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Python Connector Libraries for Oracle Financials Cloud Data Connectivity. Integrate Oracle Financials Cloud with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to use SQLAlchemy ORM to access Oracle Financials Cloud Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Oracle Financials Cloud data.

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

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

Connecting to Oracle Financials Cloud Data

Connecting to Oracle Financials Cloud 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.

Using Basic Authentication

You must set the following to authenticate to Oracle ERP:

  • Url: The Url of the account to connect to. Typically, the URL of your Oracle Cloud service. For example,
  • User: The username of your account.
  • Password: The password of your account.

Follow the procedure below to install SQLAlchemy and start accessing Oracle Financials Cloud 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 Oracle Financials Cloud Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Oracle Financials Cloud 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("oracleerp:///?Url=")

Declare a Mapping Class for Oracle Financials Cloud 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 Invoices 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 Invoices(base): __tablename__ = "Invoices" InvoiceId = Column(String,primary_key=True) Amount = Column(String) ...

Query Oracle Financials Cloud 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("oracleerp:///?Url=") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Invoices).filter_by(Supplier="CData Software"): print("InvoiceId: ", instance.InvoiceId) print("Amount: ", instance.Amount) 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

Invoices_table = Invoices.metadata.tables["Invoices"] for instance in session.execute( == "CData Software")): print("InvoiceId: ", instance.InvoiceId) print("Amount: ", instance.Amount) 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 Oracle Financials Cloud to start building Python apps and scripts with connectivity to Oracle Financials Cloud data. Reach out to our Support Team if you have any questions.