Adobe Target Python Connector

Read, Write, and Update Adobe Target with Python

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

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

Features

Powerful metadata querying enables SQL-like access to non-database sources
Push down query optimization pushes SQL operations down to the server whenever possible, increasing performance
Client-side query execution engine, supports SQL-92 operations that are not available server-side
Connect to live Adobe Target data, for real-time data access with the Adobe Target Python Connectors
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 via the Adobe Target Connector.

Specifications

Python Database API (DB-API) Modules for Adobe Target with bi-directional access.
Write SQL, get Adobe Target data. Access Adobe Target through standard Python Database Connectivity.
Integration with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
Simple command-line based data exploration of Adobe Target Offers, Activities, Audiences, Experiences, Locations, Metrics, Properties, and more!
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 Adobe Target

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

Universal Python Adobe Target Connectivity
Easily connect to Adobe Target 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 Adobe Target 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 Adobe Target 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 Adobe Target 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 Adobe Target 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 Adobe Target with Python

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

Connecting to Adobe Target in Python

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

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

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

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

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

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

df.plot()
plt.show()