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Extract, Transform, and Load LinkedIn Data in Python

The CData Python Connector for LinkedIn enables you to create ETL applications and pipelines for LinkedIn data in Python with petl.

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

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

Connecting to LinkedIn Data

Connecting to LinkedIn 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 uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with LinkedIn. For more information refer to our authentication guide.

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

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

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

Create a SQL Statement to Query LinkedIn

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

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

Extract, Transform, and Load the LinkedIn Data

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

Loading LinkedIn Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the CompanyStatusUpdates table.

Adding New Rows to LinkedIn

table1 = [ ['VisibilityCode','Comment'], ['NewVisibilityCode1','NewComment1'], ['NewVisibilityCode2','NewComment2'], ['NewVisibilityCode3','NewComment3'] ]

etl.appenddb(table1, cnxn, 'CompanyStatusUpdates')

With the CData Python Connector for LinkedIn, you can work with LinkedIn 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 LinkedIn Python Connector to start building Python apps and scripts with connectivity to LinkedIn 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.linkedin as mod

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

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

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['VisibilityCode','Comment'], ['NewVisibilityCode1','NewComment1'], ['NewVisibilityCode2','NewComment2'], ['NewVisibilityCode3','NewComment3'] ]

etl.appenddb(table3, cnxn, 'CompanyStatusUpdates')