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

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

   Connect to ADP by specifying the following properties:

   • OAuthClientId: The client Id of the custom OAuth application you obtained from ADP.
   • OAuthClientSecret: The custom OAuth application's client secret.
   • SSLClientCert: Set this to the certificate provided during registration.
   • SSLClientCertPassword: Set this to the password of the certificate.
   • UseUAT: The connector makes requests to the production environment by default. If using a developer account, set UseUAT = true.
   • RowScanDepth: The maximum number of rows to scan for the custom fields columns available in the table. The default value will be set to 100. Setting a high value may decrease performance.

   The connector uses OAuth to authenticate with ADP. OAuth requires the authenticating user to interact with ADP using the browser. OAuth access can be configured in ADP through ADP API Central. For more information, refer ADP's API Central Quick Start Guide and the OAuth section in CData's Help documentation.

   
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 ADP 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 ADP data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, ADP1).

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 AssociateOID, WorkerID "
    "FROM [ADP1].[ADP].[Workers] "
    "WHERE AssociateOID = 'G3349PZGBADQY8H8'",
    conn,
)

conn.close()

Configure the App and Layout

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

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

trace = go.Bar(x=df['AssociateOID'], y=df['WorkerID'], name='AssociateOID')

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='ADP Workers 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 ADP data.

python adp-dash.py

More Information and Free Trial

Now you can build interactive Dash web apps on live ADP data using the CData Connect AI Python SDK. For more information on connecting to ADP (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live ADP 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 AssociateOID, WorkerID "
    "FROM [ADP1].[ADP].[Workers] "
    "WHERE AssociateOID = 'G3349PZGBADQY8H8'",
    conn,
)

conn.close()

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

trace = go.Bar(x=df['AssociateOID'], y=df['WorkerID'], name='AssociateOID')

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='ADP Workers Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

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