AWS sagemaker-lakehouse-architecture documentation change
Summary
Added new sections: 'Supported data sources', 'Using connections', and 'Understanding created AWS resources'. Expanded documentation about data source connections and their AWS resource implications.
Security assessment
While the changes document connection management and AWS resource creation, they don't specifically address security vulnerabilities or add security-focused documentation. The note about lowercase identifiers is a compatibility requirement rather than a security feature.
Diff
diff --git a/sagemaker-lakehouse-architecture/latest/userguide/lakehouse-data-connection.md b/sagemaker-lakehouse-architecture/latest/userguide/lakehouse-data-connection.md index 28c26c0fb..f07ddb479 100644 --- a//sagemaker-lakehouse-architecture/latest/userguide/lakehouse-data-connection.md +++ b//sagemaker-lakehouse-architecture/latest/userguide/lakehouse-data-connection.md @@ -5 +5 @@ -Capabilities +CapabilitiesSupported data sourcesUsing The lakehouse architecture connectionsUnderstanding created AWS resources @@ -7 +7 @@ Capabilities -# Data connections in Amazon SageMaker lakehouse architecture +# Data connections in The lakehouse architecture of Amazon SageMaker @@ -9 +9 @@ Capabilities -SageMaker lakehouse architecture provides a unified approach to managing data connections across AWS services and enterprise applications. These connections provide a consistent experience for creating, testing, and exploring data sources, regardless of the underlying data platform. +The lakehouse architecture provides a unified approach to managing data connections across AWS services and enterprise applications. These connections provide a consistent experience for creating, testing, and exploring data sources, regardless of the underlying data platform. @@ -13 +13 @@ SageMaker lakehouse architecture provides a unified approach to managing data co -With SageMaker lakehouse architecture connections, you can do the following: +With The lakehouse architecture connections, you can do the following: @@ -31,0 +32,56 @@ With SageMaker lakehouse architecture connections, you can do the following: +## Supported data sources + +The lakehouse architecture connections support several popular data sources, including the following: + +Supported Data Sources Data Source | Type +---|--- +Google BigQuery | Database +Amazon DocumentDB | Database +Amazon DynamoDB | Database +Amazon Redshift | Database +MySQL | Database +PostgreSQL | Database +SQL Server | Database +Snowflake | Database +Oracle | Database + +###### Note + +The lakehouse architecture currently supports lowercase table, column, and database names. For optimal experience in The lakehouse architecture, ensure that all database identifiers are in lowercase. + +## Using The lakehouse architecture connections + +After you've created an The lakehouse architecture connection, you can use it in various AWS services: + + * The lakehouse architecture : Browse metadata, preview sample data, and run SQL queries against the connected data. + + * AWS Glue: Use the connection for ETL jobs and crawlers. + + * Amazon Athena: Query data directly using Athena's federated query capabilities. For more information, see [Register federated catalogs in Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/gdc-register-federated.html). + + * Amazon SageMaker AI: Access data for building machine learning models. + + + + +## Understanding created AWS resources + +When you create a connection in The lakehouse architecture, several resources are created in your AWS account(s) behind the scenes. These resources can include: + + * AWS Glue connection - A connection object is created in the AWS Glue crawler. This stores the core connection information and is used by various AWS services. + + * Athena data catalog - For connections that will be used with Athena , an Athena data catalog is created. This allows Athena to query the external data source. + + * AWS Glue data catalog entries - Databases, tables, and schemas from your external data source are registered in the Data Catalog. This enables AWS services to understand the structure of your external data. + + * Lambda (for Athena Federated Query) - For some data sources, a Lambda function is created to facilitate federated queries. This function acts as a bridge between Athena and the external data source. + + + + +To view these resources, access the respective AWS service consoles (AWS Glue, Athena, IAM, etc.) in the AWS account associated with your The lakehouse architecture project. + +In these consoles, look for resources with names that include your The lakehouse architecture project ID or connection name. + +For more information about how to create a data connection and explore a connected data sourc e, see [Adding data sources in The lakehouse architecture](./lakehouse-add-data.html). +