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How to create a Data Factory in Azure?

In this article, we are going to learn the steps of creating a Data Factory in Azure that can be done through the Azure Portal.

  • Log into Azure Portal: Go to Azure Portal and sign in with your Azure account credentials.
  • Navigate to Azure Data Factory: On the Azure Portal dashboard, select “Create a resource”. In the search box, type "Data Factory" and select it from the results.
  • Initiate Creation: Click on “Create” to start the process of creating a new Data Factory.
  • Fill in the Basic Details:
    • Subscription: Select the Azure subscription in which you want to create the Data Factory.
    • Resource Group: Choose an existing resource group or create a new one. A resource group is a container that holds related resources for an Azure solution.
    • Name: Enter a globally unique name for your Data Factory.
    • Version: Choose V2, as it's the latest version with more features and capabilities.
    • Region: Select the region closest to your users or where your data resides to minimize latency.
    • Git Configuration: Optionally, you can configure Git for source control. This step allows you to associate your Data Factory with a Git repository for version control, collaboration, and development lifecycle management.
    • Networking: You can also configure networking settings for your Data Factory, such as setting up a private endpoint for secure connectivity.
    • Review: Check all the details you've entered. Make sure everything is correct as per your requirements.
    • Create: Click the “Create” button. Azure will then validate your configuration and deploy a new Data Factory instance.
  • Access and Use the Data Factory
    • Navigate to Your Data Factory: Once the deployment is complete, go to the newly created Data Factory resource.
    • Open ADF Studio: To start using Data Factory, click on the “Author & Monitor” button in the Data Factory overview page. This will open the Azure Data Factory Studio, where you can create and manage pipelines, datasets, linked services, and more.
  • Best Practices
    • Naming Conventions: Use clear and consistent naming conventions for ease of management, especially if you will have multiple Data Factories.
    • Resource Organization: Keep your Data Factory within a relevant resource group along with related Azure resources for better organization and management.
    • Security and Compliance: Consider implementing security measures like managed identities, Azure role-based access control (RBAC), and ensuring compliance with your organization's policies.

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