and query data across sources while administrators maintain consistent governance and appropriate security controls. This guide includes the following steps: @@ -41 +41 @@ This guide shows you how to use SageMaker lakehouse architecture with integrated - 1. Set up federated connections between SageMaker lakehouse architecture and DynamoDB. + 1. Set up federated connections between SageMaker Lakehouse and DynamoDB. @@ -43 +43 @@ This guide shows you how to use SageMaker lakehouse architecture with integrated - * Create connections that serve as bridges between your SageMaker lakehouse architecture and external data sources. + * Create connections that serve as bridges between your SageMaker Lakehouse and external data sources. @@ -53 +53 @@ This guide shows you how to use SageMaker lakehouse architecture with integrated - * Access data from the connected data source within your SageMaker lakehouse architecture environment. + * Access data from the connected data source within your SageMaker Lakehouse environment. @@ -119 +119 @@ For more information about how to create a project in SageMaker Unified Studio, - * Administrator access to a data source. SageMaker lakehouse architecture connections support [several popular data sources](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-data-connection.html#lakehouse-data-connection-supported), such as Amazon DynamoDB, PostgreSQL, and [Amazon DocumentDB](https://aws.amazon.com/documentdb/). In this guide, we use DynamoDB as the data source. + * Administrator access to a data source. SageMaker Lakehouse connections support [several popular data sources](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-data-connection.html#lakehouse-data-connection-supported), such as Amazon DynamoDB, PostgreSQL, and [Amazon DocumentDB](https://aws.amazon.com/documentdb/). In this guide, we use DynamoDB as the data source. @@ -219 +219 @@ The first step is to set up federated catalogs for our data sources using an adm -SageMaker Unified Studio connects to the DynamoDB data source that you created in the prerequisites, registers the data source as a federated catalog with SageMaker lakehouse architecture, and displays it in your data explorer. The catalog references your DynamoDB data source. +SageMaker Unified Studio connects to the DynamoDB data source that you created in the prerequisites, registers the data source as a federated catalog with SageMaker Lakehouse, and displays it in your data explorer. The catalog references your DynamoDB data source. @@ -227 +227 @@ SageMaker Unified Studio connects to the DynamoDB data source that you created i - 3. Choose the SageMaker lakehouse architecture catalog that you just created to view its contents. Use the data explorer to drill down to a table and choose **Query with Athena**. + 3. Choose the SageMaker Lakehouse catalog that you just created to view its contents. Use the data explorer to drill down to a table and choose **Query with Athena**. @@ -243 +243 @@ Access to the data source in the SageMaker Unified Studio project is governed by -For more information about creating connections in SageMaker lakehouse architecture, see [Creating a connection in SageMaker lakehouse architecture](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-create-connection.html) in the _Amazon SageMaker Unified Studio User Guide_. For more information about creating catalogs, see [Creating a catalog](https://docs.aws.amazon.com/sagemaker-unified- studio/latest/userguide/lakehouse-create-catalog.html) in the _Amazon SageMaker Unified Studio User Guide_. +For more information about creating connections in SageMaker Lakehouse, see [Creating a connection in SageMaker Lakehouse](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-create-connection.html) in the _Amazon SageMaker Unified Studio User Guide_. For more information about creating catalogs, see [Creating a catalog](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-create-catalog.html) in the _Amazon SageMaker Unified Studio User Guide_. @@ -247 +247 @@ For more information about creating connections in SageMaker lakehouse architect -Security is a critical aspect of data access. SageMaker lakehouse architecture provides integrated access controls that work with federated queries in Athena to ensure proper governance. You can manage permissions at the catalog, database, and table levels. Administrators can apply access controls at different levels of granularity to ensure sensitive data remains protected while expanding data access. +Security is a critical aspect of data access. SageMaker Lakehouse provides integrated access controls that work with federated queries in Athena to ensure proper governance. You can manage permissions at the catalog, database, and table levels. Administrators can apply access controls at different levels of granularity to ensure sensitive data remains protected while expanding data access. @@ -323 +323 @@ For example, if you wish to restrict access to a sensitive column containing the -In this example, we demonstrate how to set up a basic column-level filter to restrict access to sensitive data. However, SageMaker lakehouse architecture supports a broad range of fine-grained access control scenarios beyond column filters that allow you to meet complex security and compliance requirements across diverse data sources. For more information about managing permissions on catalogs, see [Adding existing databases and catalogs using AWS Lake Formation permissions](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-add-catalog.html) in the _Amazon SageMaker Unified Studio User Guide_ and [Managing Lake Formation Permissions](https://docs.aws.amazon.com/lake-formation/latest/dg/managing-permissions.html) in the _AWS Lake Formation Developer Guide_. +In this example, we demonstrate how to set up a basic column-level filter to restrict access to sensitive data. However, SageMaker Lakehouse supports a broad range of fine-grained access control scenarios beyond column filters that allow you to meet complex security and compliance requirements across diverse data sources. For more information about managing permissions on catalogs, see [Adding existing databases and catalogs using AWS Lake Formation permissions](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-add-catalog.html) in the _Amazon SageMaker Unified Studio User Guide_ and [Managing Lake Formation Permissions](https://docs.aws.amazon.com/lake-formation/latest/dg/managing-permissions.html) in the _AWS Lake Formation Developer Guide_. @@ -366 +366 @@ Note how permissions are working as expected because the query result doesn't in -Make sure you remove the SageMaker lakehouse architecture resources to mitigate any unexpected costs. Delete the following resources: +Make sure you remove the SageMaker Lakehouse resources to mitigate any unexpected costs. Delete the following resources: @@ -370 +370 @@ Make sure you remove the SageMaker lakehouse architecture resources to mitigate -Specifically, choose your project from SageMaker Unified Studio. Choose **Data** in the navigation pane. Choose the SageMaker lakehouse architecture catalog that you created in Step 1. Choose the **Actions** menu and choose **Remove**. Type "**Confirm** " and choose **Remove connection**. +Specifically, choose your project from SageMaker Unified Studio. Choose **Data** in the navigation pane. Choose the SageMaker Lakehouse catalog that you created in Step 1. Choose the **Actions** menu and choose **Remove**. Type "**Confirm** " and choose **Remove connection**. @@ -383 +383 @@ Specifically, choose your project from SageMaker Unified Studio. Choose **Data** -Now that you've successfully set up SageMaker lakehouse architecture integrated access controls for Athena federated queries, consider these next steps to further enhance your data governance and analytics capabilities: +Now that you've successfully set up SageMaker Lakehouse integrated access controls for Athena federated queries, consider these next steps to further enhance your data governance and analytics capabilities: @@ -398 +398 @@ Now that you've successfully set up SageMaker lakehouse architecture integrated -This integration between SageMaker lakehouse architecture and Athena federated queries provides significant benefits for organizations with diverse data ecosystems. Data scientists can now analyze customer behavior by combining transaction data from PostgreSQL with clickstream data in [Amazon S3](https://aws.amazon.com/s3/). Financial analysts can query historical market data alongside real-time trading information without complex ETL processes. Healthcare researchers can analyze patient records stored in different systems while maintaining compliance with privacy regulations. +This integration between SageMaker Lakehouse and Athena federated queries provides significant benefits for organizations with diverse data ecosystems. Data scientists can now analyze customer behavior by combining transaction data from PostgreSQL with clickstream data in [Amazon S3](https://aws.amazon.com/s3/). Financial analysts can query historical market data alongside real-time trading information without complex ETL processes. Healthcare researchers can analyze patient records stored in different systems while maintaining compliance with privacy regulations. @@ -400 +400 @@ This integration between SageMaker lakehouse architecture and Athena federated q -For more information about federated queries in Athena and the data sources that support fine-grained access controls, see [Register your connection as a Glue Data Catalog](https://docs.aws.amazon.com/athena/latest/ug/register-connection-as-gdc.html) in the _Athena User Guide_. For more information about extending your SageMaker lakehouse architecture environment, see [Add Data to SageMaker lakehouse architecture](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-add-data.html) and [Publishing Data](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-publish.html) in the _Amazon SageMaker Unified Studio User Guide_. For more information about specific use cases and implementation examples, see [Simplify data access for your enterprise using SageMaker lakehouse architecture](https://aws.amazon.com/blogs/big-data/simplify-data-access-for-your-enterprise-using-amazon-sagemaker-lakehouse/), [Simplify analytics and AI/ML with new SageMaker lakehouse architecture](https://aws.amazon.com/blogs/aws/simplify-analytics-and-aiml-with-new-amazon-sagemaker-lakehouse/), and [Catalog and govern Amazon Athena federated queries with SageMaker lakehouse architecture](https://aws.amazon.com/blogs/big-data/catalog-and-govern-amazon-athena-federated-queries-with-amazon-sagemaker-lakehouse/) in the _AWS Blog posts_. +For more information about federated queries in Athena and the data sources that support fine-grained access controls, see [Register your connection as a Glue Data Catalog](https://docs.aws.amazon.com/athena/latest/ug/register-connection-as-gdc.html) in the _Athena User Guide_. For more information about extending your SageMaker Lakehouse environment, see [Add Data to SageMaker Lakehouse](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-add-data.html) and [Publishing Data](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/lakehouse-publish.html) in the _Amazon SageMaker Unified Studio User Guide_. For more information about specific use cases and implementation examples, see [Simplify data access for your enterprise using SageMaker Lakehouse](https://aws.amazon.com/blogs/big-data/simplify-data-access-for-your-enterprise-using-amazon-sagemaker-lakehouse/), [Simplify analytics and AI/ML with new SageMaker Lakehouse](https://aws.amazon.com/blogs/aws/simplify-analytics-and-aiml-with-new-amazon-sagemaker-lakehouse/), and [Catalog and govern Amazon Athena federated queries with SageMaker Lakehouse](https://aws.amazon.com/blogs/big-data/catalog-and-govern-amazon-athena-federated-queries-with-amazon-sagemaker-lakehouse/) in the _AWS Blog posts_.