How to use SQLAlchemy ORM to access Codat Data in Python

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 Codat-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Codat data to query Codat data.

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

Connecting to Codat Data

Connecting to Codat 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 Codat Profile on disk (e.g. C:\profiles\Codat.apip). Next, set the ProfileSettings connection property to the connection string for Codat (see below).

Codat API Profile Settings

Log into the Codat Portal, navigate to Accounts > Profile > API Key to obtain your API key.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Codat 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\Codat.apip&ProfileSettings='APIKey=your_api_key'")

Declare a Mapping Class for Codat 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 Accounts 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 Accounts(base):
	__tablename__ = "Accounts"
	Id = Column(String,primary_key=True)
	CompanyId = Column(String)
	...

Query Codat 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\Codat.apip&ProfileSettings='APIKey=your_api_key'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Accounts).filter_by(Status="Active"):
	print("Id: ", instance.Id)
	print("CompanyId: ", instance.CompanyId)
	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

Accounts_table = Accounts.metadata.tables["Accounts"]
for instance in session.execute(Accounts_table.select().where(Accounts_table.c.Status == "Active")):
	print("Id: ", instance.Id)
	print("CompanyId: ", instance.CompanyId)
	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 API Driver for Python to start building Python apps and scripts with connectivity to Codat data. Reach out to our Support Team if you have any questions.