Use Dash to Build to Web Apps on Google Cloud Storage Data

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Google Cloud Storage Python Connector

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



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

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

Connecting to Google Cloud Storage Data

Connecting to Google Cloud 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.

Authenticate with a User Account

You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

Authenticate with a Service Account

Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

  • InitiateOAuth: Set this to GETANDREFRESH.
  • OAuthJWTCertType: Set this to "PFXFILE".
  • OAuthJWTCert: Set this to the path to the .p12 file you generated.
  • OAuthJWTCertPassword: Set this to the password of the .p12 file.
  • OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
  • OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
  • OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
  • ProjectId: Set this to the Id of the project you want to connect to.

The OAuth flow for a service account then completes.

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

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

cnxn = mod.connect("ProjectId='project1';InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Google Cloud 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 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-googlecloudstorageedataplot'

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 Google Cloud Storage 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='Google Cloud Storage 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 Google Cloud Storage data.

python googlecloudstorage-dash.py

Free Trial & More Information

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

cnxn = mod.connect("ProjectId='project1';InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

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

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='Google Cloud Storage Buckets Data', barmode='stack')
		})
], className="container")

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