Use Dash to Build to Web Apps on Databricks Data

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Databricks Python Connector

Python Connector Libraries for Databricks Data Connectivity. Integrate Databricks with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Databricks enables you to create Python applications that use pandas and Dash to build Databricks-connected web apps.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Databricks, the pandas module, and the Dash framework, you can build Databricks-connected web applications for Databricks data. This article shows how to connect to Databricks with the CData Connector and use pandas and Dash to build a simple web app for visualizing Databricks data.

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

Connecting to Databricks Data

Connecting to Databricks 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.

To connect to a Databricks cluster, set the properties as described below.

Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

  • Server: Set to the Server Hostname of your Databricks cluster.
  • HTTPPath: Set to the HTTP Path of your Databricks cluster.
  • Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=443;TransportMode=HTTP;HTTPPath=MyHTTPPath;UseSSL=True;User=MyUser;Password=MyPassword;")

Execute SQL to Databricks

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 City, CompanyName FROM Customers WHERE Country = 'US'", 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-databricksedataplot'

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 Databricks data and configure the app layout.

trace = go.Bar(x=df.City, y=df.CompanyName, name='City')

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='Databricks Customers 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 Databricks data.

python databricks-dash.py

Free Trial & More Information

Download a free, 30-day trial of the Databricks Python Connector to start building Python apps with connectivity to Databricks 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.databricks as mod
import plotly.graph_objs as go

cnxn = mod.connect("Server=127.0.0.1;Port=443;TransportMode=HTTP;HTTPPath=MyHTTPPath;UseSSL=True;User=MyUser;Password=MyPassword;")

df = pd.read_sql("SELECT City, CompanyName FROM Customers WHERE Country = 'US'", cnxn)
app_name = 'dash-databricksdataplot'

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.City, y=df.CompanyName, name='City')

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='Databricks Customers Data', barmode='stack')
		})
], className="container")

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