Use Dash to Build to Web Apps on Veeva Vault Data

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

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

Connecting to Veeva Vault Data

Connecting to Veeva Vault 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.

You are ready to connect after specifying the following connection properties:

• Url: The host you see in the URL after you login to your account. For example: https://my-veeva-domain.veevavault.com
• User: The username you use to login to your account.
• Password: The password you use to login to your account.

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;")

Execute SQL to Veeva Vault

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 ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'", 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-veevavaultedataplot'

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 Veeva Vault data and configure the app layout.

trace = go.Bar(x=df.ProductId, y=df.ProductName, name='ProductId')

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='Veeva Vault NorthwindProducts 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 Veeva Vault data.

python veevavault-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Veeva to start building Python apps with connectivity to Veeva Vault 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.veevavault as mod
import plotly.graph_objs as go

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;")

df = pd.read_sql("SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = '5'", cnxn)
app_name = 'dash-veevavaultdataplot'

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.ProductId, y=df.ProductName, name='ProductId')

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='Veeva Vault NorthwindProducts Data', barmode='stack')
		})
], className="container")

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