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

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

Connecting to Google Data Catalog Data

Connecting to Google 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.

Google Data Catalog uses the OAuth authentication standard. Authorize access to Google APIs on behalf on individual users or on behalf of users in a domain.

Before connecting, specify the following to identify the organization and project you would like to connect to:

• OrganizationId: The ID associated with the Google Cloud Platform organization resource you would like to connect to. Find this by navigating to the cloud console.

  Click the project selection drop-down, and select your organization from the list. Then, click More -> Settings. The organization ID is displayed on this page.

• ProjectId: The ID associated with the Google Cloud Platform project resource you would like to connect to.

  Find this by navigating to the cloud console dashboard and selecting your project from the Select from drop-down. The project ID will be present in the Project info card.

When you connect, the OAuth endpoint opens in your default browser. Log in and grant permissions to the application to completes the OAuth process. For more information, refer to the OAuth section in the Help documentation.

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

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

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

Execute SQL to Google 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 Type, DatasetName FROM Schemas WHERE ProjectId = 'bigquery-public-data'", 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-googledatacatalogedataplot'

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 Data Catalog data and configure the app layout.

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

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 Data Catalog Schemas 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 Data Catalog data.

python googledatacatalog-dash.py

Free Trial & More Information

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

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

df = pd.read_sql("SELECT Type, DatasetName FROM Schemas WHERE ProjectId = 'bigquery-public-data'", cnxn)
app_name = 'dash-googledatacatalogdataplot'

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

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

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