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The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Cvent and the SQLAlchemy toolkit, you can build Cvent-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Cvent data to query, update, delete, and insert Cvent data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Cvent data in Python. When you issue complex SQL queries from Cvent, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Cvent and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Cvent 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.
Before you can authenticate to Cvent, you must create a workspace and an OAuth application.
To create a workspace:
| event/attendees:read | event/attendees:write | event/contacts:read |
| event/contacts:write | event/custom-fields:read | event/custom-fields:write |
| event/events:read | event/events:write | event/sessions:delete |
| event/sessions:read | event/sessions:write | event/speakers:delete |
| event/speakers:read | event/speakers:write | budget/budget-items:read |
| budget/budget-items:write | exhibitor/exhibitors:read | exhibitor/exhibitors:write |
| survey/surveys:read | survey/surveys:write |
After you have set up a Workspace and invited them, developers can sign up and create a custom OAuth app. See the Creating a Custom OAuth Application section in the Help documentation for more information.
After creating an OAuth application, set the following connection properties to connect to Cvent:
Follow the procedure below to install SQLAlchemy and start accessing Cvent through Python objects.
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
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Cvent 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("cvent:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&InitiateOAuth=GETANDREFRESH")
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 Events 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 Events(base): __tablename__ = "Events" Id = Column(String,primary_key=True) Title = Column(String) ...
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.
engine = create_engine("cvent:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Events).filter_by(Virtual="true"):
print("Id: ", instance.Id)
print("Title: ", instance.Title)
print("---------")
Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.
Events_table = Events.metadata.tables["Events"]
for instance in session.execute(Events_table.select().where(Events_table.c.Virtual == "true")):
print("Id: ", instance.Id)
print("Title: ", instance.Title)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
To insert Cvent 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 Cvent.
new_rec = Events(Id="placeholder", Virtual="true") session.add(new_rec) session.commit()
To update Cvent 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 Cvent.
updated_rec = session.query(Events).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Virtual = "true" session.commit()
To delete Cvent 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(Events).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()
Download a free, 30-day trial of the CData Python Connector for Cvent to start building Python apps and scripts with connectivity to Cvent data. Reach out to our Support Team if you have any questions.
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