Extract, Transform, and Load Bullhorn CRM Data in Python

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Bullhorn CRM Python Connector

Python Connector Libraries for Bullhorn CRM Data Connectivity. Integrate Bullhorn CRM with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Bullhorn CRM Data

Connecting to Bullhorn CRM 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.

Begin by providing your Bullhorn CRM account credentials in the following:

If you are uncertain about your data center code, codes like CLS2, CLS21, etc. are cluster IDs that are contained in a user's browser URL (address bar) once they are logged in.

Example: https://cls21.bullhornstaffing.com/BullhornSTAFFING/MainFrame.jsp?#no-ba... indicates that the logged in user is on CLS21.

Authenticating with OAuth

Bullhorn CRM uses the OAuth 2.0 authentication standard. To authenticate using OAuth, create and configure a custom OAuth app. See the Help documentation for more information.

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

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

cnxn = mod.connect("DataCenterCode=CLS33;OAuthClientId=myoauthclientid;OAuthClientSecret=myoauthclientsecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Bullhorn CRM

Use SQL to create a statement for querying Bullhorn CRM. In this article, we read data from the Candidate entity.

sql = "SELECT Id, CandidateName FROM Candidate WHERE CandidateName = 'Jane Doe'"

Extract, Transform, and Load the Bullhorn CRM Data

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

Loading Bullhorn CRM Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Bullhorn CRM

table1 = [ ['Id','CandidateName'], ['NewId1','NewCandidateName1'], ['NewId2','NewCandidateName2'], ['NewId3','NewCandidateName3'] ]

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

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

cnxn = mod.connect("DataCenterCode=CLS33;OAuthClientId=myoauthclientid;OAuthClientSecret=myoauthclientsecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, CandidateName FROM Candidate WHERE CandidateName = 'Jane Doe'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','CandidateName'], ['NewId1','NewCandidateName1'], ['NewId2','NewCandidateName2'], ['NewId3','NewCandidateName3'] ]

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