Use Dash to Build to Web Apps on Sage X3 Cloud 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 X3 Cloud, the pandas module, and the Dash framework, you can build Sage X3 Cloud-connected web applications for Sage X3 Cloud data. This article shows how to connect to Sage X3 Cloud with the CData Connector and use pandas and Dash to build a simple web app for visualizing Sage X3 Cloud data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Sage X3 Cloud data in Python. When you issue complex SQL queries from Sage X3 Cloud, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage X3 Cloud and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage X3 Cloud Data

Connecting to Sage X3 Cloud 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 X3 Cloud uses the OAuth 2.0 Client Credentials flow, and an X-API-Key is also required for API access. Set AuthScheme to OAuth and specify the following connection properties:

• URL: The base URL of your Sage X3 Cloud instance.
• OAuthAccessTokenUrl: The OAuth token endpoint (e.g., https://your-auth-domain/oauth/token).
• OAuthClientId: Your OAuth application client ID.
• OAuthClientSecret: Your OAuth application client secret.
• Audience: The API audience value for the token request.
• XAPIKey: The X-API-Key provided by your Sage X3 Cloud administrator.
• Folder: The Sage X3 folder name (e.g., SEED). This folder is used as the default schema.
• Folders (optional): A comma-separated list of Sage X3 folders (e.g., SEED,PERF). Each folder is exposed as a separate schema, so you can query across folders with the Schema.Table syntax.

The driver obtains an access token with the Client Credentials flow and sends it with the X-API-Key on every API request. With InitiateOAuth set to GETANDREFRESH (the default), the driver acquires and refreshes the token automatically.

After installing the CData Sage X3 Cloud Connector, follow the procedure below to install the other required modules and start accessing Sage X3 Cloud 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 X3 Cloud 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.sagex3cloud as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Sage X3 Cloud Connector to create a connection for working with Sage X3 Cloud data.

cnxn = mod.connect("AuthScheme=OAuth;URL=https://x3server/;OAuthAccessTokenUrl=https://auth-domain/oauth/token;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;Audience=https://api-audience;XAPIKey=your_api_key;Folder=SEED;")

Execute SQL to Sage X3 Cloud

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 BPCNUM, BPCNAM FROM BPCUSTOMER WHERE BPCNUM = 'MARTIN'", 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-sagex3cloudedataplot'

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 X3 Cloud data and configure the app layout.

trace = go.Bar(x=df.BPCNUM, y=df.BPCNAM, name='BPCNUM')

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 X3 Cloud BPCUSTOMER 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 X3 Cloud data.

python sagex3cloud-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Sage X3 Cloud to start building Python apps with connectivity to Sage X3 Cloud 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.sagex3cloud as mod
import plotly.graph_objs as go

cnxn = mod.connect("AuthScheme=OAuth;URL=https://x3server/;OAuthAccessTokenUrl=https://auth-domain/oauth/token;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;Audience=https://api-audience;XAPIKey=your_api_key;Folder=SEED;")

df = pd.read_sql("SELECT BPCNUM, BPCNAM FROM BPCUSTOMER WHERE BPCNUM = 'MARTIN'", cnxn)
app_name = 'dash-sagex3clouddataplot'

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.BPCNUM, y=df.BPCNAM, name='BPCNUM')

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 X3 Cloud BPCUSTOMER Data', barmode='stack')
		})
], className="container")

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