Use Dash to Build to Web Apps on Sage 300 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 Sage 300, the pandas module, and the Dash framework, you can build Sage 300-connected web applications for Sage 300 data. This article shows how to connect to Sage 300 with the CData Connector and use pandas and Dash to build a simple web app for visualizing Sage 300 data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Sage 300 data in Python. When you issue complex SQL queries from Sage 300, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 300 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 300 Data

Connecting to Sage 300 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.

Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

• Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
• Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
• Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

Authenticate to Sage 300 using Basic authentication.

Connect Using Basic Authentication

You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

• Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
• User: Set this to the username of your account.
• Password: Set this to the password of your account.

After installing the CData Sage 300 Connector, follow the procedure below to install the other required modules and start accessing Sage 300 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 Sage 300 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.sage300 as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Sage 300 Connector to create a connection for working with Sage 300 data.

cnxn = mod.connect("User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;")

Execute SQL to Sage 300

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 InvoiceUniquifier, ApprovedLimit FROM OEInvoices WHERE AllowPartialShipments = 'Yes'", 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-sage300edataplot'

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 Sage 300 data and configure the app layout.

trace = go.Bar(x=df.InvoiceUniquifier, y=df.ApprovedLimit, name='InvoiceUniquifier')

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='Sage 300 OEInvoices 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 Sage 300 data.

python sage300-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Sage 300 to start building Python apps with connectivity to Sage 300 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.sage300 as mod
import plotly.graph_objs as go

cnxn = mod.connect("User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;")

df = pd.read_sql("SELECT InvoiceUniquifier, ApprovedLimit FROM OEInvoices WHERE AllowPartialShipments = 'Yes'", cnxn)
app_name = 'dash-sage300dataplot'

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.InvoiceUniquifier, y=df.ApprovedLimit, name='InvoiceUniquifier')

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='Sage 300 OEInvoices Data', barmode='stack')
		})
], className="container")

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