SQL Server Driver

Read, Write, and Update SQL Server with Python

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


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

Features

  • Connect to live Microsoft SQL Server 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 Server with bi-directional access to MS SQL-compatible databases.
  • Direct mode Access to SQL Server through standard Python Database Connectivity.
  • Broad compatibility with current and legacy SQL server versions.
  • Secure connectivity and authentication via SSL, Kerberos, Integrated Security, etc.
  • 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 Microsoft SQL Server

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


  • Universal Python SQL Server Connectivity

    Easily connect to SQL Server 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 Server 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 Server 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 Server 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 Server 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 Server with Python

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


Connecting to SQL Server in Python

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

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

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM SQLTable")
 
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 Server Data with pandas

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

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

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

df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

SQL Server Connector goes beyond read-only functionality to deliver full support for Create, Read Update, and Delete operations (CRUD). Your end-users can interact with the data presented by the SQL Server Connector as easily as interacting with a database table.