How to use SQLAlchemy ORM to access PhantomBuster 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 PhantomBuster-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to PhantomBuster data to query PhantomBuster data.

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

Connecting to PhantomBuster Data

Connecting to PhantomBuster 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 API Key Authentication

To use the Phantombuster API, you need to obtain an API key from your Phantombuster account settings. Navigate to phantombuster.com, click your profile icon, select Settings, and copy the API key from the API section.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Phantombuster API key from the account settings page.

Multi-Organization Accounts

If your API key is associated with multiple organizations, you can target a specific organization by setting the OrganizationId connection property to the desired organization identifier. When set, it is sent as the X-Phantombuster-Org request header.

Example connection string:

Profile=C:\profiles\Phantombuster.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key_here"

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with PhantomBuster 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\Phantombuster.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key_here"")

Declare a Mapping Class for PhantomBuster 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 Agents 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 Agents(base):
	__tablename__ = "Agents"
	 = Column(String,primary_key=True)
	 = Column(String)
	...

Query PhantomBuster 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\Phantombuster.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key_here"")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Agents).filter_by(=""):
	print(": ", instance.)
	print(": ", instance.)
	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

Agents_table = Agents.metadata.tables["Agents"]
for instance in session.execute(Agents_table.select().where(Agents_table.c. == "")):
	print(": ", instance.)
	print(": ", instance.)
	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 PhantomBuster data. Reach out to our Support Team if you have any questions.