Use Dash to Build to Web Apps on SAS xpt 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 SASxpt, the pandas module, and the Dash framework, you can build SAS xpt-connected web applications for SAS xpt data. This article shows how to connect to SAS xpt with the CData Connector and use pandas and Dash to build a simple web app for visualizing SAS xpt data.

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

Connecting to SAS xpt Data

Connecting to SAS xpt 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.

Connecting to Local SASXpt Files

You can connect to local SASXpt file by setting the URI to a folder containing SASXpt files.

Connecting to S3 data source

You can connect to Amazon S3 source to read SASXpt files. Set the following properties to connect:

• URI: Set this to the folder within your bucket that you would like to connect to.
• AWSAccessKey: Set this to your AWS account access key.
• AWSSecretKey: Set this to your AWS account secret key.
• TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

Connecting to Azure Data Lake Storage Gen2

You can connect to ADLS Gen2 to read SASXpt files. Set the following properties to connect:

• URI: Set this to the name of the file system and the name of the folder which contacts your SASXpt files.
• AzureAccount: Set this to the name of the Azure Data Lake storage account.
• AzureAccessKey: Set this to our Azure DataLakeStore Gen 2 storage account access key.
• TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

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

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

cnxn = mod.connect("URI=C:/folder;")

Execute SQL to SAS xpt

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, Column1 FROM SampleTable_1 WHERE Column2 = '100'", 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-sasxptedataplot'

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 SAS xpt data and configure the app layout.

trace = go.Bar(x=df.Id, y=df.Column1, 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='SAS xpt SampleTable_1 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 SAS xpt data.

python sasxpt-dash.py

👁 SAS xpt data in a Dash web app (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SASxpt to start building Python apps with connectivity to SAS xpt 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.sasxpt as mod
import plotly.graph_objs as go

cnxn = mod.connect("URI=C:/folder;")

df = pd.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'", cnxn)
app_name = 'dash-sasxptdataplot'

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.Column1, 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='SAS xpt SampleTable_1 Data', barmode='stack')
		})
], className="container")

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