How to Build an ETL App for Adobe Target Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Adobe Target and the petl framework, you can build Adobe Target-connected applications and pipelines for extracting, transforming, and loading Adobe Target data. This article shows how to connect to Adobe Target with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData Adobe Target Connector, follow the procedure below to install the other required modules and start accessing Adobe Target through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Adobe Target Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.adobetarget as mod

You can now connect with a connection string. Use the connect function for the CData Adobe Target Connector to create a connection for working with Adobe Target data.

cnxn = mod.connect("Tenant=mycompanyname;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Adobe Target

Use SQL to create a statement for querying Adobe Target. In this article, we read data from the Activities entity.

sql = "SELECT Id, Name FROM Activities WHERE Type = 'AB'"

Extract, Transform, and Load the Adobe Target Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Adobe Target data. In this example, we extract Adobe Target data, sort the data by the Name column, and load the data into a CSV file.

Loading Adobe Target Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

etl.tocsv(table2,'activities_data.csv')

With the CData Python Connector for Adobe Target, you can work with Adobe Target data just like you would with any database, including direct access to data in ETL packages like petl.

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.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.adobetarget as mod

cnxn = mod.connect("Tenant=mycompanyname;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, Name FROM Activities WHERE Type = 'AB'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

etl.tocsv(table2,'activities_data.csv')