![]() ![]() We will be using the synthetic credit card transaction data to create tables in our database. Create a SQL editor tab and be sure the sagemaker database is selected. Let’s populate this database with tables for the RStudio user to query. The CloudFormation script created a database called sagemaker. ![]() Loading data in Amazon Redshift Serverless Note: The pattern demonstrated in this blog integrating Amazon Redshift and RStudio on Amazon SageMaker will be the same regardless of Amazon Redshift deployment pattern (serverless or traditional cluster). There is no need to set up and manage clusters. This is a new capability that makes it super easy to run analytics in the cloud with high performance at any scale. Once the stack status is CREATE_COMPLETE, navigate to the Amazon Redshift Serverless console.
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