How to Build an ETL App for LinkedIn Ads 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 LinkedIn Ads and the petl framework, you can build LinkedIn Ads-connected applications and pipelines for extracting, transforming, and loading LinkedIn Ads data. This article shows how to connect to LinkedIn Ads with the CData Python Connector and use petl and pandas to extract, transform, and load LinkedIn Ads data.

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

Connecting to LinkedIn Ads Data

Connecting to LinkedIn Ads 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.

LinkedIn Ads uses the OAuth authentication standard. OAuth requires the authenticating user to interact with LinkedIn using the browser. See the OAuth section in the Help documentation for a guide.

After installing the CData LinkedIn Ads Connector, follow the procedure below to install the other required modules and start accessing LinkedIn Ads 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 LinkedIn Ads 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.linkedinads as mod

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query LinkedIn Ads

Use SQL to create a statement for querying LinkedIn Ads. In this article, we read data from the Analytics entity.

sql = "SELECT VisibilityCode, Comment FROM Analytics WHERE EntityId = '238'"

Extract, Transform, and Load the LinkedIn Ads Data

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

Loading LinkedIn Ads Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for LinkedIn Ads, you can work with LinkedIn Ads 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 LinkedIn Ads to start building Python apps and scripts with connectivity to LinkedIn Ads data. Reach out to our Support Team if you have any questions.

Full Source Code

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT VisibilityCode, Comment FROM Analytics WHERE EntityId = '238'"

table1 = etl.fromdb(cnxn,sql)

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

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