(a) Association rules are big data when you are doing them on large data sets with many variables. I work at a company that sells tens of thousands of different products and tens of millions of customers. Definitely takes us a while to compute those rules.
(b) The majority of big data is structured. For most big data projects it is typically stored in old school Oracle/Teradata/etc data warehouses and shipped into a Hadoop cluster. It may not be consolidated but it is definitely structured.
(c) The total RAM of our Hadoop cluster is 4TB and ours is small. I would consider that to be big data in the sense that it overwhelms any applications that directly try to access the raw data.
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What on earth are you talking about ?
(a) Association rules are big data when you are doing them on large data sets with many variables. I work at a company that sells tens of thousands of different products and tens of millions of customers. Definitely takes us a while to compute those rules.
(b) The majority of big data is structured. For most big data projects it is typically stored in old school Oracle/Teradata/etc data warehouses and shipped into a Hadoop cluster. It may not be consolidated but it is definitely structured.
(c) The total RAM of our Hadoop cluster is 4TB and ours is small. I would consider that to be big data in the sense that it overwhelms any applications that directly try to access the raw data.
You can stick 6TB in a single 4U Proliant from HP: http://www8.hp.com/us/en/products/servers/proliant-servers.h...
If you need a few PBs of spindle storage, hook that server up to a DDN or Panasas rack.