SQL Analysis Services Python Connector

SQL-based Access to SQL Analysis Services from Python

Easily connect Python-based Data Access, Visualization, ORM, ETL, AI/ML, and Custom Apps with SQL Analysis Services!


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Python Connector Libraries for SQL Server Analysis Services Data Connectivity. Integrate SQL Server Analysis Services with popular Python tools like Pandas, SQLAlchemy, Dash & petl. Easy-to-use Python Database API (DB-API) Modules connect SQL Analysis Services data with Python and any Python-based applications.

Features

  • Enables SQL-92 capabilities on SQL Analysis Services NoSQL data.
  • Flexible NoSQL flattening - automatic schema generation, flexible querying etc.
  • Connect to live SQL Analysis Services data, for real-time data access
  • Full support for data aggregation and complex JOINs in SQL queries
  • Secure connectivity through modern cryptography, including TLS 1.2, SHA-256, ECC, etc.
  • Seamless integration with leading BI, reporting, and ETL tools and with custom applications

Specifications

  • Python Database API (DB-API) Modules for SQL Analysis Services.
  • Write SQL, get SQL Analysis Services data. Access SQL Analysis Services through standard Python Database Connectivity.
  • Integration with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
  • Supports Direct query and MDX query capabilities
  • Full Unicode support for data, parameter, & metadata.


CData Python Connectors in Action!

Watch the video overview for a first hand-look at the powerful data integration capabilities included in the CData Python Connectors.

WATCH THE PYTHON CONNECTOR VIDEO OVERVIEW

Python Connectivity with SQL Analysis Services

Full-featured and consistent SQL access to any supported data source through Python


  • Universal Python SQL Analysis Services Connectivity

    Easily connect to SQL Analysis Services data from common Python-based frameworks, including:


    • Data Analysis/Visualization: Jupyter Notebook, pandas, Matplotlib
    • ORM: SQLAlchemy, SQLObject, Storm
    • Web Applications: Dash, Django
    • ETL: Apache Airflow, Luigi, Bonobo, Bubbles, petl
  • Popular Tooling Integration

    The SQL Analysis Services Connector integrates seamlessly with popular data science and developer tooling like Anaconda, Visual Studio Python IDE, PyCharm, and more. Real Python,

  • Replication and Caching

    Our replication and caching commands make it easy to copy data to local and cloud data stores such as Oracle, SQL Server, Google Cloud SQL, etc. The replication commands include many features that allow for intelligent incremental updates to cached data.

  • String, Date, Numeric SQL Functions

    The SQL Analysis Services Connector includes a library of 50 plus functions that can manipulate column values into the desired result. Popular examples include Regex, JSON, and XML processing functions.

  • Collaborative Query Processing

    Our Python Connector enhances the capabilities of SQL Analysis Services with additional client-side processing, when needed, to enable analytic summaries of data such as SUM, AVG, MAX, MIN, etc.

  • Easily Customizable and Configurable

    The data model exposed by our SQL Analysis Services Connector can easily be customized to add or remove tables/columns, change data types, etc. without requiring a new build. These customizations are supported at runtime using human-readable schema files that are easy to edit.

  • Enterprise-class Secure Connectivity

    Includes standard Enterprise-class security features such as TLS/ SSL data encryption for all client-server communications.

Connecting to SQL Analysis Services with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with SQL Analysis Services from a wide range of standard Python data tools. Connecting to and working with your data in Python follows a basic pattern, regardless of data source:

  • Configure the connection properties to SQL Analysis Services
  • Query SQL Analysis Services to retrieve or update data
  • Connect your SQL Analysis Services data with Python data tools.


Connecting to SQL Analysis Services in Python

To connect to your data from Python, import the extension and create a connection:

import cdata.sql analysis services as mod
conn = mod.connect("User=user@domain.com; Password=password;")

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM SSASData")
 
rs = cur.fetchall()
 
for row in rs:
print(row)

Once you import the extension, you can work with all of your enterprise data using the python modules and toolkits that you already know and love, quickly building apps that help you drive business.

Visualize SQL Analysis Services Data with pandas

The data-centric interfaces of the SQL Analysis Services Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.

engine = create_engine("sql analysis services///Password=password&User=user")

df = pandas.read_sql("SELECT * FROM SSASData", engine)

df.plot()
plt.show()