Use Dash to Build to Web Apps on Aha! Data

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

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

Connecting to Aha! Data

Connecting to Aha! 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.

Start by setting the Profile connection property to the location of the Aha! Profile on disk (e.g. C:\profiles\aha.apip). Next, set the ProfileSettings connection property to the connection string for Aha! (see below).

Aha! API Profile Settings

The Aha! API uses OAuth-based authentication.

You will first need to register an OAuth app with Aha!. This can be done from your Aha! account under 'Settings' > 'Personal' > 'Developer' > 'OAuth Applications'. Additionally, set the Domain, found in the domain name of your Aha account. For example if your Aha account is acmeinc.aha.io, then the Domain should be 'acmeinc'.

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

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the client_id that is specified in you app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in you app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.
• Domain: Set this in the ProfileSettings to your Aha domain.

After installing the CData Aha! Connector, follow the procedure below to install the other required modules and start accessing Aha! 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 Aha! 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.api as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Aha! Connector to create a connection for working with Aha! data.

cnxn = mod.connect("Profile=C:\profiles\aha.apip;ProfileSettings='Domain=acmeinc';Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Execute SQL to Aha!

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, Name FROM Ideas WHERE AssignedToUserId = 'my_user_id'", 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-apiedataplot'

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 Aha! data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Name, 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='Aha! Ideas 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 Aha! data.

python api-dash.py

👁 Aha! data in a Dash web app (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Aha! data. Reach out to our Support Team if you have any questions.

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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.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\aha.apip;ProfileSettings='Domain=acmeinc';Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

df = pd.read_sql("SELECT Id, Name FROM Ideas WHERE AssignedToUserId = 'my_user_id'", cnxn)
app_name = 'dash-apidataplot'

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.Name, 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='Aha! Ideas Data', barmode='stack')
		})
], className="container")

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