AWS AmazonRDS documentation change
Summary
Updated documentation to replace 'Amazon SageMaker AI' references with 'Amazon SageMaker Lakehouse' terminology across multiple sections
Security assessment
Change involves branding/terminology updates from 'AI' to 'Lakehouse' with no security-related content modifications. Impacts documentation clarity but not security controls.
Diff
diff --git a/AmazonRDS/latest/AuroraUserGuide/zero-etl.setting-up.md b/AmazonRDS/latest/AuroraUserGuide/zero-etl.setting-up.md index 97f668616..503534150 100644 --- a//AmazonRDS/latest/AuroraUserGuide/zero-etl.setting-up.md +++ b//AmazonRDS/latest/AuroraUserGuide/zero-etl.setting-up.md @@ -5 +5 @@ -Step 1: Create a custom DB cluster parameter groupStep 2: Select or create a source DB clusterStep 3a: Create a target data warehouseSet up an integration using the AWS SDKsStep 3b: Create an AWS Glue catalog for Amazon SageMaker AI zero-ETL integrationNext steps +Step 1: Create a custom DB cluster parameter groupStep 2: Select or create a source DB clusterStep 3a: Create a target data warehouseSet up an integration using the AWS SDKsStep 3b: Create an AWS Glue catalog for Amazon SageMaker Lakehouse zero-ETL integrationNext steps @@ -15 +15 @@ Before you create a zero-ETL integration, configure your Aurora DB cluster and y - 3. Create a target data warehouse for Amazon Redshift or Create a target Amazon SageMaker AI lakehouse. + 3. Create a target data warehouse for Amazon Redshift or Create a target Amazon SageMaker Lakehouse. @@ -32 +32 @@ For Step 3, you can choose to create either a target data warehouse (Step 3a) or - * Choose an Amazon SageMaker AI lakehouse if you need machine learning capabilities and want to use lakehouse features for data science and ML workflows. + * Choose an Amazon SageMaker Lakehouse if you need machine learning capabilities and want to use lakehouse features for data science and ML workflows. @@ -551 +551 @@ Aurora PostgreSQL -## Step 3b: Create an AWS Glue catalog for Amazon SageMaker AI zero-ETL integration +## Step 3b: Create an AWS Glue catalog for Amazon SageMaker Lakehouse zero-ETL integration @@ -553 +553 @@ Aurora PostgreSQL -When creating a zero-ETL integration with an Amazon SageMaker AI lakehouse, you must create an AWS Glue managed catalog in AWS Lake Formation. The target catalog must be an Amazon Redshift managed catalog. To create an Amazon Redshift managed catalog, first create the `AWSServiceRoleForRedshift` service-linked role. In the Lake Formation console, add the `AWSServiceRoleForRedshift` as a read-only administrator. +When creating a zero-ETL integration with an Amazon SageMaker Lakehouse, you must create an AWS Glue managed catalog in AWS Lake Formation. The target catalog must be an Amazon Redshift managed catalog. To create an Amazon Redshift managed catalog, first create the `AWSServiceRoleForRedshift` service-linked role. In the Lake Formation console, add the `AWSServiceRoleForRedshift` as a read-only administrator. @@ -705 +705 @@ JSON -With a source Aurora DB cluster and either an Amazon Redshift target data warehouse or Amazon SageMaker AI lakehouse, you can create a zero-ETL integration and replicate data. For instructions, see [Creating Aurora zero-ETL integrations with Amazon Redshift](./zero-etl.creating.html). +With a source Aurora DB cluster and either an Amazon Redshift target data warehouse or Amazon SageMaker Lakehouse, you can create a zero-ETL integration and replicate data. For instructions, see [Creating Aurora zero-ETL integrations with Amazon Redshift](./zero-etl.creating.html).