Ready to get started?

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

Download Now

Extract, Transform, and Load SQL Analysis Services Data in Python

The CData Python Connector for SQL Analysis Services enables you to create ETL applications and pipelines for SQL Analysis Services data in Python with petl.

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 and the petl framework, you can build SQL Analysis Services-connected applications and pipelines for extracting, transforming, and loading SQL Analysis Services data. This article shows how to connect to SQL Analysis Services with the CData Python Connector and use petl and pandas to extract, transform, and load 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 petl
pip install pandas

Build an ETL App for SQL Analysis Services Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL 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 petl as etl
import pandas as pd
import cdata.ssas as mod

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;")

Create a SQL Statement to Query SQL Analysis Services

Use SQL to create a statement for querying SQL Analysis Services. In this article, we read data from the Adventure_Works entity.

sql = "SELECT Fiscal_Year, Sales_Amount FROM Adventure_Works WHERE Fiscal_Year = 'FY 2008'"

Extract, Transform, and Load the SQL Analysis Services Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SQL Analysis Services data. In this example, we extract SQL Analysis Services data, sort the data by the Sales_Amount column, and load the data into a CSV file.

Loading SQL Analysis Services Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Sales_Amount')

etl.tocsv(table2,'adventure_works_data.csv')

In the following example, we add new rows to the Adventure_Works table.

Adding New Rows to SQL Analysis Services

table1 = [ ['Fiscal_Year','Sales_Amount'], ['NewFiscal_Year1','NewSales_Amount1'], ['NewFiscal_Year2','NewSales_Amount2'], ['NewFiscal_Year3','NewSales_Amount3'] ]

etl.appenddb(table1, cnxn, 'Adventure_Works')

With the CData Python Connector for SQL Analysis Services, you can work with SQL Analysis Services data just like you would with any database, including direct access to data in ETL packages like petl.

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 petl as etl
import pandas as pd
import cdata.ssas as mod

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

sql = "SELECT Fiscal_Year, Sales_Amount FROM Adventure_Works WHERE Fiscal_Year = 'FY 2008'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Sales_Amount')

etl.tocsv(table2,'adventure_works_data.csv')

table3 = [ ['Fiscal_Year','Sales_Amount'], ['NewFiscal_Year1','NewSales_Amount1'], ['NewFiscal_Year2','NewSales_Amount2'], ['NewFiscal_Year3','NewSales_Amount3'] ]

etl.appenddb(table3, cnxn, 'Adventure_Works')