Connect and Visualize Live JD Edwards Data in Databricks Lakehouse Federation with CData Connect AI

Databricks Lakehouse Federation enables organizations to query and integrate data from multiple sources without requiring data movement. It allows federated queries across databases, data warehouses, and lakehouses, providing a unified interface for data analysis and management within Databricks. When combined with CData Connect AI, it enables seamless access to JD Edwards data for data virtualization, while also supporting data lineage and fine-grained access control.

This article explains how to use CData Connect AI to establish a live connection to JD Edwards and how to access live JD Edwards data from the Databricks platform.

CData Connect AI offers a seamless SQL Server, cloud-to-cloud interface for JD Edwards, enabling you to effortlessly create dashboards and visualizations using live JD Edwards data in Databricks. While building visualizations, Databricks requires SQL queries to retrieve the necessary data. With built-in optimized data processing, CData Connect AI pushes all supported SQL operations (such as filters and JOINs) directly to JD Edwards, utilizing server-side processing for fast and efficient data retrieval of JD Edwards data.

Configure JD Edwards connectivity for Databricks in CData Connect AI

To work with JD Edwards data in Databricks - Lakehouse Federation, you need to connect to JD Edwards from Connect AI and provide user access to the connection.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "JD Edwards" from the Add Connection panel

3. Enter the necessary authentication properties to connect to JD Edwards.

   The driver connects to JD Edwards through your Application Interface Services (AIS) Server. Set the following connection properties:

   • URL: The base HTTPS URL of your AIS Server (e.g., https://jde-ais.example.com:8300).
   • User: Your JD Edwards username.
   • Password: Your JD Edwards password.
   • Environment (optional): The JD Edwards environment to use (e.g., PD920 for production or DV920 for development). If not specified, the AIS Server's default environment is used.
   • Role (optional): The JD Edwards role for the session. If not specified, the AIS Server's default role is used.
   • DeviceName (optional): An identifier for the connecting device or application, used for auditing and logging on the AIS Server.
   • Jasserver (optional): The specific Java Application Server (JAS) instance to route requests through, useful in clustered environments.

   Choosing Which Data Is Exposed

   JD Edwards organizes tables and business views by System Code, and the driver exposes each System Code as its own schema. Use these properties to control which schemas are available:

   • DataModel: One or more ERP modules (comma-separated) whose System Codes are exposed as schemas, or All to expose every System Code in the connected instance. Defaults to FinancialManagement.
   • SystemCodes: A comma-separated list of additional System Codes to expose alongside those from DataModel (e.g., 42,43).

   When you connect, the driver sends your credentials to the AIS Server to obtain a session token and caches it. The driver requests a new token automatically before the session expires.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add JD Edwards Connection page and update the User-based permissions.

Add a Personal Access Token

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. It is 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. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to JD Edwards data from Databricks.

Connecting live JD Edwards data in Databricks

Follow these steps to establish a connection from Databricks to the CData Connect AI Virtual SQL Server API.

1. Log into Databricks.
2. Navigate to SQL Warehouses and start any warehouse of your choice.
3. In the navigation pane, select Catalog. Click and select Create a connection.
4. In the Connection basics section (or Step 1 of Set up connection page), enter the following connection details and click Next:
   • Connection name: a user-defined connection name.
   • Connection type: select SQL Server from the drop-down list.
   • Auth type: select Username and password.
   
5. In the Authentication section (or Step 2), enter the required authentication details, and click Next:
   • Host: tds.cdata.com
   • Port: 14333
   • User: enter your CData Connect AI username, displayed in the top-right corner of the CData Connect AI interface. For example, [email protected]
   • Password: enter the PAT generated and copied in the previous section.
   
6. In the Connection details section (or Step 3), enable the Trust server certificate checkbox and select the appropriate Application intent. Click Create Connection.
7. In the Catalog basics section (or Step 4), enter the required details and click Create catalog:
   • Catalog name: enter a name of your choice
   • Connection: this will be the Databricks connection you defined earlier
   • Database: enter your JD Edwards connection name (for example, JD Edwards1)
   
8. In the Access section (or Step 5), assign the Workspace, User access rights, and Grant read or edit privileges to the catalog.
9. Click Next > Save to save all the details for the catalog.

Access the catalog and visualize live JD Edwards data in Databricks

To access the newly created catalog and create a dashboard to visualize live JD Edwards data in Databricks, follow these steps:

1. Select the catalog and expand it. A list of tables from JD Edwards will appear on the screen.
2. Choose the desired table and click the Overview tab to view the table metadata.
3. Click the Sample Data tab to view real-time data in the table.
4. Now, click Create at the top right corner and select Dashboard.
5. Manually create a visualization by selecting at least one field in the visualization editor from the widget, or choose one of the visualization options suggested by Databricks AI.
6. Once the visualization is created, edit the details in the widget settings of the dashboard.
7. Click Publish to publish the dashboard report.

Live access to JD Edwards data from cloud applications

At this stage, you have established a direct, cloud-to-cloud connection to live JD Edwards data in Databricks. This enables you to create dashboards to monitor and visualize your data seamlessly.

For more details on accessing live data from over 100 SaaS, Big Data, and NoSQL sources through cloud applications like Databricks, visit our Connect AI page. As always, let us know if you have any questions during your evaluation. Our world-class CData Support Team is always available to help!