How to Build an ETL App for Adobe Experience Manager Data in Python with CData Connect AI

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

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to Adobe Experience Manager in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Adobe Experience Manager" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Adobe Experience Manager.

   The driver connects to Adobe Experience Manager (AEM) instances that expose the JCR repository over WebDAV. It supports both on-premises AEM and AEM as a Cloud Service deployments.

   To establish a connection, set the following properties:

   • URL: The WebDAV-enabled JCR server URL.
     • AEM as a Cloud Service: https://author-pXXXXX-eXXXXX.adobeaemcloud.com/crx/server
     • Local development: http://localhost:4502/crx/server
   • User: Your AEM username.
   • Password: Your AEM password.

   Note: Tables are dynamically generated based on the JCR repository structure. Ensure that the configured user has sufficient permissions to access the required content paths in the AEM repository.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for Adobe Experience Manager Data in Python

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

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query Adobe Experience Manager

Use SQL to create a statement for querying Adobe Experience Manager. In this article, we read data from the Content entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, AdobeExperienceManager1).

sql = (
    "SELECT Id, Name "
    "FROM [AdobeExperienceManager1].[AdobeExperienceManager].[Content] "
    "WHERE Name = 'example'"
)

Extract, Transform, and Load the Adobe Experience Manager Data

With a connection and query in hand, use petl to extract, transform, and load the Adobe Experience Manager data. In this example, we extract Adobe Experience Manager data, sort the data by the Name column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

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

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

Adobe Experience Manager is a read-only source in Connect AI, so this pipeline can extract and transform Adobe Experience Manager data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with Adobe Experience Manager data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live Adobe Experience Manager data through petl using the CData Connect AI Python SDK. For more information on connecting to Adobe Experience Manager (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Adobe Experience Manager data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT Id, Name "
    "FROM [AdobeExperienceManager1].[AdobeExperienceManager].[Content] "
    "WHERE Name = 'example'"
)

table1 = etl.fromdb(conn, sql)

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

etl.tocsv(table2, 'content_data.csv')
conn.close()