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Python Connector Libraries for Raisers Edge NXT Data Connectivity. Integrate Raisers Edge NXT with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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How to use SQLAlchemy ORM to access Raisers Edge NXT Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Raisers Edge NXT data.

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

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

Connecting to Raisers Edge NXT Data

Connecting to Raisers Edge NXT 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.

Before establishing a connection, supply the SubscriptionKey, found in the Blackbaud Raiser's Edge NXT Profile.

Authenticating to Raiser's Edge NXT

Blackbaud Raiser's Edge NXT uses the OAuth authentication standard. You can connect to without setting any connection properties using the embedded OAuth credentials.

Alternatively, you can authenticate by creating a custom app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties.

See the Help documentation for an authentication guide.

Follow the procedure below to install SQLAlchemy and start accessing Raisers Edge NXT 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 Raisers Edge NXT Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Raisers Edge NXT 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("raiseredgenxt:///?SubscriptionKey=MySubscriptionKey&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Raisers Edge NXT 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 Constituents 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 Constituents(base): __tablename__ = "Constituents" Id = Column(String,primary_key=True) AddressLines = Column(String) ...

Query Raisers Edge NXT 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("raiseredgenxt:///?SubscriptionKey=MySubscriptionKey&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Constituents).filter_by(Type="Home"): print("Id: ", instance.Id) print("AddressLines: ", instance.AddressLines) 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

Constituents_table = Constituents.metadata.tables["Constituents"] for instance in session.execute(Constituents_table.select().where(Constituents_table.c.Type == "Home")): print("Id: ", instance.Id) print("AddressLines: ", instance.AddressLines) 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 Raisers Edge NXT to start building Python apps and scripts with connectivity to Raisers Edge NXT data. Reach out to our Support Team if you have any questions.