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

Use SQLAlchemy ORMs to Access Oracle Sales Data in Python

The CData Python Connector for Oracle Sales enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Oracle Sales 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 Sales and the SQLAlchemy toolkit, you can build Oracle Sales-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Oracle Sales data to query, update, delete, and insert Oracle Sales data.

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

Connecting to Oracle Sales Data

Connecting to Oracle Sales 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.

Oracle Sales uses Basic authentication over SSL; after setting the following connection properties, you are ready to connect:

  • Username: Set this to the user name that you use to log into your Oracle Cloud service.
  • Password: Set this to your password.
  • HostURL: Set this to the Web address (URL) of your Oracle Cloud service.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Oracle Sales 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("oraclesalescloud:///?HostURL= Username=abc123& Password=abcdef")

Declare a Mapping Class for Oracle Sales 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 Opportunities 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 Opportunities(base): __tablename__ = "Opportunities" OptyId = Column(String,primary_key=True) Name = Column(String) ...

Query Oracle Sales 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("oraclesalescloud:///?HostURL= Username=abc123& Password=abcdef") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Opportunities).filter_by(CreatedBy="Jack"): print("OptyId: ", instance.OptyId) 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

Opportunities_table = Opportunities.metadata.tables["Opportunities"] for instance in session.execute( == "Jack")): print("OptyId: ", instance.OptyId) 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.

Insert Oracle Sales Data

To insert Oracle Sales data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Oracle Sales.

new_rec = Opportunities(OptyId="placeholder", CreatedBy="Jack") session.add(new_rec) session.commit()

Update Oracle Sales Data

To update Oracle Sales data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Oracle Sales.

updated_rec = session.query(Opportunities).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.CreatedBy = "Jack" session.commit()

Delete Oracle Sales Data

To delete Oracle Sales data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Opportunities).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

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