Wikibon Blog, by Jeff Kelly, June 28, 2012
Big-Data-as-a-Service Just a Start
When Google released its Big Data Platform-as-a-Service, called BigQuery, last month, the company touted its "time to insight" advantage over competing, on-premise Big Data platforms. The company boasted that customers could upload their data and be up and running Big Data Analytics via BigQuery in a matter of a day or two, versus weeks or more needed to build on-premise, custom Hadoop clusters.
Cloud-based services such as BigQuery certainly lower barriers to enterprise Big Data adoption. They eliminate the need to buy and provision hardware, to hire and train dedicated Big Data staff to keep clusters up and running optimally, and to deploy and tune Hadoop or other Big Data software. With a push of a button (or two), with Big-Data-as-a-Service enterprises can begin mining troves of data for new insights that could have profound effects on their businesses.
But Google isn't the only vendor with such offerings. Amazon Web Services has its Elastic MapReduce cloud, where users can load data into S3 and begin crunching it with Amazon's Hadoop distribution or, as of two weeks ago, either of MapR's Hadoop distributions, M3 or M5. 1010Data has coopted the Excel model for its "Trillion Row Spreadsheet" as a service offering, and Hortonworks' HDP is now available on Microsoft Azure.
Data, Data and More Data
No, Google's Big Data advantage isn't simply its cloud-based model, though that is a crucial component, as we'll see. But the search giant has another strategic asset, one that gets to the heart of Big Data ... the data itself.
... As I've written before, if you're not mashing up data sources, you're not doing Big Data Analytics. Specifically, Big Data Analytics becomes exponentially more valuable once users begin mashing up and enriching internal transactional data with third-party data sources. It's at this point that truly game-changing insights begin to emerge.
Google is nothing if not a treasure-trove of data. It indexes the entire web, knows what search terms are trending, understands user behavior, and runs millions of online ad campaigns. All this data is available to Google to sell to BigQuery customers and seamlessly integrate with internal transactional data for richer analytics.