Google BigQuery ODBC Driver for Python Connection

Accessing the Google BigQuery database can be fast and seamless by using the Open Database Connectivity (ODBC) solution. Providing full interoperability, the ODBC driver for Google BigQuery allows you to access live Google BigQuery data directly from Python. It enables you to retrieve data to Python, and review and modify these data right in this application.

Google BigQuery Key Features

Open source
Low memory leakage problem
Support for multiple table types
Platform Independent

Python Key Features

Extensive libraries and frameworks
Data manipulation and analysis
Web development and APIs
Machine learning and data science

Direct Connection

Our data connector enables various ODBC-aware applications to establish a direct connection to Google BigQuery via TCP/IP to eliminate the need for a Google BigQuery client. A direct connection increases the speed of data transmission between Python and Google BigQuery for real-time analytics. It streamlines the deployment process since there is no need to distribute any additional client-side software with the ODBC driver for Google BigQuery.

What are some reasons to choose Devart ODBC Driver for Google BigQuery?

Integration

ODBC Driver for Google BigQuery is compatible with Python allowing extraction of data by executing SQL statements.

Platforms Variety

ODBC Driver for Google BigQuery can be used with 32-bit and 64-bit applications on both x32 and x64 platforms, so there is no need to additionally configure the driver, applications, or environment.

Fully Unicode-Compliant Driver

With our fully Unicode-compliant driver, you can properly retrieve and modify any data in multilingual Google BigQuery databases, regardless of their character set: Latin, Cyrillic, Hebrew, Chinese, etc., and in any language environment.

How to download, install, and configure ODBC Driver for Google BigQuery?

Windows

Step 1. Download and install the driver

Download the driver. Run the installer and follow the instructions in the wizard. Open ODBC Data Sources. In the System DSN tab, click Add. Select the needed driver and click Finish.

Step 2. Activate the driver

In the License tab of the Configuration dialog, click Input Activation Key and enter your key. Click OK.

Step 3. Configure the Data Source Name (DSN)

In the General tab of the Configuration dialog, fill in the fields with the database connection data. To test the connectivity, click Test Connection. To save the DSN, click OK.

Steps to connect to Google BigQuery database in Python
with pyodbc module

01.Import the pyodbc module and create a connection to the database.

import pyodbc 
cnxn = pyodbc.connect('DRIVER={Devart ODBC Driver for SQLite};Direct=True;Database=mydatabase')

02.Execute an INSERT statement to test the connection to the database.

cursor = cnxn.cursor()  
cursor.execute("INSERT INTO EMP (EMPNO, ENAME, JOB, MGR) VALUES (535, 'Scott', 'Manager', 545)")

03.Retrieve a result set from a query, iterate over it and print out all records.

cursor.execute("SELECT * FROM EMP")
row = cursor.fetchone() 
while row: 
    print (row)
    row = cursor.fetchone()

Advantages of Connectivity to Google BigQuery from Python via ODBC Driver

Secure Connection

Every operation with Google BigQuery becomes significantly faster using such capabilities of our driver as local data caching, connection pooling, and much more.

Working with Python using Multiple Functions

Using Python, you can create tables, add data, select, delete, sort, edit, combine data from several database tables, and find unique values quickly and easily.

ODBC Conformance

The driver fully supports the ODBC interface, its data types, and functions. It offers advanced connection string parameters and allows any ODBC-compliant desktop or web application to connect to Google BigQuery from Python on various platforms.

Simple SQL Querying on Python Files

Python processes SQL through its SQL dialect. Using ODBC connection, it is simple to execute different SQL queries to retrieve entire tables or specific columns from Excel, filter data, formate data, numbers, etc.

Cost-Effective Deployment

Installing the driver is easy with the standalone installation file. There is no need in deploying and configuring any additional software which results in a reduction in deployment costs.

Sharing Your Data with Python

You can share your data with other users in Python quickly and easily. Upload your Python data to the SharePoint lists so that it can be available for you and others to use to create reports, data models, dashboards, etc.