How to use SQLAlchemy ORM to access Adobe Target Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Adobe Target and the SQLAlchemy toolkit, you can build Adobe Target-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Adobe Target data to query Adobe Target data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Adobe Target data in Python. When you issue complex SQL queries from Adobe Target, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Adobe Target and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Adobe Target Data

Connecting to Adobe Target data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

To connect to Adobe Target, you must provide the Tenant property along with OAuth connection properties mentioned below. Note that while other connection properties can influence processing behavior, they do not affect the ability to connect.

To determine your Tenant name:

1. Log in to Adobe Experience. The URL will look similar to: "https://experience.adobe.com/#/@mycompanyname/preferences/general-section".
2. Extract the value after the "/#/@". In this example, it is "mycompanyname".
3. Set the Tenant connection property to that value.

User Accounts (OAuth)

You must set AuthScheme to OAuthClient for all user account flows.

Note: Adobe authentication via OAuth requires updating your token every two weeks.

All Applications

CData provides an embedded OAuth application that simplifies OAuth authentication. Alternatively, you can create a custom OAuth application. Review Creating a Custom OAuth App in the Help documentation for more information.

Obtaining the OAuth Access Token

Set the following properties to connect:

• InitiateOAuth: Set to GETANDREFRESH to automatically perform the OAuth exchange and refresh the OAuthAccessToken as needed.
• OAuthClientId : Set to the client Id assigned when you registered your app.
• OAuthClientSecret : Set to the client secret assigned when you registered your app.
• CallbackURL : Set to the redirect URI defined when you registered your app. For example: https://localhost:3333

With these settings, the provider obtains an access token from Adobe Target, which it uses to request data. The OAuth values are stored in the location specified by OAuthSettingsLocation, ensuring they persist across connections.

Follow the procedure below to install SQLAlchemy and start accessing Adobe Target through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Adobe Target Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Adobe Target data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("adobetarget:///?Tenant=mycompanyname&InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for Adobe Target Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Activities table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Activities(base):
	__tablename__ = "Activities"
	Id = Column(String,primary_key=True)
	Name = Column(String)
	...

Query Adobe Target Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("adobetarget:///?Tenant=mycompanyname&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Activities).filter_by(Type="AB"):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Activities_table = Activities.metadata.tables["Activities"]
for instance in session.execute(Activities_table.select().where(Activities_table.c.Type == "AB")):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Adobe Target to start building Python apps and scripts with connectivity to Adobe Target data. Reach out to our Support Team if you have any questions.