Use Dash to Build Web Apps on MongoDB Data via CData Connect AI

Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Build interactive Dash web apps on live MongoDB data using pandas and the CData Connect AI Python SDK.

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 MongoDB-connected web applications for MongoDB data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing MongoDB 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.

About MongoDB Data Integration

Accessing and integrating live data from MongoDB has never been easier with CData. Customers rely on CData connectivity to:

MongoDB's flexibility means that it can be used as a transactional, operational, or analytical database. That means CData customers use our solutions to integrate their business data with MongoDB or integrate their MongoDB data with their data warehouse (or both). Customers also leverage our live connectivity options to analyze and report on MongoDB directly from their preferred tools, like Power BI and Tableau.

For more details on MongoDB use case and how CData enhances your MongoDB experience, check out our blog post: The Top 10 Real-World MongoDB Use Cases You Should Know in 2024.


Getting Started


Connect to MongoDB 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. Adding a Connection
  3. Select "MongoDB" from the Add Connection panel
  4. Selecting a data source
  5. Enter the necessary authentication properties to connect to MongoDB.

    Set the Server, Database, User, and Password connection properties to connect to MongoDB. To access MongoDB collections as tables you can use automatic schema discovery or write your own schema definitions. Schemas are defined in .rsd files, which have a simple format. You can also execute free-form queries that are not tied to the schema.

    Configuring a connection (Salesforce is shown)
  6. Click Save & Test
  7. Navigate to the Permissions tab and update the user-based permissions. Updating 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. Creating a new PAT
  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 MongoDB 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 MongoDB data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, MongoDB1).

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 borough, cuisine "
    "FROM [MongoDB1].[MongoDB].[restaurants] "
    "WHERE Name = 'Morris Park Bake Shop'",
    conn,
)

conn.close()

Configure the App and Layout

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

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

trace = go.Bar(x=df['borough'], y=df['cuisine'], name='borough')

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='MongoDB restaurants 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 MongoDB data.

python mongodb-dash.py
The Dash web app running in a browser (Salesforce is shown)

More Information and Free Trial

Now you can build interactive Dash web apps on live MongoDB data using the CData Connect AI Python SDK. For more information on connecting to MongoDB (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live MongoDB 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 borough, cuisine "
    "FROM [MongoDB1].[MongoDB].[restaurants] "
    "WHERE Name = 'Morris Park Bake Shop'",
    conn,
)

conn.close()

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

trace = go.Bar(x=df['borough'], y=df['cuisine'], name='borough')

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='MongoDB restaurants Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

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

Ready to get started?

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