Use Dash to Build Web Apps on JD Edwards Data via 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, the pandas module, and the Dash framework, you can build JD Edwards-connected web applications for JD Edwards data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing JD Edwards data.

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

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 (with the pandas extra), Dash, and Plotly using the pip utility:

pip install "cdata-connect-ai[full]"
pip install dash
pip install plotly

Build a Web App on JD Edwards Data in Python

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

First, import the modules, then connect to Connect AI with your account email and PAT and read JD Edwards data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, JDEdwards1).

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

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

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

conn.close()

Configure the App and Layout

With the query results stored in a DataFrame, build a bar graph from the JD Edwards data and configure the app layout.

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['DocumentNumber'], y=df['Amount'], name='DocumentNumber')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='JD Edwards AccountsPayable.AccountLedger Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

Set the App to Run

With the connection, app, and layout configured, you are ready to run the app.

if __name__ == '__main__':
    app.run(debug=True)

Now, use Python to run the web app and a browser to view the JD Edwards data.

python jdedwards-dash.py

More Information and Free Trial

Now you can build interactive Dash web apps on live JD Edwards data 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 web apps on live JD Edwards data in Python.

Full Source Code

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

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

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

conn.close()

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['DocumentNumber'], y=df['Amount'], name='DocumentNumber')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='JD Edwards AccountsPayable.AccountLedger Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

if __name__ == '__main__':
    app.run(debug=True)