AWS AmazonRDS documentation change
Summary
Updated terminology from 'Amazon SageMaker AI lakehouse' to 'Amazon SageMaker Lakehouse' across multiple sections
Security assessment
The changes are purely branding/naming convention updates with no security implications mentioned. No vulnerabilities or security features are addressed.
Diff
diff --git a/AmazonRDS/latest/UserGuide/zero-etl.setting-up.md b/AmazonRDS/latest/UserGuide/zero-etl.setting-up.md index 6afd29aa6..350715880 100644 --- a//AmazonRDS/latest/UserGuide/zero-etl.setting-up.md +++ b//AmazonRDS/latest/UserGuide/zero-etl.setting-up.md @@ -5 +5 @@ -Step 1: Create a custom DB parameter groupStep 2: Select or create a source databaseStep 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 parameter groupStep 2: Select or create a source databaseStep 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 RDS database and your d - 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. @@ -30 +30 @@ 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. @@ -321 +321 @@ Within the script, optionally modify the names of the source, target, and parame -## 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 @@ -323 +323 @@ Within the script, optionally modify the names of the source, target, and parame -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. @@ -475 +475 @@ JSON -With a source RDS database 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 Amazon RDS zero-ETL integrations with Amazon Redshift](./zero-etl.creating.html). +With a source RDS database 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 Amazon RDS zero-ETL integrations with Amazon Redshift](./zero-etl.creating.html).