Tuesday, May 1, 2012
By Ju-kay Kwek, Product Manager, BigQuery
BigQuery enables businesses and developers to gain real-time business insights from massive amounts of data without any upfront hardware or software investments. Imagine a big pharmaceutical company optimizing daily marketing spend using worldwide sales and advertisement data. Or think of a small online retailer that makes product recommendations based on user clicks. Today, we are making BigQuery publicly available, an important milestone in our effort to bring Big Data analytics to all businesses via the cloud.
Since announcing BigQuery in limited preview last November, many businesses and developers have started using it for real-time Big Data analytics in the cloud. Claritics, a social and mobile analytics company, built a web application for game developers to gain real-time insights into user behavior. Crystalloids, an Amsterdam-based analytics firm, built a cloud-based application to help a resort network analyze customer reservations, optimize marketing and maximize revenue. This just scratches the surface of use cases for BigQuery.
BigQuery is accessible via a simple UI or REST interface. It lets you take advantage of Google’s massive compute power, store as much data as needed and pay only for what you use. Your data is protected with multiple layers of security, replicated across multiple data centers and can be easily exported.
Developers and businesses can sign up for BigQuery online and query up to 100 GB of data per month for free. See our introductory pricing plan for storing and querying datasets of up to 2 TB. If you need more than that, contact a sales representative.
We hope you will be able to gain real-time business insights using BigQuery. Share your BigQuery use cases and feedback in our user forums or on our +Google Enterprise page.
Ju-kay Kwek is the Product Management Lead for Google's Cloud Big Data initiative. In this role, he focuses on creating services that enable businesses and developers to harness Google's unparalleled data processing infrastructure and algorithms to tackle Big Data needs.
Posted by Scott Knaster, Editor