Use Dash to Build to Web Apps on Raisers Edge NXT Data

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Raisers Edge NXT Python Connector

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



The CData Python Connector for Raisers Edge NXT enables you to create Python applications that use pandas and Dash to build Raisers Edge NXT-connected web apps.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Raisers Edge NXT, the pandas module, and the Dash framework, you can build Raisers Edge NXT-connected web applications for Raisers Edge NXT data. This article shows how to connect to Raisers Edge NXT with the CData Connector and use pandas and Dash to build a simple web app for visualizing 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 driver 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.

After installing the CData Raisers Edge NXT Connector, follow the procedure below to install the other required modules and start accessing Raisers Edge NXT through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Raisers Edge NXT Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.raiseredgenxt as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Raisers Edge NXT Connector to create a connection for working with Raisers Edge NXT data.

cnxn = mod.connect("SubscriptionKey=MySubscriptionKey;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Raisers Edge NXT

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT Id, AddressLines FROM Constituents WHERE Type = 'Home'", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-raiseredgenxtedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Raisers Edge NXT data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.AddressLines, name='Id')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Raisers Edge NXT Constituents Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Raisers Edge NXT data.

python raiseredgenxt-dash.py

Free Trial & More Information

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



Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.raiseredgenxt as mod
import plotly.graph_objs as go

cnxn = mod.connect("SubscriptionKey=MySubscriptionKey;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, AddressLines FROM Constituents WHERE Type = 'Home'", cnxn)
app_name = 'dash-raiseredgenxtdataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.Id, y=df.AddressLines, name='Id')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Raisers Edge NXT Constituents Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)