Hadoop remains a difficult platform for most enterprises to master. For now skills are still hard to come by – both for data architect or engineer, and especially for data scientists. It still takes too much skill, tape, and baling wire to get a Hadoop cluster together. Not every enterprise is Google or Facebook, with armies of software engineers that they can throw at a problem. With some exceptions, most enterprises don’t deal with data on the scale of Google or Facebook either – but the bar is rising.
If 2011 was the year that the big IT data warehouse and analytic platform brand names discovered Hadoop, 2012 becomes the year where a tooling ecosystem starts emerging to make Hadoop more consumable for the enterprise. Let’s amend that – along with tools, Hadoop must also become a first-class citizen with enterprise IT infrastructure. Hadoop won’t cross over to the enterprise if it has to be treated as some special island. That means meshing with the practices and technology approaches that enterprises are using to manage their data centers or cloud deployments. Like SQL, data integration, virtualization, storage strategy, and so on.
Admittedly, much of this cuts against the grain of early Hadoop deployment that stressed open source and commodity infrastructure. Early adopters did so out of necessity as commercial software ran out of gas for Facebook when its data warehouse daily refreshes were breaking terabyte range, not to mention that the cost of commercial licenses for such scaled out analytic platforms wouldn’t have been trivial. Anyway, Hadoop’s linearity leverages scale out of commodity blades and direct attached disk as far as the eye can see, enabling such an almost pure noncommercial approach. At the time, Google’s, Yahoo’s, and Facebook’s issues were considered rather unique – most enterprise don’t run global search engines – not to mention that their business was built on armies of software engineers.
On the fear of clusters side, players like Oracle, EMC Greenplum, and TeradataAster are already offering appliances that simplify deployment of Hadoop, typically in conjunction with an Advanced SQL analytic platform. While most vendors position this as a way for Hadoop to “extend’ your data warehouse so you perform exploration in Hadoop, but the serious analytics in SQL, we view appliances as more than transitional strategy; the workloads are going to get more equitably distributed, and in the long run, we wouldn’t be surprised to see more Hadoop-only appliances, sort of like Oracle’s (for the record, they also bundle another NoSQL database).
Also addressing the same constituency are storage and virtualization – facts of life in the data center. For Hadoop to cross over to the enterprise, it, too, must get virtualization-friendly; storage is an open question. The need for virtualization becomes even more apparent because (1) the exploratory nature of Hadoop analytics demands the ability to try out queries offline without having to disrupt or physically build a new cluster; and (2) the variable nature of Hadoop processing suggests that workloads are likely to be elastic. So we’ve been waiting for VMware to make their move. VMware – also part of EMC – has announced a pair of initiatives. First, they are working with the Apache Hadoop project to make the core pieces (HDFS and MapReduce) virtualization-aware, and separately, they are hosting their own open source project (Serengeti) for virtualizing Hadoop clusters. While Project Serengeti is not VM-specific, there’s little doubt that this will be a VMware project (we’d be shocked if the Xen folks were to buy in).
Where there’s virtualized servers, storage often closely follows. A few months back, EMC dropped the other shoe, finally unveiling a strategy for leverag ingIsilon with the Greenplum HD platform, the closest thing in NAS that replicates the scale-out model storage model popularized with Hadoop. This opens an argument of whether the scales of data in Hadoop make premium products such as Isilon unaffordable; the flip side however is the “open source tax,” where you hire the skills in your IT organization to manage and deploy scale-out storage, or pay consultants to do it for you.
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