--- title: Databricks - Target environment description: This article proposes a possible target environment on Databricks. 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 Hive catalog. 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[Target solution environment](https://knowledge.bigenius-x.com/generators?hsLang=en#target-solution-environment) # Databricks - Target environment This article proposes a **possible target environment on Databricks**. 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 Hive catalog. The **[*Unity Catalog*](https://knowledge.bigenius-x.com/unity-catalog-project?hsLang=en) Property** must be set to false: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Sep-09-2024-08-30-20-1228-AM.png?width=670&height=45&name=image-png-Sep-09-2024-08-30-20-1228-AM.png) ### 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](https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/manage-resource-groups-portal) - Inside the Ressource Group: - 2 **Storage Accounts**: some help is available [here](https://learn.microsoft.com/en-us/azure/synapse-analytics/security/connect-to-a-secure-storage-account) - 1 for the Source data (if you have files as source) - 1 for the Target Data Lake - An **Azure Databricks Service** some help is available [here](https://learn.microsoft.com/en-us/azure/databricks/scenarios/databricks-extract-load-sql-data-warehouse?toc=%2Fazure%2Fsynapse-analytics%2Ftoc.json#create-an-azure-databricks-service) 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-testing* ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-11-54-43-4038-AM.png?width=670&height=211&name=image-png-Oct-19-2023-11-54-43-4038-AM.png) #### Tools to install Please install the following tools: - **Azure Storage Explorer**: available [here](https://azure.microsoft.com/en-us/products/storage/storage-explorer/) #### Target Storage Account We have chosen to create a folder named *docu-datalake* in our Target Storage Account: - Open Azure Storage Explorer - Connect to your Subscription - Open the Target Storage Account - Create a folder For this example, we have 1 Target folder for our Data Lake: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Sep-06-2024-12-23-40-1379-PM.png?width=670&height=302&name=image-png-Sep-06-2024-12-23-40-1379-PM.png) ### Source data There are three ways to provide source data to a Databricks generator: - From Parquet and Delta files that exist in a **Unity Catalog** by using a direct Discovery - In a **dedicated Project** using the **Generator Configuration** Databricks Data Store, Data Vault, Data Vault and Mart, Dimensional, Dimensional and Mart, or Data Mart - From **Parquet and Delta files** by using the **Databricks Stage Files** generator as a Linked Project - In a **dedicated Project** using the **Generator Configuration** Databricks Stage Files - From any **database** accessed through JDBC by using the **Databricks Stage JDBC** generator as a Linked Project - In a **dedicated Project** using the **Generator Configuration** Databricks Stage JDBC ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-May-27-2025-09-01-14-4497-AM.png?width=670&height=375&name=image-png-May-27-2025-09-01-14-4497-AM.png) #### 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 Store, Data Vault, Data Vault and Mart, Dimensional, Dimensional and Mart, or Data Mart generator*. - 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: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Jun-05-2024-09-23-49-7786-AM.png?width=670&height=196&name=image-png-Jun-05-2024-09-23-49-7786-AM.png)Upload in each folder the corresponding Parquet Source file, for example, for the *SalesOrderDetail* folder: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Jun-05-2024-09-24-46-9908-AM.png?width=670&height=219&name=image-png-Jun-05-2024-09-24-46-9908-AM.png) We have the following Delta File containing Credit Card data in our target storage account named *bgdatabrickslandingzone1*: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-May-06-2024-01-05-52-6636-PM.png?width=670&height=118&name=image-png-May-06-2024-01-05-52-6636-PM.png) #### 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 Store, Data Vault, Data Vault and Mart, Dimensional, Dimensional and Mart, or Data Mart generator*. - In this second Project, use the first Project *Stage* Model Object as a source by using the [Linked Project](https://knowledge.bigenius-x.com/manage-linked-projects?hsLang=en)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: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-21-2024-03-32-31-5237-PM.png?width=670&height=562&name=image-png-Feb-21-2024-03-32-31-5237-PM.png) 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: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Feb-22-2024-08-57-29-5471-AM.png?width=389&height=77&name=image-png-Feb-22-2024-08-57-29-5471-AM.png) ### Create a Compute Cluster To be able to execute the Notebooks from our generated artifacts, please create a Compute Cluster in Databricks: - Click on the Compute menu on the left-hand side: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-39-29-4012-PM.png?width=147&height=43&name=image-png-Oct-19-2023-12-39-29-4012-PM.png) - Click on the Create compute button: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-40-01-1819-PM.png?width=167&height=54&name=image-png-Oct-19-2023-12-40-01-1819-PM.png) - Change the following information: - Check "Single node" - Databricks runtime version: choose "13.3 LTS (Scala 2.12, Spark 3.4.1) - Node type: choose "Standard\_DS3\_v2" - In Advanced Options, Spark, insert all the needed config to authorize your cluster to access the Source and Target Storages Accounts. Following is an example in our environment (should not work the same in your environment): - Replace *\* by the name of your scope storing the secret - Replace *\* by your secret's name ``` fs.azure.account.auth.type.bfmsampledata.dfs.core.windows.net OAuthfs.azure.account.oauth.provider.type.bfmsampledata.dfs.core.windows.net org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProviderfs.azure.account.oauth2.client.secret.bgdatabricksdatalake1.dfs.core.windows.net fs.azure.account.oauth.provider.type.bgdatabricksdatalake1.dfs.core.windows.net org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProviderfs.azure.account.auth.type.bgdatabricksdatalake1.dfs.core.windows.net OAuthspark.hadoop.fs.azure.account.oauth2.client.secret spark.hadoop.fs.azure.account.oauth2.client.endpoint fs.azure.account.oauth.provider.type.bgdatabrickslandingzone1.dfs.core.windows.net org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProviderfs.azure.account.auth.type.bgdatabrickslandingzone1.dfs.core.windows.net OAuthfs.azure.account.oauth2.client.secret.bgdatabrickslandingzone1.dfs.core.windows.net spark.master local[*, 4]fs.azure.account.oauth2.client.id.bfmsampledata.dfs.core.windows.net spark.databricks.cluster.profile singleNodespark.hadoop.fs.azure.account.oauth2.client.id spark.hadoop.fs.azure.account.auth.type OAuthfs.azure.account.oauth2.client.endpoint.bgdatabrickslandingzone1.dfs.core.windows.net spark.hadoop.fs.azure.account.oauth.provider.type org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProviderfs.azure.account.oauth2.client.endpoint.bfmsampledata.dfs.core.windows.net fs.azure.account.oauth2.client.id.bgdatabricksdatalake1.dfs.core.windows.net fs.azure.account.oauth2.client.id.bgdatabrickslandingzone1.dfs.core.windows.net fs.azure.account.oauth2.client.secret.bfmsampledata.dfs.core.windows.net fs.azure.account.oauth2.client.endpoint.bgdatabricksdatalake1.dfs.core.windows.net ```   - If you want to **activate the logging**: - Download the following script: [log4j\_init\_script.sh](https://f926da42ec3845beb4acf72f.blob.core.windows.net/hubspotknowledgebase/log4j_init_script.sh) - Import this file in your Databricks workspace (near the artifacts folder) - In Advanced Options, Init Scripts, select this file from your workspace: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Jan-11-2024-10-10-43-6902-AM.png?width=670&height=136&name=image-png-Jan-11-2024-10-10-43-6902-AM.png) - It is then considered on cluster bootup. - Click on the Create compute button: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-40-01-1819-PM.png?width=167&height=54&name=image-png-Oct-19-2023-12-40-01-1819-PM.png) - Wait until the Cluster is available: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-45-01-2796-PM.png?width=670&height=119&name=image-png-Oct-19-2023-12-45-01-2796-PM.png) ### Upload Artifacts in Databricks If your source data is coming from a JDBC source, you should **add the source schema name before the source Model Object Name** in biGENIUS-X, Dataflow Modeling overview.  Transform *CreditCard* in *Sales.CreditCard,* for example. There are 2 possibilities to upload the generated artifacts: - [Using the *deploy.ps1* helper](https://knowledge.bigenius-x.com/databricks-target-environment#upload-with-the-deploy.ps1-helper) (recommended) - [Manually](https://knowledge.bigenius-x.com/databricks-target-environment#manual-upload) Please [replace the placeholders](https://knowledge.bigenius-x.com/generators?hsLang=en#replace-placeholders) before uploading the artifacts. #### Upload with the deploy.ps1 helper To use the deploy.ps1 helper to upload the generated artifacts: - Install the [Databricks CLI](https://docs.databricks.com/aws/en/dev-tools/cli/install) - You will need the following information: - Profile: choose a Databricks CLI profile name - Workspace path:  - Depending on the location you want to upload the artifacts - For example, we want to deploy them in an Artifacts folder inside our Databricks user - To have the path, open the Artifacts folder, click on the 3 dots icon and choose the option Copy URL/path \> Full path ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-01-2025-12-04-32-1200-PM.png?width=670&height=166&name=image-png-Oct-01-2025-12-04-32-1200-PM.png) - Cluster name: compute name you created in the previous chapter - Host: URL to access your Databricks as https://XXXX.cloud.databricks.com - Open Powershell and execute the deploy.ps1 script: - Enter the profile name: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-01-2025-12-09-15-0933-PM.png?width=670&height=91&name=image-png-Oct-01-2025-12-09-15-0933-PM.png) - - Enter the workspace path: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-01-2025-12-08-17-5069-PM.png?width=670&height=117&name=image-png-Oct-01-2025-12-08-17-5069-PM.png) - Enter the cluster name: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-01-2025-12-10-17-7900-PM.png?width=670&height=136&name=image-png-Oct-01-2025-12-10-17-7900-PM.png) - If you never used the profile, enter the host: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-01-2025-12-11-42-5843-PM.png?width=670&height=200&name=image-png-Oct-01-2025-12-11-42-5843-PM.png) - You will be authenticated in the Browser: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-01-2025-12-12-01-6680-PM.png?width=243&height=193&name=image-png-Oct-01-2025-12-12-01-6680-PM.png) - All the artifacts are uploaded: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-01-2025-12-13-17-3475-PM.png?width=670&height=100&name=image-png-Oct-01-2025-12-13-17-3475-PM.png) #### Manual upload To manually upload the generated Artifacts from the biGENIUS-X application to the Databricks Workspace: - Click on the Azure Databricks Service in Azure: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-11-58-51-8752-AM.png?width=670&height=211&name=image-png-Oct-19-2023-11-58-51-8752-AM.png) - Then click on the URL: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-00-17-8683-PM.png?width=670&height=171&name=image-png-Oct-19-2023-12-00-17-8683-PM.png) - Databricks is opened: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-01-08-4001-PM.png?width=670&height=227&name=image-png-Oct-19-2023-12-01-08-4001-PM.png) - Click on the Workspace menu on the left-hand side: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-01-23-3008-PM.png?width=151&height=47&name=image-png-Oct-19-2023-12-01-23-3008-PM.png) - Expand the folders Workspace \> Users and click on your user: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-03-42-5791-PM.png?width=236&height=215&name=image-png-Oct-19-2023-12-03-42-5791-PM.png) - We have chosen to create a folder named *Artifacts*: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-04-33-3968-PM.png?width=670&height=171&name=image-png-Oct-19-2023-12-04-33-3968-PM.png) - Import all the generated artifacts from the folder *Jupyter*, *Helpers*, and *LoadControl*: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-05-20-2845-PM.png?width=331&height=287&name=image-png-Oct-19-2023-12-05-20-2845-PM.png) It is possible to have one or several files not imported as: ![](https://knowledge.bigenius-x.com/hs-fs/hubfs/image-png-Oct-19-2023-12-51-51-1918-PM.png?width=400&height=66&name=image-png-Oct-19-2023-12-51-51-1918-PM.png) It is due to Databricks itself. Just restart the import for the relevant files, and it should work. If you have already [discovered your source data](https://knowledge.bigenius-x.com/use-the-discovery-application?hsLang=en), [modeled your project](https://knowledge.bigenius-x.com/start-modeling-in-a-branch?hsLang=en), and [generated the artifacts](https://knowledge.bigenius-x.com/generate-artifacts?hsLang=en), you're now ready to [replace the placeholders](https://knowledge.bigenius-x.com/generators?hsLang=en#replace-placeholders) in your generated artifacts, [deploy these artifacts](https://knowledge.bigenius-x.com/generators?hsLang=en#deployment), and subsequently load the data based on the Generator you are using with the following possible load controls: - [Load the data with a native load control](https://knowledge.bigenius-x.com/generators?hsLang=en#load-data-with-a-native-load-control) - [Load the data with Apache Airflow](https://knowledge.bigenius-x.com/generators?hsLang=en#load-data-with-apache-airflow)   - [Getting started](https://knowledge.bigenius-x.com/getting-started?hsLang=en#main-content) - 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