How to use SQLAlchemy ORM to access Talkdesk Data in Python

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

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

Connecting to Talkdesk Data

Connecting to Talkdesk 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.

Talkdesk uses the OAuth 2.0 Client Credentials grant. There is no browser-based authorization step and no callback URL.

Set the following connection properties:

• AccountName: The name of your Talkdesk account.
• Region: The region where your Talkdesk instance is deployed. Supported values are US (default), EU, CA, AU, UK, and FedRamp.
• OAuthClientId: The Client Id assigned when you registered your custom OAuth application.
• OAuthClientSecret: The Client Secret assigned to your custom OAuth application.

Creating a Custom OAuth Application

1. Log in to your Talkdesk account and select OAuth Clients from the navigation menu.
2. Click Create OAuth Client and give the client a descriptive name.
3. Set Grant Type to Client Credentials.
4. Click Add scopes and select the scopes for the data you want to access.
5. Click Create and copy the Client Id and Client Secret.

When you connect, the driver automatically requests an access token from Talkdesk, caches it, and refreshes it when it expires. Make sure the scopes selected for the application match the views you plan to query, or the token request can fail.

Follow the procedure below to install SQLAlchemy and start accessing Talkdesk 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 Talkdesk Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Talkdesk 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("talkdesk:///?AccountName=myAccount&Region=US&OAuthClientId=myClientId&OAuthClientSecret=myClientSecret")

Declare a Mapping Class for Talkdesk 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 Users 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 Users(base):
	__tablename__ = "Users"
	Id = Column(String,primary_key=True)
	Name = Column(String)
	...

Query Talkdesk 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("talkdesk:///?AccountName=myAccount&Region=US&OAuthClientId=myClientId&OAuthClientSecret=myClientSecret")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Users).filter_by(Active="true"):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	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

Users_table = Users.metadata.tables["Users"]
for instance in session.execute(Users_table.select().where(Users_table.c.Active == "true")):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	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

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