Use Dash to Build to Web Apps on Azure Data Catalog Data

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Azure Data Catalog Python Connector

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



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

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

Connecting to Azure Data Catalog Data

Connecting to Azure Data Catalog 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 can optionally set the following to read the different catalog data returned from Azure Data Catalog.

    CatalogName: Set this to the CatalogName associated with your Azure Data Catalog. To get your Catalog name, navigate to your Azure Portal home page > Data Catalog > Catalog Name

Connect Using OAuth Authentication

You must use OAuth to authenticate with Azure Data Catalog. OAuth requires the authenticating user to interact with Azure Data Catalog using the browser. For more information, refer to the OAuth section in the help documentation.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Azure Data Catalog

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 DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'", 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-azuredatacatalogedataplot'

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 Azure Data Catalog data and configure the app layout.

trace = go.Bar(x=df.DslAddressDatabase, y=df.Type, name='DslAddressDatabase')

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='Azure Data Catalog Tables 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 Azure Data Catalog data.

python azuredatacatalog-dash.py

Free Trial & More Information

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'", cnxn)
app_name = 'dash-azuredatacatalogdataplot'

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.DslAddressDatabase, y=df.Type, name='DslAddressDatabase')

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='Azure Data Catalog Tables Data', barmode='stack')
		})
], className="container")

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