![]() When you develop in dbt Cloud, you can leverage Git to version control your code. You should see these schemas listed under dbtworkshop. If you are on the Classic Query Editor, you might need to input them separately into the UI. You can highlight the statement and then click on Run to run them individually. In your query editor, execute this query below to create the schemas that we will be placing your raw data into. ![]() Search for Redshift in the search bar, choose your cluster, and select Query data. Now let’s go back to the Redshift query editor. It should look like this: s3://dbt-data-lake-xxxx. Remember the name of the S3 bucket for later. Drag the three files into the UI and click the Upload button. If you have multiple S3 buckets, this will be the bucket that was listed under “Workshopbucket” on the Outputs page.
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