Model JD Edwards Data Using Azure Analysis Services

Dibyendu Datta
Dibyendu Datta
Lead Technology Evangelist
Leverage CData Connect AI to establish a connection between Azure Analysis Services and JD Edwards, enabling the direct import of real-time JD Edwards data.

Microsoft Azure Analysis Services (AAS) is a fully-managed platform-as-a-service (PaaS) offering that delivers enterprise-grade data models in the cloud. When combined with CData Connect AI, AAS facilitates immediate cloud-to-cloud access to JD Edwards data for applications. This article outlines the process of connecting to JD Edwards via Connect AI and importing JD Edwards data into Visual Studio using an AAS extension.

CData Connect AI offers a seamless cloud-to-cloud interface tailored for JD Edwards, enabling you to create live models of JD Edwards data in Azure Analysis Services without the need to replicate data to a natively supported database. While constructing high-quality semantic data models for business reports and client applications, Azure Analysis Services formulates SQL queries to retrieve data. CData Connect AI is equipped with optimized data processing capabilities right from the start, directing all supported SQL operations, including filters and JOINs, directly to JD Edwards. This leverages server-side processing for swift retrieval of the requested JD Edwards data.

Prerequisites

Before you connect, you must first do the following:

  • Connect a data source to your CData Connect AI account. Detailed steps are provided in the next section.
  • Generate a Personal Access Token (PAT). Copy this down, as it acts as your password during authentication.
  • Create a server in Azure Analysis Services to which you will deploy your data from CData Connect AI.
  • Install and configure an On-Premise Gateway in your system. This will pull data from the source via CData Connect AI into the Azure Analysis Services project and deploy models to the server. Refer to the given link to find the detailed process.

Configure JD Edwards Connectivity for AAS

Connectivity to JD Edwards from Azure Analysis Services is made possible through CData Connect AI. To work with JD Edwards data from Azure Analysis Services, we start by creating and configuring a JD Edwards connection.

  1. Log into Connect AI, click Sources, and then click Add Connection
    2. Adding a Connection
  2. Select "JD Edwards" from the Add Connection panel
    3. Selecting a data source
  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.

    Configuring a connection (Salesforce is shown)
  4. Click Save & Test
  5. Navigate to the Permissions tab in the Add JD Edwards Connection page and update the User-based permissions. Updating 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. Creating a new PAT
  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 Visual Studio using Azure Analysis Services.

Connect to JD Edwards in Visual Studio Using AAS

The steps below outline connecting to CData Connect AI from Azure Analysis Services to create a new JD Edwards data source. You will need the Microsoft Analysis Services Project extension installed in Microsoft Visual Studio to continue.

  1. In Visual Studio, create a new project. Select Analysis Services Tabular Project. Click on Next.
    2. Selecting Analysis Services Tabular Project
  2. In the Configure your new project dialog box, enter a name for your project in the Project name field. Fill in the rest of the fields.
    3. Configure new project
  3. Click on Create. The Tabular model designer dialog box opens. Select Workspace server and enter the address of your Azure Analysis Services server (for example, asazure://eastus.azure.windows.net/myAzureServer). Also, make sure to select the option SQL Server 2022 / Azure Analysis Services (1600) from the Compatibility level dropdown. Click on Test Connection to check if the connection details are correct. Click OK and sign in to your server.
    4. Adding AAS server
  4. Now, click on OK to create the project. Your Visual Studio window should resemble the following screenshot:
    5. Visual Studio interface for creating the project
  5. In the Tabular Model Explorer window of Visual Studio, right-click Data Sources and select Import From Data Source.
    6. Importing from the data source
  6. In the Get Data window, select SQL Server database and click Connect. In the Server field, enter the Virtual SQL Server endpoint and the port separated by a comma: e.g., “tds.cdata.com, 14333”, and click on OK.
    7. Selecting SQL Server database Entering the virtual SQL server endpoint and port number
  7. Click on Database and enter the following information:
    • User name: Enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected].
    • Password: Enter the PAT you generated on the Settings page.

    Click on Connect. If successful, the Navigator window will pop up.

    Entering the Username and Password (PAT)
  8. In the Navigator window, search and select the tables of your choice Searching and selectisng the data source tables
  9. You should now see the Salesforce table populated with data in the preview section on the right panel.
  10. Click on Load to import the data. Select and load tables from the data source

Now that you have imported the JD Edwards data into your data model, you are ready to deploy the project to Azure Analysis Services for use in business reports, client applications, and more.

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