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

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

   Adobe Analytics uses the OAuth authentication standard. To authenticate using OAuth, create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the "Getting Started" section of the help documentation for a guide.

   Retrieving GlobalCompanyId

   GlobalCompanyId is a required connection property. If you do not know your Global Company ID, you can find it in the request URL for the users/me endpoint on the Swagger UI. After logging into the Swagger UI Url, expand the users endpoint and then click the GET users/me button. Click the Try it out and Execute buttons. Note your Global Company ID shown in the Request URL immediately preceding the users/me endpoint.

   Retrieving Report Suite Id

   Report Suite ID (RSID) is also a required connection property. In the Adobe Analytics UI, navigate to Admin -> Report Suites and you will get a list of your report suites along with their identifiers next to the name.

   After setting the GlobalCompanyId, RSID and OAuth connection properties, you are ready to connect to Adobe Analytics.

   
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 Analytics 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 Analytics

Use SQL to create a statement for querying Adobe Analytics. In this article, we read data from the AdsReport entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, AdobeAnalytics1).

sql = (
    "SELECT Page, PageViews "
    "FROM [AdobeAnalytics1].[AdobeAnalytics].[AdsReport] "
    "WHERE City = 'Chapel Hill'"
)

Extract, Transform, and Load the Adobe Analytics Data

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

table1 = etl.fromdb(conn, sql)

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

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

Adobe Analytics is a read-only source in Connect AI, so this pipeline can extract and transform Adobe Analytics 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 Analytics 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 Analytics data through petl using the CData Connect AI Python SDK. For more information on connecting to Adobe Analytics (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 Analytics 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 Page, PageViews "
    "FROM [AdobeAnalytics1].[AdobeAnalytics].[AdsReport] "
    "WHERE City = 'Chapel Hill'"
)

table1 = etl.fromdb(conn, sql)

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

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