Hadoop

Hadoop success requires avoidance of past data mistakes

01st Nov `13, 09:51 AM in Hadoop

Twenty-one years ago, a year before the first web browser appeared, Walmart’s Teradata data warehouse exceeded a terabyte…

BDMS
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Twenty-one years ago, a year before the first web browser appeared, Walmart’s Teradata data warehouse exceeded a terabyte of data and kicked off a revolution in supply-chain analytics. Today Hadoop is doing the same for demand-chain analytics. The question is, will we just add more zeros to our storage capacity this time or will we learn from our data warehouse infrastructure mistakes?

A data silo is a system that has lots of inputs but few outputs. The Wikipedia page for “data warehouse” shows an architecture diagram with operational systems on the left, data marts on the right, and a “data vault” in the middle, but the third definition of “vault” at Merriam-Webster.com is “a burial chamber.” All too often, enterprise data warehouses have become data burial chambers, or perhaps, data hospice facilities: places where data goes to die.

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