Use Dash to Build to Web Apps on Azure Analysis Services Data

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Azure Analysis Services Python Connector

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



The CData Python Connector for Azure Analysis Services enables you to create Python applications that use pandas and Dash to build Azure Analysis Services-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 Azure Analysis Services, the pandas module, and the Dash framework, you can build Azure Analysis Services-connected web applications for Azure Analysis Services data. This article shows how to connect to Azure Analysis Services with the CData Connector and use pandas and Dash to build a simple web app for visualizing Azure Analysis Services data.

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

Connecting to Azure Analysis Services Data

Connecting to Azure Analysis Services 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 Azure Analysis Services, set the Url property to a valid server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating. Optionally, set Database to distinguish which Azure database on the server to connect to.

Azure Analysis Services uses the OAuth authentication standard. OAuth requires the authenticating user to interact with Azure Analysis Services using the browser. You can connect without setting any connection properties for your user credentials. See the Help documentation for more information.

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

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

cnxn = mod.connect("URL=asazure://REGION.asazure.windows.net/server;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Azure Analysis Services

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 Country, Education FROM Customer WHERE Country = 'Australia'", 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-aasedataplot'

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 Azure Analysis Services data and configure the app layout.

trace = go.Bar(x=df.Country, y=df.Education, name='Country')

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='Azure Analysis Services Customer 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 Azure Analysis Services data.

python aas-dash.py

Free Trial & More Information

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

cnxn = mod.connect("URL=asazure://REGION.asazure.windows.net/server;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Country, Education FROM Customer WHERE Country = 'Australia'", cnxn)
app_name = 'dash-aasdataplot'

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.Country, y=df.Education, name='Country')

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='Azure Analysis Services Customer Data', barmode='stack')
		})
], className="container")

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