Ready to get started?

Learn more about the CData Python Connector for SQL Analysis Services or download a free trial:

Download Now

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

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

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

Connecting to SQL Analysis Services Data

Connecting to SQL 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, provide authentication and set the Url property to a valid SQL Server Analysis Services endpoint. You can connect to SQL Server Analysis Services instances hosted over HTTP with XMLA access. See the Microsoft documentation to configure HTTP access to SQL Server Analysis Services.

To secure connections and authenticate, set the corresponding connection properties, below. The data provider supports the major authentication schemes, including HTTP and Windows, as well as SSL/TLS.

  • HTTP Authentication

    Set AuthScheme to "Basic" or "Digest" and set User and Password. Specify other authentication values in CustomHeaders.

  • Windows (NTLM)

    Set the Windows User and Password and set AuthScheme to "NTLM".

  • Kerberos and Kerberos Delegation

    To authenticate with Kerberos, set AuthScheme to NEGOTIATE. To use Kerberos delegation, set AuthScheme to KERBEROSDELEGATION. If needed, provide the User, Password, and KerberosSPN. By default, the data provider attempts to communicate with the SPN at the specified Url.

  • SSL/TLS:

    By default, the data provider attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store. To specify another certificate, see the SSLServerCert property for the available formats.

You can then access any cube as a relational table: When you connect the data provider retrieves SSAS metadata and dynamically updates the table schemas. Instead of retrieving metadata every connection, you can set the CacheLocation property to automatically cache to a simple file-based store.

See the Getting Started section of the CData documentation, under Retrieving Analysis Services Data, to execute SQL-92 queries to the cubes.

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;URL=http://localhost/OLAP/msmdpump.dll;")

Execute SQL to SQL 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 Fiscal_Year, Sales_Amount FROM Adventure_Works WHERE Fiscal_Year = 'FY 2008'", 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-ssasedataplot'

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 SQL Analysis Services data and configure the app layout.

trace = go.Bar(x=df.Fiscal_Year, y=df.Sales_Amount, name='Fiscal_Year')

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='SQL Analysis Services Adventure_Works 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 SQL Analysis Services data.

python ssas-dash.py

Free Trial & More Information

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;URL=http://localhost/OLAP/msmdpump.dll;")

df = pd.read_sql("SELECT Fiscal_Year, Sales_Amount FROM Adventure_Works WHERE Fiscal_Year = 'FY 2008'", cnxn)
app_name = 'dash-ssasdataplot'

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.Fiscal_Year, y=df.Sales_Amount, name='Fiscal_Year')

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='SQL Analysis Services Adventure_Works Data', barmode='stack')
		})
], className="container")

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