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ODBMS Industry Watch, Nov 2, 2011, by Roberto V. Zicari
I wanted to know more on what is going on at Amazon.com in the area of Big Data and Analytics. For that, I have interviewed
Dr. Werner Vogels, Chief Technology Officer and Vice President of Amazon.com
Q1. In your keynote at the Strata Making Data Work Conference held this February in Santa Clara, California, you said that "Data and Storage should be unconstrained". What did you mean with that?
Vogels: In the old world of data analysis you knew exactly which questions you wanted to asked, which drove a very predictable collection and storage model. In the new world of data analysis your questions are going to evolve and change over time and as such you need to be able to collect, store and analyze data without being constrained by resources.
... Q4. Michael Olson of Cloudera, in a recent interview said talking about Analytical Data Platforms that
"Cloud is a deployment detail, not fundamental. Where you run your software and what software you run are two different decisions, and you need to make the right choice in both cases."
What is your opinion on this? What is in your opinion the relationships between Big Data Analysis and Cloud Computing?
Vogels: Big Data holds the promise of helping companies create a competitive advantage as through data analysis they learn how to better serve their customers. This is an approach that we have already applied for 15 years to Amazon.com and we have a solid understanding of the all the challenges around managing and processing Big Data.
One of the core concepts of Big Data is being able to evolve analytics over time. For that, a company cannot be constrained by any resource. As such, Cloud Computing and Big Data are closely linked because for a company to be able to collect, store, organize, analyze and share data, they need access to infinite resources.
AWS customers are doing some really innovative things around dealing with Big Data. For example digital advertising and marketing firm, Razorfish. Razorfish targets online adverts based on data from browsing sessions. A common issue Razorfish found was the need to process gigantic data sets. These large data sets are often the result of holiday shopping traffic on a retail website, or sudden dramatic growth on a media or social networking site.
Normally crunching these numbers would take them two days or more. By leveraging on-demand services such as Amazon Elastic MapReduce, Razorfish is able to drop their processing time to eight hours. There was no upfront investment in computing hardware, no procurement delay, and no additional operations staff hired. All this means Razorfish can offer multi-million dollar client service programs on a small business budget, helping them to increase their return on ad spend by 500%.
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