Use Dash to Build to Web Apps on JD Edwards Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for JD Edwards, 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 JD Edwards with the CData Connector and use pandas and Dash to build a simple web app for visualizing JD Edwards data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live JD Edwards data in Python. When you issue complex SQL queries from JD Edwards, the driver pushes supported SQL operations, like filters and aggregations, directly to JD Edwards and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to JD Edwards Data

Connecting to JD Edwards data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

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.

After installing the CData JD Edwards Connector, follow the procedure below to install the other required modules and start accessing JD Edwards through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize JD Edwards Data in Python

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

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.jdedwards as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData JD Edwards Connector to create a connection for working with JD Edwards data.

cnxn = mod.connect("URL=https://your-jde-environment-app.example.com;User=admin;Password=myPassword;")

Execute SQL to JD Edwards

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT DocumentNumber, Amount FROM AccountsPayable.AccountLedger WHERE BusinessUnit = '100'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-jdedwardsedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our JD Edwards data and configure the app layout.

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

app.layout = html.Div(children=[html.H1("CData Extension + 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, we are ready to run the app. The last lines of Python code follow.

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

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

python jdedwards-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for JD Edwards to start building Python apps with connectivity to JD Edwards data. Reach out to our Support Team if you have any questions.

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.jdedwards as mod
import plotly.graph_objs as go

cnxn = mod.connect("URL=https://your-jde-environment-app.example.com;User=admin;Password=myPassword;")

df = pd.read_sql("SELECT DocumentNumber, Amount FROM AccountsPayable.AccountLedger WHERE BusinessUnit = '100'", cnxn)
app_name = 'dash-jdedwardsdataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.DocumentNumber, y=df.Amount, name='DocumentNumber')

app.layout = html.Div(children=[html.H1("CData Extension + 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_server(debug=True)