MongoDB Python Connector

Read, Write, and Update MongoDB with Python

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


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

Features

  • Unmatched performance for reading large datasets
  • Compatible with MongoDB 2.6 and above
  • Enables SQL-92 capabilities on MongoDB NoSQL data.
  • Flexible NoSQL flattening - automatic schema generation, flexible querying etc.
  • Connect to live MongoDB data, for real-time data access
  • Full support for data aggregation and complex JOINs in SQL queries
  • Seamless integration with leading BI, reporting, and ETL tools and with custom applications

Specifications

  • Python Database API (DB-API) Modules for MongoDB with bi-directional access.
  • Write SQL, get MongoDB data. Access MongoDB through standard Python Database Connectivity.
  • Integration with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
  • An easy-to-use 'flattened' interface for working with MongoDB document databases.
  • 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 MongoDB

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


  • Universal Python MongoDB Connectivity

    Easily connect to MongoDB 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 MongoDB 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 MongoDB 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 MongoDB 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 MongoDB 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 MongoDB with Python

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


Connecting to MongoDB in Python

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

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

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

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

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

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

df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

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