Use pandas to Visualize Google Data Catalog Data in Python

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 & Matplotlib modules, and the SQLAlchemy toolkit, you can build Google Data Catalog-connected Python applications and scripts for visualizing Google Data Catalog data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Google Data Catalog data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing Google Data Catalog through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Google Data Catalog Data in Python

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

engine = create_engine("googledatacatalog:///?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 resultset in a DataFrame.

df = pandas.read_sql("SELECT Type, DatasetName FROM Schemas WHERE ProjectId = 'bigquery-public-data'", engine)

Visualize Google Data Catalog Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Google Data Catalog data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Type", y="DatasetName")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Google Data Catalog to start building Python apps and scripts with connectivity to Google Data Catalog data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("googledatacatalog:///?ProjectId=YourProjectId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Type, DatasetName FROM Schemas WHERE ProjectId = 'bigquery-public-data'", engine)

df.plot(kind="bar", x="Type", y="DatasetName")
plt.show()