Use Dash to Build Web Apps on Anaplan 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 Anaplan-connected web applications for Anaplan data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing Anaplan 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 Anaplan 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 "Anaplan" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Anaplan.

   Authenticating to Anaplan

   The driver supports authenticating with Basic, Certificate, or OAuth. In every case, set Region to the region where your Anaplan account data is hosted (e.g., US1, which is the default).

   Using Basic Authentication

   Set AuthScheme to Basic, then supply your Anaplan User and Password. If your workspace uses single sign-on (SSO), you must be assigned as an Exception User to use Basic authentication.

   Using Certificate Authentication

   Set AuthScheme to Certificate, then supply the Certificate, CertificateType, and PrivateKey properties (and the matching CertificatePassword / PrivateKeyPassword if either is encrypted). The certificate must be a CA-issued X.509 certificate registered with your Anaplan tenant administrator.

   Using OAuth Authentication

   Register a custom OAuth application in Anaplan, then set the following properties:

   • OAuthClientId: The client Id assigned when you registered your custom OAuth application.
   • OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.
   • CallbackURL: The redirect URI defined when you registered your application.
   • InitiateOAuth: Set to GETANDREFRESH to have the driver manage the OAuth token exchange and refresh automatically.

   See the Getting Started chapter of the help documentation for a guide to creating a custom OAuth app and using OAuth.

   
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 Anaplan 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 Anaplan data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Anaplan1).

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 Region, Product "
    "FROM [Anaplan1].[Anaplan].[Sales] "
    "WHERE Value = '100'",
    conn,
)

conn.close()

Configure the App and Layout

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

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

trace = go.Bar(x=df['Region'], y=df['Product'], name='Region')

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='Anaplan Sales 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 Anaplan data.

python anaplan-dash.py

More Information and Free Trial

Now you can build interactive Dash web apps on live Anaplan data using the CData Connect AI Python SDK. For more information on connecting to Anaplan (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live Anaplan 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 Region, Product "
    "FROM [Anaplan1].[Anaplan].[Sales] "
    "WHERE Value = '100'",
    conn,
)

conn.close()

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

trace = go.Bar(x=df['Region'], y=df['Product'], name='Region')

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='Anaplan Sales Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

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