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Storage and Availability Management
Showing posts tagged with analytics
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Rags Srinivasan | 25 May 2012 | 1 comment

The biggest driver for adoption of Hadoop is its promise of unlocking value from an enterprise’s vast data store. Use cases that show incremental revenue from data analysis are very well publicized.  Every organization strives to achieve that and wants to leverage the power of data analytics to drive its revenues. Promises aside, Hadoop storage has severe issues that calls  into question its place in the enterprise datacenter.

  1. Increased storage and server sprawl – Hadoop cluster is built  with numerous commodity hosts, each with its own direct attached storage. Just when datacenter architects have spent considerable time and resources consolidating their datacenters and reducing footprint through server consolidation, virtualization and private cloud,  Hadoop requires them to build out a massively parallel system with hundreds or even thousands of compute nodes. Managing these numerous nodes and keeping them up to date with right...