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Databricks Python Connector
 

Databricks Python Connector

Read, Write, and Update Databricks with Python

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


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

Features

  • SQL access to Databricks lakehouses
  • Connect to live Databricks 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 Databricks with bi-directional access.
  • Write SQL, get Databricks data. Access Databricks 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 Databricks

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


  • Universal Python Databricks Connectivity

    Easily connect to Databricks 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 Databricks 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 Databricks 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 Databricks 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 Databricks 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 Databricks with Python

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


Connecting to Databricks in Python

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

import cdata.databricks as mod
conn = mod.connect("[email protected]; Password=password;")

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

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

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

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

df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

Databricks 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 Databricks Connector as easily as interacting with a database table.

Popular Python Videos:

Python Connectors, Jupyter Notebook, and pandas