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Databricks Unity - Target environment

This article proposes a possible target environment on Databricks using the Unity Catalog.

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 Databricks target environment.

Below is a possible target environment setup for the Databricks generator using the Unity catalog.

The Unity Catalog Property must be set to true:

Setup environment

The Databricks target environment needs at least the following Azure resources set in your Azure Subscription:

  • A Ressource Group: some help is available here
  • Inside the Ressource Group:
    • 2 Storage Accounts: some help is available here
      • 1 for the Source data (if you are using files as source)
      • 1 for the Target Data Lake
    • An Azure Databricks Service some help is available here
    • An Access Connector for Azure Databricks: some help is available here

In the following example of the target environment, we will use the following resources:

  • A Ressource Group named bg-databricks
  • A Storage Account for our Source Data named bgdatabrickslandingzone1
  • A Storage Account for our Target Data Lake named bgdatabricksdatalake1
  • An Azure Databricks Service named bgaas-unity
  • An Access Connector for Azure Databricks named bgaasunity

Tools to install

Please install the following tools:

  • Azure Storage Explorer: available here

Target Storage Account

We have chosen to create folders named silver and gold in our Target Storage Account:

  • Open Azure Storage Explorer
  • Connect to your Subscription
  • Open the Target Storage Account
  • Create the folders 

For this example, we have 2 Target folders for our Data Lake:

Source data

There are three ways to provide source data to a Databricks generator:

  • From Parquet and Delta files that exist in an Unity Catalog by using a direct Discovery
  • From Parquet and Delta files by using the Databricks Stage Files generator as a Linked Project
  • From any database accessed through JDBC by using the Databricks Stage JDBC generator as a Linked Project

Parquet and Delta Files

If your source data is stored in Parquet or Delta files, please:

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

 

Please upload the source Parquet files to the Source Storage Account:

  • Open Azure Storage Explorer
  • Connect to your Subscription
  • Open the Source Storage Account
  • Create one folder by Parquet Source file

The folder name should be identical to the Parquet file name.

For this example, we have 2 Parquet source files, so we need 2 folders:

Upload in each folder the corresponding Parquet Source file, for example, for the SalesOrderDetail folder:

We have the following Delta File containing Credit Card data in our target storage account named bgdatabrickslandingzone1:

Database

If your source data is 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 Databricks Stage JDBC generator
  • In this first Project, discover the database tables, create the Stage Model Object, generate, deploy, and load data in the target storage account.
  • Create a second Project with the Databricks 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.

 

The source data are coming from a JDBC source.

In this example, we will use a Microsoft SQL Server database stored in Azure in a dedicated resource group:

The Azure database is AdventureWorks2019 and contains the data from the SQL Server sample database AdventureWorks2019.

To be able to access the Microsoft SQL Server from Databricks, you should check the box Allow Azure services and resources to access this server in the Server Networking configuration:

Unity Catalog

We must prepare the database and schemas in our Unity catalog.

For this example, we created:

  • A database named datalakehouse
  • A schema per layer for each Project (ex.: docu_rawvault, docu_businessvault, and docu_mart for a Data Vault Project and docu_stage for a Stage File Linked Project)

Upload Artifacts in Databricks

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

Please replace the placeholders before uploading the artifacts.

  • Click on the Azure Databricks Service in Azure:
  • Then click on the URL:
  • Databricks is opened:
  • Click on the Workspace menu on the left-hand-side:
  • Expand the folders Workspace > Users and click on your user:
  • We have chosen to create a folder named artifacts_dv_dm

  • Import all the generated artifacts from the folder Jupyter, Helpers, and LoadControl:

It is possible to have one or several files not imported as:

It is due to Databricks itself.

Just restart the import for the relevant files, and it should work.

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

    Create a Personal Compute

    To be able to execute the Notebooks from our generated artifacts, please create a Personal Compute in Databricks:

    • Click on the Compute menu on the left-hand-side:
    • Click on the Create with Personal Compute button:
    • Change the following information:
      • Databricks runtime version: choose "13.3 LTS (Scala 2.12, Spark 3.4.1)
    • Click on the Create compute button:
    • Wait until the Personal Compute is available:

     

    If you have already discovered your source data, modeled your project, and generated the artifacts, you're now ready to replace the placeholders in your generated artifacts, deploy these artifacts, and subsequently load the data based on the Generator you are using with the following possible load controls: