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Get the Report →Use Dash to Build to Web Apps on Raisers Edge NXT Data
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 CData Python Connector for Raisers Edge NXT 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)