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

How to use SQLAlchemy ORM to access Bullhorn CRM Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Bullhorn CRM data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Bullhorn CRM and the SQLAlchemy toolkit, you can build Bullhorn CRM-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Bullhorn CRM data to query, update, delete, and insert 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 CData Connector 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: 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.

Follow the procedure below to install SQLAlchemy and start accessing Bullhorn CRM through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Bullhorn CRM Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Bullhorn CRM data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("bullhorncrm:///?DataCenterCode=CLS33&OAuthClientId=myoauthclientid&OAuthClientSecret=myoauthclientsecret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Bullhorn CRM Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Candidate table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class Candidate(base): __tablename__ = "Candidate" Id = Column(String,primary_key=True) CandidateName = Column(String) ...

Query Bullhorn CRM Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("bullhorncrm:///?DataCenterCode=CLS33&OAuthClientId=myoauthclientid&OAuthClientSecret=myoauthclientsecret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Candidate).filter_by(CandidateName="Jane Doe"): print("Id: ", instance.Id) print("CandidateName: ", instance.CandidateName) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Candidate_table = Candidate.metadata.tables["Candidate"] for instance in session.execute( == "Jane Doe")): print("Id: ", instance.Id) print("CandidateName: ", instance.CandidateName) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Bullhorn CRM Data

To insert Bullhorn CRM data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Bullhorn CRM.

new_rec = Candidate(Id="placeholder", CandidateName="Jane Doe") session.add(new_rec) session.commit()

Update Bullhorn CRM Data

To update Bullhorn CRM data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Bullhorn CRM.

updated_rec = session.query(Candidate).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.CandidateName = "Jane Doe" session.commit()

Delete Bullhorn CRM Data

To delete Bullhorn CRM data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Candidate).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Bullhorn CRM to start building Python apps and scripts with connectivity to Bullhorn CRM data. Reach out to our Support Team if you have any questions.