How to Build an ETL App for JD Edwards Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK and the petl framework, you can build JD Edwards-connected applications and pipelines for extracting, transforming, and loading JD Edwards data. This article shows how to connect to Connect AI and use petl to extract, transform, and load JD Edwards data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to JD Edwards in Connect AI

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

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 and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click 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 PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for JD Edwards Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query JD Edwards

Use SQL to create a statement for querying JD Edwards. In this article, we read data from the AccountsPayable.AccountLedger entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, JDEdwards1).

sql = (
    "SELECT DocumentNumber, Amount "
    "FROM [JDEdwards1].[JDEdwards].[AccountsPayable.AccountLedger] "
    "WHERE BusinessUnit = '100'"
)

Extract, Transform, and Load the JD Edwards Data

With a connection and query in hand, use petl to extract, transform, and load the JD Edwards data. In this example, we extract JD Edwards data, sort the data by the Amount column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Amount')

etl.tocsv(table2, 'accountspayable.accountledger_data.csv')

JD Edwards is a read-only source in Connect AI, so this pipeline can extract and transform JD Edwards data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with JD Edwards data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live JD Edwards data through petl using the CData Connect AI Python SDK. For more information on connecting to JD Edwards (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live JD Edwards data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT DocumentNumber, Amount "
    "FROM [JDEdwards1].[JDEdwards].[AccountsPayable.AccountLedger] "
    "WHERE BusinessUnit = '100'"
)

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, 'Amount')

etl.tocsv(table2, 'accountspayable.accountledger_data.csv')
conn.close()