How to Visualize SQL Analysis Services Data in Python with pandas via CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas and Matplotlib modules, you can build SQL Analysis Services-connected Python applications and scripts for visualizing SQL Analysis Services data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query SQL Analysis Services data and visualize the results.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

Connect to SQL Analysis Services in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "SQL Analysis Services" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SQL Analysis Services.

   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.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize SQL Analysis Services Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, SSAS1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Query SQL Analysis Services with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT Fiscal_Year, Sales_Amount "
    "FROM [SSAS1].[SSAS].[Adventure_Works] "
    "WHERE Fiscal_Year = 'FY 2008'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize SQL Analysis Services Data

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

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

conn.close()

More Information and Free Trial

Now you can read live SQL Analysis Services data into pandas through the CData Connect AI Python SDK. For more information on connecting to SQL Analysis Services (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live SQL Analysis Services data in Python.

Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pandas.read_sql(
    "SELECT Fiscal_Year, Sales_Amount "
    "FROM [SSAS1].[SSAS].[Adventure_Works] "
    "WHERE Fiscal_Year = 'FY 2008'",
    conn,
)

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

conn.close()