Google BigQuery Python Connector

Connecting to BigQuery with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with BigQuery from a wide range of standard Python data tools. Connecting to and working with your data in Python follows a basic pattern, regardless of data source:

• Configure the connection properties to BigQuery
• Query BigQuery to retrieve or update data
• Connect your BigQuery data with Python data tools.

Connecting to BigQuery in Python

To connect to your data from Python, import the extension and create a connection:

import cdata.googlebigquery as mod
conn = mod.connect("[email protected]; Password=password;")

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM Dataset")
 
rs = cur.fetchall()
 
for row in rs:
print(row)

Once you import the extension, you can work with all of your enterprise data using the python modules and toolkits that you already know and love, quickly building apps that help you drive business.

Visualize BigQuery Data with pandas

The data-centric interfaces of the BigQuery Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.

engine = create_engine("googlebigquery///Password=password&User=user")

df = pandas.read_sql("SELECT * FROM Dataset", engine)

df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

BigQuery Connector goes beyond read-only functionality to deliver full support for Create, Read Update, and Delete operations (CRUD). Your end-users can interact with the data presented by the BigQuery Connector as easily as interacting with a database table.