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Get the Report →How to use SQLAlchemy ORM to access Zoom Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Zoom data.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData API Driver for Python and the SQLAlchemy toolkit, you can build Zoom-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Zoom data to query Zoom data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Zoom data in Python. When you issue complex SQL queries from Zoom, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Zoom and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Zoom Data
Connecting to Zoom 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.
Start by setting the Profile connection property to the location of the Zoom Profile on disk (e.g. C:\profiles\Zoom.apip). Next, set the ProfileSettings connection property to the connection string for Zoom (see below).
Zoom API Profile Settings
To authenticate to Zoom, you can use the OAuth standard to connect to your own data or to allow other users to connect to their data.
First you will need to create an OAuth app. To do so, navigate to https://marketplace.zoom.us/develop/create and click Create under the OAuth section. Select whether or not the app will be for individual users or for the entire account, and uncheck the box to publish the app. Give the app a name and click Create. You will then be given your Client Secret and Client ID
After setting the following connection properties, you are ready to connect:
- AuthScheme: Set this to OAuth.
- InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
- OAuthClientID: Set this to the OAuth Client ID that is specified in your app settings.
- OAuthClientSecret: Set this to the OAuth Client Secret that is specified in your app settings.
- CallbackURL: Set this to the Redirect URI you specified in your app settings.
Follow the procedure below to install SQLAlchemy and start accessing Zoom 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 Zoom Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Zoom 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("api:///?Profile=C:\profiles\Zoom.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
Declare a Mapping Class for Zoom 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 MeetingRegistrants 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 MeetingRegistrants(base):
__tablename__ = "MeetingRegistrants"
Id = Column(String,primary_key=True)
JobTitle = Column(String)
...
Query Zoom 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("api:///?Profile=C:\profiles\Zoom.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(MeetingRegistrants).filter_by(State="NC"):
print("Id: ", instance.Id)
print("JobTitle: ", instance.JobTitle)
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
MeetingRegistrants_table = MeetingRegistrants.metadata.tables["MeetingRegistrants"]
for instance in session.execute(MeetingRegistrants_table.select().where(MeetingRegistrants_table.c.State == "NC")):
print("Id: ", instance.Id)
print("JobTitle: ", instance.JobTitle)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Free Trial & More Information
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