How to Build an ETL App for Adobe Commerce 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 Commerce-connected applications and pipelines for extracting, transforming, and loading Adobe Commerce data. This article shows how to connect to Connect AI and use petl to extract, transform, and load Adobe Commerce 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 Commerce 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 Commerce" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Adobe Commerce.

   Adobe Commerce uses the OAuth 1 authentication standard. To connect to the Adobe Commerce REST API, obtain values for the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties by registering an app with your Adobe Commerce system. See the "Getting Started" section in the help documentation for a guide to obtaining the OAuth values and connecting.

   You will also need to provide the URL to your Adobe Commerce system. The URL depends on whether you are using the Adobe Commerce REST API as a customer or administrator.

   • Customer: To use Adobe Commerce as a customer, make sure you have created a customer account in the Adobe Commerce homepage. To do so, click Account -> Register. You can then set the URL connection property to the endpoint of your Adobe Commerce system.

   • Administrator: To access Adobe Commerce as an administrator, set CustomAdminPath instead. This value can be obtained in the Advanced settings in the Admin menu, which can be accessed by selecting System -> Configuration -> Advanced -> Admin -> Admin Base URL.

     If the Use Custom Admin Path setting on this page is set to YES, the value is inside the Custom Admin Path text box; otherwise, set the CustomAdminPath connection property to the default value, which is "admin".

   
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 Commerce 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 Commerce

Use SQL to create a statement for querying Adobe Commerce. In this article, we read data from the Products entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Adobe Commerce1).

sql = (
    "SELECT Name, Price "
    "FROM [Adobe Commerce1].[Adobe Commerce].[Products] "
    "WHERE Style = 'High Tech'"
)

Extract, Transform, and Load the Adobe Commerce Data

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

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into Adobe Commerce

When Adobe Commerce supports writes, load rows back with a batch INSERT. The SDK's executemany takes @name placeholders and a list of parameter dictionaries, one per row.

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [Adobe Commerce1].[Adobe Commerce].[Products] (Name, Price) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")

conn.close()

Note: Even for writable sources, a read-only PAT or connection permission will reject write operations.

With the CData Connect AI Python SDK, you can work with Adobe Commerce 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 Commerce data through petl using the CData Connect AI Python SDK. For more information on connecting to Adobe Commerce (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 Commerce 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 Name, Price "
    "FROM [Adobe Commerce1].[Adobe Commerce].[Products] "
    "WHERE Style = 'High Tech'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [Adobe Commerce1].[Adobe Commerce].[Products] (Name, Price) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")
conn.close()