Use Dash to Build to Web Apps on CleverPush Data

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

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

Connecting to CleverPush Data

Connecting to CleverPush 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.

Start by setting the Profile connection property to the location of the Cleverpush Profile on disk (e.g. C:\profiles\Cleverpush.apip). Next, set the ProfileSettings connection property to the connection string for Cleverpush (see below).

Cleverpush API Profile Settings

CleverPush uses private API keys to authenticate requests. Your API key is passed as the Authorization request header value on every API call.

You can find your private API key in the CleverPush dashboard under Settings > API. Use the private key (not the public key) for server-side access.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your CleverPush private API key.

Optional Connection Properties

• ChannelId: Set this to your default CleverPush channel identifier. Most tables require a channel filter. Setting this property allows queries without specifying ChannelId in every WHERE clause.

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

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

cnxn = mod.connect("Profile=C:\profiles\Cleverpush.apip;ProfileSettings='APIKey=my_api_key';")

Execute SQL to CleverPush

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 Id, Name FROM Segments WHERE ChannelId = 'your_channel_id'", 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-apiedataplot'

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 CleverPush data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Name, name='Id')

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='CleverPush Segments 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 CleverPush data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to CleverPush 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.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Cleverpush.apip;ProfileSettings='APIKey=my_api_key';")

df = pd.read_sql("SELECT Id, Name FROM Segments WHERE ChannelId = 'your_channel_id'", cnxn)
app_name = 'dash-apidataplot'

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.Id, y=df.Name, name='Id')

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='CleverPush Segments Data', barmode='stack')
		})
], className="container")

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