Process & Analyze Bullhorn CRM 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 Bullhorn CRM data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Bullhorn CRM data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Bullhorn CRM data. When you issue complex SQL queries to Bullhorn CRM, the driver pushes supported SQL operations, like filters and aggregations, directly to Bullhorn CRM 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 Bullhorn CRM data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Bullhorn CRM 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.bullhorncrm.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Bullhorn CRM Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Bullhorn CRM 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 Bullhorn CRM, and create a basic report.

Configure the Connection to Bullhorn CRM

Connect to Bullhorn CRM 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.bullhorncrm.BullhornCRMDriver"
url = "jdbc:bullhorncrm:RTK=5246...;DataCenterCode=CLS33;OAuthClientId=myoauthclientid;OAuthClientSecret=myoauthclientsecret;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Bullhorn CRM 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.

Begin by providing your Bullhorn CRM account credentials in the following:

• DataCenterCode: Set this to the data center code which responds to your data center. Refer to the list of data-center-specific Bullhorn API URLs: https://bullhorn.github.io/Data-Center-URLs/

If you are uncertain about your data center code, codes like CLS2, CLS21, etc. are cluster IDs that are contained in a user's browser URL (address bar) once they are logged in.

Example: https://cls21.bullhornstaffing.com/BullhornSTAFFING/MainFrame.jsp?#no-ba... indicates that the logged in user is on CLS21.

Authenticating with OAuth

Bullhorn CRM uses the OAuth 2.0 authentication standard. To authenticate using OAuth, create and configure a custom OAuth app. See the Help documentation for more information.

Load Bullhorn CRM Data

Once you configure the connection, you can load Bullhorn CRM 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" , "Candidate") \
	.load ()

Display Bullhorn CRM Data

Check the loaded Bullhorn CRM data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

Analyze Bullhorn CRM 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 Bullhorn CRM data for reporting, visualization, and analysis.

% sql

SELECT Id, CandidateName FROM SAMPLE_VIEW ORDER BY CandidateName DESC LIMIT 5

The data from Bullhorn CRM 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 Bullhorn CRM and start working with your live Bullhorn CRM data in Databricks. Reach out to our Support Team if you have any questions.