Ask any data warehouse architect what is driving the “big data” craze and he’ll tell you it has to do with the cost of storage and the advancements in distributed computing and most likely will mention Hadoop. Most enterprise data warehouses are constrained by cost and scalability of relational databases.
By now we have all heard how Yahoo uses Hadoop to optimize the user experience by ad and content targeting. We know that Hadoop is well suited for analysis or processing that can be distributed in a parallel fashion on multiple nodes. We know it’s great for managing big data.
Volume, Variety, Velocity – how many times have you heard that lately? The “3 V’s” are commonly used to describe big data by various vendors and analysts. Forrester extends this by introducing “Variability” as a 4th V – this addresses the fact that you need to design for agility due
The term “big data” is all the rage right now, however the term “big” is relative. At SAS we have been called on to do “big data” projects and more importantly “big analytics” projects for many years now. In fact, we are the pioneers of analytics on “big data.” There is
