Process & Analyze Microsoft Dataverse Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Microsoft Dataverse data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Microsoft Dataverse data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Microsoft Dataverse data. When you issue complex SQL queries to Microsoft Dataverse, the driver pushes supported SQL operations, like filters and aggregations, directly to Microsoft Dataverse and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Microsoft Dataverse data using native data types.

Getting Started

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Install the CData JDBC Driver in Databricks

To work with live Microsoft Dataverse data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.cds.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Microsoft Dataverse Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Microsoft Dataverse data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Microsoft Dataverse, and create a basic report.

Configure the Connection to Microsoft Dataverse

Connect to Microsoft Dataverse by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.cds.CDSDriver"
url = "jdbc:cds:RTK=5246...;OrganizationUrl=https://myaccount.crm.dynamics.com/InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Microsoft Dataverse JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

Fill in the connection properties and copy the connection string to the clipboard.

You can connect without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.

• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• OrganizationUrl: Set this to the organization URL you are connecting to, such as https://myorganization.crm.dynamics.com.
• Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.

When you connect the Common Data Service OAuth endpoint opens in your default browser. Log in and grant permissions. The OAuth process completes automatically.

Load Microsoft Dataverse Data

Once you configure the connection, you can load Microsoft Dataverse data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Accounts") \
	.load ()

Display Microsoft Dataverse Data

Check the loaded Microsoft Dataverse data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("AccountId"))

Analyze Microsoft Dataverse Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Microsoft Dataverse data for reporting, visualization, and analysis.

% sql

SELECT AccountId, Name FROM SAMPLE_VIEW ORDER BY Name DESC LIMIT 5

The data from Microsoft Dataverse is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Microsoft Dataverse and start working with your live Microsoft Dataverse data in Databricks. Reach out to our Support Team if you have any questions.