How to use SQLAlchemy ORM to access Anaplan 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 Anaplan and the SQLAlchemy toolkit, you can build Anaplan-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Anaplan data to query Anaplan data.

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

Connecting to Anaplan Data

Connecting to Anaplan 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.

Authenticating to Anaplan

The driver supports authenticating with Basic, Certificate, or OAuth. In every case, set Region to the region where your Anaplan account data is hosted (e.g., US1, which is the default).

Using Basic Authentication

Set AuthScheme to Basic, then supply your Anaplan User and Password. If your workspace uses single sign-on (SSO), you must be assigned as an Exception User to use Basic authentication.

Using Certificate Authentication

Set AuthScheme to Certificate, then supply the Certificate, CertificateType, and PrivateKey properties (and the matching CertificatePassword / PrivateKeyPassword if either is encrypted). The certificate must be a CA-issued X.509 certificate registered with your Anaplan tenant administrator.

Using OAuth Authentication

Register a custom OAuth application in Anaplan, then set the following properties:

• OAuthClientId: The client Id assigned when you registered your custom OAuth application.
• OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.
• CallbackURL: The redirect URI defined when you registered your application.
• InitiateOAuth: Set to GETANDREFRESH to have the driver manage the OAuth token exchange and refresh automatically.

See the Getting Started chapter of the help documentation for a guide to creating a custom OAuth app and using OAuth.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Anaplan 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("anaplan:///?OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackURL=your_callback_url&Region=US1&InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for Anaplan 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 Sales 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 Sales(base):
	__tablename__ = "Sales"
	Region = Column(String,primary_key=True)
	Product = Column(String)
	...

Query Anaplan 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("anaplan:///?OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackURL=your_callback_url&Region=US1&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Sales).filter_by(Value="100"):
	print("Region: ", instance.Region)
	print("Product: ", instance.Product)
	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

Sales_table = Sales.metadata.tables["Sales"]
for instance in session.execute(Sales_table.select().where(Sales_table.c.Value == "100")):
	print("Region: ", instance.Region)
	print("Product: ", instance.Product)
	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 Anaplan to start building Python apps and scripts with connectivity to Anaplan data. Reach out to our Support Team if you have any questions.