![]() ![]() This builds out your folder structure with example models. Above the file tree to the left, click Initialize dbt project.It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse. Now that you have a repository configured, you can initialize your project and start development in dbt Cloud: Initialize your dbt project and start developing Once you see the "Successfully imported repository," click Continue.It will take a few seconds for your repository to be created and imported. Type a name for your repo such as bbaggins-dbt-quickstart.Under "Setup a repository", select Managed.In the long run, it's better to connect to a supported git provider to use features like automation and continuous integration. Managed repositories are a great way to trial dbt without needing to create a new repository. To connect to a repository, you can either set up a dbt Cloud-hosted managed repository or directly connect to a supported git provider. 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. Click on the name of the bucket S3 bucket.The bucket will be prefixed with dbt-data-lake. There will be sample data in the bucket already, feel free to ignore it or use it for other modeling exploration. Go to the search bar at the top and type in S3 and click on S3. Now we are going to use the S3 bucket that you created with CloudFormation and upload the files. Download these to your computer to use in the following steps. You can use the following URLs to download these files. The data used in this course is stored as CSVs in a public S3 bucket. S3 buckets are simple and inexpensive way to store data outside of Redshift. Now we are going to load our sample data into the S3 bucket that our Cloudformation template created. Redshift Query Editor v2 Connect to Redshift Cluster Password - Use the autogenerated RSadminpassword from the output of the stack and save it for later.Authentication - Use the default which is Database user name and password (NOTE: IAM authentication is not supported in dbt Cloud).In the Connect to popup, fill out the credentials from the output of the stack: ![]() For this sandbox environment, we recommend selecting “Configure account”. We will be using the v2 version for the purpose of this guide. You can choose the classic query editor or v2. The cluster name should begin with dbtredshiftcluster. Click on RedshiftĬonfirm that your new Redshift cluster is listed in Cluster overview. Type Redshift in the search bar at the top and click Amazon Redshift. Save those credentials for later by keeping this open in a tab. When the stack status changes to CREATE_COMPLETE, click the Outputs tab on the top to view information that you will use throughout the rest of this guide. You should land on the stack page with a CREATE_IN_PROGRESS status. Select I acknowledge that AWS CloudFormation might create IAM resources with custom names and click Create Stack. Start a CloudFormation stack and you can refer to the create-dbtworkshop-infr JSON file for more template details.Ĭlick Next for each page until you reach the Select acknowledgement checkbox. A CloudFormation template is a configuration file that automatically spins up the necessary resources in AWS. Use a CloudFormation template to quickly set up a Redshift cluster.Sign in to your AWS account as a root user or an IAM user depending on your level of access. ![]()
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