Connect and Query Live ADP Data in Databricks with CData Connect AI

Mohsin Turki
Mohsin Turki
Technical Marketing Engineer
Use CData Connect AI to integrate live ADP data into Databricks and enable direct, live querying and analysis without replication.

Databricks is a leading AI cloud-native platform that unifies data engineering, machine learning, and analytics at scale. Its powerful data lakehouse architecture combines the performance of data warehouses with the flexibility of data lakes. Integrating Databricks with CData Connect AI gives organizations live, real-time access to ADP data without the need for complex ETL pipelines or data duplication—streamlining operations and reducing time-to-insights.

In this article, we'll walk through how to configure a secure, live connection from Databricks to ADP using CData Connect AI. Once configured, you'll be able to access ADP data directly from Databricks notebooks using standard SQL—enabling unified, real-time analytics across your data ecosystem.

Overview

Here is an overview of the simple steps:

  1. Step 1 — Connect and Configure: In CData Connect AI, create a connection to your ADP source, configure user permissions, and generate a Personal Access Token (PAT).
  2. Step 2 — Query from Databricks: Install the CData JDBC driver in Databricks, configure your notebook with the connection details, and run SQL queries to access live ADP data.

Prerequisites

Before you begin, make sure you have the following:

  1. An active ADP account.
  2. A CData Connect AI account. You can log in or sign up for a free trial here.
  3. A Databricks account. Sign up or log in here.

Step 1: Connect and Configure a ADP Connection in CData Connect AI

1.1 Add a Connection to ADP

CData Connect AI uses a straightforward, point-and-click interface to connect to available data sources.

  1. Log into Connect AI, click Sources on the left, and then click Add Connection in the top-right.
  2. Select "ADP" from the Add Connection panel.
  3. Enter the necessary authentication properties to connect to ADP.

    Connect to ADP by specifying the following properties:

    • OAuthClientId: The client Id of the custom OAuth application you obtained from ADP.
    • OAuthClientSecret: The custom OAuth application's client secret.
    • 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. OAuth access can be configured in ADP through ADP API Central. For more information, refer ADP's API Central Quick Start Guide and the OAuth section in CData's Help documentation.

  4. Click Save & Test in the top-right.
  5. Navigate to the Permissions tab on the ADP Connection page and update the user-based permissions based on your preferences.

1.2 Generate a Personal Access Token (PAT)

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. PAT functions as an alternative to your login credentials for secure, token-based authentication. It is a best practice to create a separate PAT for each service to maintain granularity of access.

  1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
  2. On the Settings page, go to the Access Tokens section and click Create PAT.
  3. Give the PAT a name and click Create.
  4. Note: The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

Step 2: Connect and Query ADP Data in Databricks

Follow these steps to establish a connection from Databricks to ADP. You'll install the CData JDBC Driver for Connect AI, add the JAR file to your cluster, configure your notebooks, and run SQL queries to access live ADP data data.

2.1 Install the CData JDBC Driver for Connect AI

  1. In CData Connect AI, click the Integrations page on the left. Search for JDBC or Databricks, click Download, and select the installer for your operating system.
  2. Once downloaded, run the installer and follow the instructions:
    • For Windows: Run the setup file and follow the installation wizard.
    • For Mac/Linux: Unpack the archive and move the folder to /opt or /Applications. Make sure you have execute permissions.
  3. After installation, locate the JAR file in the installation directory:
    • Windows: 
      C:\Program Files\CData\CData JDBC Driver for Connect AI\lib\cdata.jdbc.connect.jar
    • Mac/Linux: 
      /Applications/CData/CData JDBC Driver for Connect AI/lib/cdata.jdbc.connect.jar

2.2 Install the JAR File on Databricks

  1. Log in to Databricks. In the navigation pane, click Compute on the left. Start or create a compute cluster.
  2. Click on the running cluster, go to the Libraries tab, and click Install New at the top right.
  3. In the Install Library dialog, select DBFS, and drag and drop the cdata.jdbc.connect.jar file. Click Install.

2.3 Query ADP Data in a Databricks Notebook

Notebook Script 1 — Define JDBC Connection:

  1. Paste the following script into the notebook cell:
driver = "cdata.jdbc.connect.ConnectDriver"
url = "jdbc:connect:AuthScheme=Basic;User=your_username;Password=your_pat;URL=https://cloud.cdata.com/api/;DefaultCatalog=Your_Connection_Name;"
  1. Replace:
    • your_username - With your CData Connect AI username
    • your_pat - With your CData Connect AI Personal Access Token (PAT)
    • Your_Connection_Name - With the name of your Connect AI data source, from the Sources page
  2. Run the script.

Notebook Script 2 — Load DataFrame from ADP data:

  1. Add a new cell for this second script. From the menu on the right side of your notebook, click Add cell below.
  2. Paste the following script into the new cell:
remote_table = spark.read.format("jdbc") \
  .option("driver", "cdata.jdbc.connect.ConnectDriver") \
  .option("url", "jdbc:connect:AuthScheme=Basic;User=your_username;Password=your_pat;URL=https://cloud.cdata.com/api/;DefaultCatalog=Your_Connection_Name;") \
  .option("dbtable", "YOUR_SCHEMA.YOUR_TABLE") \
  .load()
  1. Replace:
    • your_username - With your CData Connect AI username
    • your_pat - With your CData Connect AI Personal Access Token (PAT)
    • Your_Connection_Name - With the name of your Connect AI data source, from the Sources page
    • YOUR_SCHEMA.YOUR_TABLE - With your schema and table, for example, ADP.Workers
  2. Run the script.

Notebook Script 3 — Preview Columns:

  1. Similarly, add a new cell for this third script.
  2. Paste the following script into the new cell:
display(remote_table.select("ColumnName1", "ColumnName2"))
  1. Replace ColumnName1 and ColumnName2 with the actual columns from your ADP structure (e.g. AssociateOID, WorkerID, etc.).
  2. Run the script.

You can now explore, join, and analyze live ADP data directly within Databricks notebooks—without needing to know the complexities of the back-end API and without replicating ADP data.

Try CData Connect AI Free for 14 Days

Ready to simplify real-time access to ADP data? Start your free 14-day trial of CData Connect AI today and experience seamless, live connectivity from Databricks to ADP.

Low code, zero infrastructure, zero replication — just seamless, secure access to your most critical data and insights.

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