Process & Analyze ADP Data in Azure Databricks

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ADP JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with ADP.



Host the CData JDBC Driver for ADP in Azure and use Databricks to perform data engineering and data science on live ADP data.

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 ADP data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live ADP data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live ADP data in Databricks, install the driver on your Azure 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.adp.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for ADP\lib).

Connect to ADP from Databricks

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

Configure the Connection to ADP

Connect to ADP by referencing the class for the JDBC Driver 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.

driver = "cdata.jdbc.adp.ADPDriver"
url = "jdbc:adp:RTK=5246...;OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;SSLClientCert='c:\cert.pfx';SSLClientCertPassword='admin@123'InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.adp.jar

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

Connect to ADP by specifying the following properties:

  • SSLClientCert: Set this to the certificate provided during registration.
  • SSLClientCertPassword: Set this to the password of the certificate.
  • UseUAT: The connector makes requests to the production environment by default. If using a developer account, set UseUAT = true.
  • RowScanDepth: The maximum number of rows to scan for the custom fields columns available in the table. The default value will be set to 100. Setting a high value may decrease performance.

The connector uses OAuth to authenticate with ADP. OAuth requires the authenticating user to interact with ADP using the browser. For more information, refer to the OAuth section in the Help documentation.

Load ADP Data

Once the connection is configured, you can load ADP data as a dataframe using the CData JDBC Driver and the connection information.

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

Display ADP Data

Check the loaded ADP data by calling the display function.

display (remote_table.select ("AssociateOID"))

Analyze ADP Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the ADP data for analysis.

% sql

SELECT AssociateOID, WorkerID FROM Workers WHERE AssociateOID = 'G3349PZGBADQY8H8'

The data from ADP 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 ADP and start working with your live ADP data in Apache NiFi. Reach out to our Support Team if you have any questions.