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

Use SQLAlchemy ORMs to Access Oracle Eloqua Data in Python

The CData Python Connector for Eloqua enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Oracle Eloqua data.

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

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

Connecting to Oracle Eloqua Data

Connecting to Oracle Eloqua 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.

There are two authentication methods available for connecting to Oracle Eloqua: Login and OAuth. The Login method requires you to have the Company, User, and Password of the user.

If you do not have access to the username and password or do not wish to require them, you can use OAuth authentication. OAuth is better suited for allowing other users to access their own data. Using login credentials is better suited for accessing your own data.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Oracle Eloqua 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("oracleeloqua:///?User=user&Password=password&Company=CData")

Declare a Mapping Class for Oracle Eloqua 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 Campaign 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 Campaign(base): __tablename__ = "Campaign" Name = Column(String,primary_key=True) ActualCost = Column(String) ...

Query Oracle Eloqua 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("oracleeloqua:///?User=user&Password=password&Company=CData") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Campaign).filter_by(ShipCity="New York"): print("Name: ", instance.Name) print("ActualCost: ", instance.ActualCost) 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

Campaign_table = Campaign.metadata.tables["Campaign"] for instance in session.execute( == "New York")): print("Name: ", instance.Name) print("ActualCost: ", instance.ActualCost) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Oracle Eloqua Data

To insert Oracle Eloqua 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 Eloqua.

new_rec = Campaign(Name="placeholder", ShipCity="New York") session.add(new_rec) session.commit()

Update Oracle Eloqua Data

To update Oracle Eloqua 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 Eloqua.

updated_rec = session.query(Campaign).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.ShipCity = "New York" session.commit()

Delete Oracle Eloqua Data

To delete Oracle Eloqua 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(Campaign).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 CData Python Connector for Eloqua to start building Python apps and scripts with connectivity to Oracle Eloqua data. Reach out to our Support Team if you have any questions.