Use Dash to Build to Web Apps on Redshift Data

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

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

Connecting to Redshift Data

Connecting to Redshift 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 Redshift, set the following:

• Server: Set this to the host name or IP address of the cluster hosting the Database you want to connect to.
• Port: Set this to the port of the cluster.
• Database: Set this to the name of the database. Or, leave this blank to use the default database of the authenticated user.
• User: Set this to the username you want to use to authenticate to the Server.
• Password: Set this to the password you want to use to authenticate to the Server.

You can obtain the Server and Port values in the AWS Management Console:

1. Open the Amazon Redshift console (http://console.aws.amazon.com/redshift).
2. On the Clusters page, click the name of the cluster.
3. On the Configuration tab for the cluster, copy the cluster URL from the connection strings displayed.

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

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

cnxn = mod.connect("User=admin;Password=admin;Database=dev;Server=examplecluster.my.us-west-2.redshift.amazonaws.com;Port=5439;")

Execute SQL to Redshift

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 ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", 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-redshiftedataplot'

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 Redshift data and configure the app layout.

trace = go.Bar(x=df.ShipName, y=df.ShipCity, name='ShipName')

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='Redshift 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 Redshift data.

python redshift-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Amazon Redshift to start building Python apps with connectivity to Redshift 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.redshift as mod
import plotly.graph_objs as go

cnxn = mod.connect("User=admin;Password=admin;Database=dev;Server=examplecluster.my.us-west-2.redshift.amazonaws.com;Port=5439;")

df = pd.read_sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'", cnxn)
app_name = 'dash-redshiftdataplot'

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.ShipName, y=df.ShipCity, name='ShipName')

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='Redshift Orders Data', barmode='stack')
		})
], className="container")

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