Target solution environment
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      Microsoft Fabric - Target environment

      This article proposes a possible target environment for Microsoft Fabric DataVault and Microsoft Fabric DataVault and Mart generators.

      Installation and configuration of the target environment are not part of biGENIUS support.

      Unfortunately, we won't be able to provide any help beyond this example in this article.

      Many other configurations and installations are possible for a Microsoft Fabric target environment.

      Below is a possible target environment setup for a Microsoft Fabric generator.

      The Property Target Platform should be set to the Fabric value:

      Setup environment

      You should have access to a Microsoft Fabric target environment:

       

      To activate the git integration later, you should also have access to an Azure DevOps git repository. 

      Enter the Synapse Data Engineering section:

      The Synapse Data Engineering section is displayed:

      Create a Workspace

      Click on the Workspace entry in the left menu, then on + New workspace:

      In this example, we will create a Workspace named bgfabricdvdm.

      Fill in the name and click on the Apply button:

      Now click on the Workspace settings menu to configure the git integration:

      Choose the Git integration entry in the left menu:

      Connect to your Azure DevOps git repository, then click on the Connect and sync button:

      Source data

      There are two ways to provide source data to a Microsoft Fabric Data Vault or Data Vault and Mart generators:

      • From Parquet and Delta files by using the Microsoft Fabric Stage Files generator as a Linked Project
      • From any database accessed through JDBC by using the Microsoft Fabric Stage JDBC generator as a Linked Project

      Parquet and Delta Files

      If your source data are stored in Parquet files, please:

      • Create a first Project with the Microsoft Fabric Stage Files generator
      • In this first Project, discover the Parquet and the Delta files, create the Stage Model Object, generate, deploy, and load data in a Lake House.
      • Create a second Project with the Microsoft Fabric Data Vault or DataVault and Mart generators.
      • In this second Project, use the first Project Stage Model Object as a source by using the Linked Project feature.

      Database

      If your source data are stored in a database such as Microsoft SQL Server or Postgres (or any database you can access through JDBC), please:

      • Create a first Project with the Microsoft Fabric Stage JDBC generator
      • In this first Project, discover the database tables, create the Stage Model Object, generate, deploy, and load data in a Lake House.
      • Create a second Project with the Microsoft Fabric Data Vault or Microsoft Fabric DataVault and Mart generator
      • In this second Project, use the first Project Stage Model Object as a source by using the Linked Project feature.

      Upload Artifacts in Microsoft Fabric

      Please now upload the generated Artifacts from the biGENIUS-X application to the Microsoft Fabric Workspace.

      Please replace the placeholders before uploading the artifacts.

      • Click on the Workspace we just created in the left menu:
      • Click on the Import button and choose Notebook then From this computer:
      • Click on the Upload button:
      • Select all the generated artifacts from the folders Jupyter, Helpers, and LoadControl:
      • Import, in addition, the following helper:

      In the file 500_Deploy_and_Load_DataVault_Fabric.ipynb, adapt the name of the XXX_Deployment, the XXX_SimpleLoadexecution, the XXX_MultithreadingLoadExecution, and the XXX_SelectResults by the name of your Helper files.

      • Commit all the changes into your git repository by:
        • Clicking on the Source control menu:
        • Selecting all the changes:
        • Clicking on the Commit button:

       

      Depending on your load control environment:

       

      You're now ready to deploy these artifacts and subsequently load the data based on the Generator you are using with the following possible load controls: