Considerations of Hadoop in the Enterprise

I am guilty. I have not been tendering this blog for quite a while now, but it feels good to be back. What have I been doing? Since leaving NetApp 2 months or so ago, I have been active in the scenes again. This time I am more aligned towards data analytics and its burgeoning impact on the storage networking segment.

I was intrigued by an article posted by a friend of mine in Facebook. The article (circa 2013) was titled “Never, ever do this to Hadoop”. It described the author’s gripe with the SAN bigots. I have encountered storage professionals who throw in the SAN solution every time, because that was all they know. NAS, to them, was like that old relative smelled of camphor oil and they avoid NAS like a plague. Similar DAS was frowned upon but how things have changed. The pendulum has swung back to DAS and new market segments such as VSANs and Hyper Converged platforms have been dominating the scene in the past 2 years. I highlighted this in my blog, “Praying to the Hypervisor God” almost 2 years ago.

I agree with the author, Andrew C. Oliver. The “locality” of resources is central to Hadoop’s performance.

Consider these 2 models:


In the model on your left (Moving Data to Compute), the delivery process from Storage to Compute is HEAVY. That is because data has dependencies; data has gravity. However, if you consider the model on your right (Moving Compute to Data), delivering data processing to the storage layer is much lighter. Compute or data processing is transient, and the data in the compute layer is volatile. Once compute’s power is turned off, everything starts again from a clean slate, hence the volatile stage.

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