Azure Analysis Services Python Connector

SQL-based Access to Azure Analysis Services from Python

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


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

Features

  • SQL access to the Azure Analysis Services data models
  • Enables SQL-92 capabilities on Azure Analysis Services NoSQL data.
  • Flexible NoSQL flattening - automatic schema generation, flexible querying etc.
  • 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 Azure Analysis Services .
  • Write SQL, get Azure Analysis Services data. Access Azure Analysis Services through standard Python Database Connectivity.
  • Integration with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
  • 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 Azure Analysis Services

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


  • Universal Python Azure Analysis Services Connectivity

    Easily connect to Azure 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 Azure 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 Azure 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 Azure 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 Azure 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 Azure Analysis Services with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with Azure 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 Azure Analysis Services
  • Query Azure Analysis Services to retrieve or update data
  • Connect your Azure Analysis Services data with Python data tools.


Connecting to Azure Analysis Services in Python

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

import cdata.azure 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 AnalysisServicesData")
 
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 Azure Analysis Services Data with pandas

The data-centric interfaces of the Azure 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("azure analysis services///Password=password&User=user")

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

df.plot()
plt.show()