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Use pandas to Visualize SQL Analysis Services Data in Python

The CData Python Connector for SQL Analysis Services enables you use pandas and other modules to analyze and visualize live SQL Analysis Services data in Python.

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 & Matplotlib modules, and the SQLAlchemy toolkit, you can build SQL Analysis Services-connected Python applications and scripts for visualizing SQL Analysis Services data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to SQL Analysis Services data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing SQL Analysis Services through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize SQL Analysis Services Data in Python

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

engine = create_engine("ssas:///?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 resultset in a DataFrame.

df = pandas.read_sql("SELECT Fiscal_Year, Sales_Amount FROM Adventure_Works WHERE Fiscal_Year = 'FY 2008'", engine)

Visualize SQL Analysis Services Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the SQL Analysis Services data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Fiscal_Year", y="Sales_Amount")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the SQL Analysis Services Python Connector to start building Python apps and scripts with connectivity to SQL Analysis Services data. Reach out to our Support Team if you have any questions.



Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("ssas:///?User=myuseraccount&Password=mypassword&URL=http://localhost/OLAP/msmdpump.dll")
df = pandas.read_sql("SELECT Fiscal_Year, Sales_Amount FROM Adventure_Works WHERE Fiscal_Year = 'FY 2008'", engine)

df.plot(kind="bar", x="Fiscal_Year", y="Sales_Amount")
plt.show()