Use Dash to Build BigQuery-Connected Python Web Apps
Tags: python, sql, webdev, dbapi
Published: 2020-04-21 19:18:05

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the [CData Python Connector for BigQuery](https://www.cdata.com/drivers/bigquery/python?utm_source=devto&utm_campaign=developer), the pandas module, and the Dash framework, you can build BigQuery-connected web applications. This article shows how to connect to BigQuery with the CData Connector and use pandas and Dash to build a simple web app for visualizing BigQuery data.

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

## Connecting to BigQuery Data

Connecting to BigQuery 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.

Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, you will need to specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

After installing the CData BigQuery Connector, follow the procedure below to install the other required modules and start accessing BigQuery 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 BigQuery 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.googlebigquery as mod
    import plotly.graph_objs as go</pre>

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

    cnxn = mod.connect("""DataSetId=MyDataSetId;
		ProjectId=MyProjectId;
		InitiateOAuth=GETANDREFRESH;
		OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt""")

### Execute SQL to BigQuery

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 OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", 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-googlebigqueryedataplot'

    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 BigQuery data and configure the app layout.

    trace = go.Bar(x=df.OrderName, y=df.Freight, name='OrderName')

    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='BigQuery Orders 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 BigQuery data.

    python googlebigquery-dash.py

![Google BigQuery Data in Dash](https://dev-to-uploads.s3.amazonaws.com/i/hg5yt2xgslwifstwn43n.png)

#### 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.googlebigquery as mod
    import plotly.graph_objs as go

    cnxn = mod.connect("""DataSetId=MyDataSetId;
		ProjectId=MyProjectId;
		InitiateOAuth=GETANDREFRESH;
		OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt""")

    df = pd.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", cnxn)
    app_name = 'dash-googlebigquerydataplot'

    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.OrderName, y=df.Freight, name='OrderName')

    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='BigQuery Orders Data', barmode='stack')
    		})
    ], className="container")
    
    if __name__ == '__main__':
        app.run_server(debug=True)