Use Dash to Build to Web Apps on Wasabi Data

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Wasabi Python Connector

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



The CData Python Connector for Wasabi enables you to create Python applications that use pandas and Dash to build Wasabi-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 Wasabi, the pandas module, and the Dash framework, you can build Wasabi-connected web applications for Wasabi data. This article shows how to connect to Wasabi with the CData Connector and use pandas and Dash to build a simple web app for visualizing Wasabi data.

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

Connecting to Wasabi Data

Connecting to Wasabi 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.

To authorize Wasabi requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

Note: You can connect as the AWS account administrator, but it is recommended to use IAM user credentials to access AWS services.

For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

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

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

cnxn = mod.connect("AccessKey=a123;SecretKey=s123;")

Execute SQL to Wasabi

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 Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'", 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-wasabiedataplot'

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 Wasabi data and configure the app layout.

trace = go.Bar(x=df.Name, y=df.OwnerId, name='Name')

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='Wasabi Buckets 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 Wasabi data.

python wasabi-dash.py

Free Trial & More Information

Download a free, 30-day trial of the Wasabi Python Connector to start building Python apps with connectivity to Wasabi 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.wasabi as mod
import plotly.graph_objs as go

cnxn = mod.connect("AccessKey=a123;SecretKey=s123;")

df = pd.read_sql("SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'", cnxn)
app_name = 'dash-wasabidataplot'

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.Name, y=df.OwnerId, name='Name')

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='Wasabi Buckets Data', barmode='stack')
		})
], className="container")

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