Creating a Workspace

In this guide, you’ll learn all the essential steps to create a Paradime workspace, the foundation for your dbt™ development journey. A well-configured workspace is crucial for efficient collaboration and streamlined analytics engineering processes. Estimated completion time: 10 minutes

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What You’ll Learn

In this guide, you’ll learn how to:

1. Create a Paradime workspace
2. Connect a Git Repository
3. Add a Data warehouse connection

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Video Tutorial

The following video provides step-by-step instructions for creating a workspace, connecting a Git repository, and setting up a data warehouse connection:

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1. Creating a Paradime Workspace

To create a workspace in Paradime:

1. Navigate to your Platform settings.
2. Click the New Workspace button to begin setting up your workspace.
3. Provide a name for your workspace.
4. Choose whether you want other people in your organization to access it without an invite (with a business user role). This option makes the workspace visible in the workspaces list to users, even if they’re not yet part of it.

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2. Connect a Git Repository to the Workspace

Next, connect a Git repository to Paradime:

• This can be an empty repository or an existing dbt™ project.
• Upon adding a repository SSH URI, Paradime will generate a deploy key.
• Use this deploy key to grant Paradime write access to the repo, allowing users to create commits and push branches from the Paradime IDE.

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3. Add a Data Warehouse Connection

Finally, add a data warehouse connection during the workspace onboarding:

• Paradime supports many warehouse providers, including:
  • Snowflake
  • BigQuery
  • Redshift
  • Databricks
• Multiple authentication methods are available based on your needs.

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Summary

You’ve successfully created your Paradime workspace, which serves as the foundation for your dbt™ development. Here’s what you’ve accomplished:

1. Created a new workspace in Paradime
2. Connected a Git repository to your workspace
3. Set up an initial data warehouse connection

In the next guide, we’ll explore how to configure additional data warehouse connections for development, testing, and production environments.