Use Dash to Build to Web Apps on Azure Data Lake Storage Data

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Azure Data Lake Storage Python Connector

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



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

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

Connecting to Azure Data Lake Storage Data

Connecting to Azure Data Lake Storage 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.

Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Azure AD for authentication.

For this, an Active Directory web application is required. You can create one as follows:

  1. Sign in to your Azure Account through the .
  2. Select "Azure Active Directory".
  3. Select "App registrations".
  4. Select "New application registration".
  5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
  6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
  7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

  • Schema: Set this to ADLSGen1.
  • Account: Set this to the name of the account.
  • OAuthClientId: Set this to the application Id of the app you created.
  • OAuthClientSecret: Set this to the key generated for the app you created.
  • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
  • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Authenticating to a Gen 2 DataLakeStore Account

To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

  • Schema: Set this to ADLSGen2.
  • Account: Set this to the name of the account.
  • FileSystem: Set this to the file system which will be used for this account.
  • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
  • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

After installing the CData Azure Data Lake Storage Connector, follow the procedure below to install the other required modules and start accessing Azure Data Lake Storage 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 Lake Storage 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.adls 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 Lake Storage Connector to create a connection for working with Azure Data Lake Storage data.

cnxn = mod.connect("Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Azure Data Lake Storage

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 FullPath, Permission FROM Resources WHERE Type = 'FILE'", 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-adlsedataplot'

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 Lake Storage data and configure the app layout.

trace = go.Bar(x=df.FullPath, y=df.Permission, name='FullPath')

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 Lake Storage Resources 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 Lake Storage data.

python adls-dash.py

Free Trial & More Information

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

cnxn = mod.connect("Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT FullPath, Permission FROM Resources WHERE Type = 'FILE'", cnxn)
app_name = 'dash-adlsdataplot'

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.FullPath, y=df.Permission, name='FullPath')

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 Lake Storage Resources Data', barmode='stack')
		})
], className="container")

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