Business
Making Hadoop Safe for Clusterophobics
Hadoop focuses on what it needs to do to become more consumable for the enterprise; a tooling ecosystem addressing “clusterophobia” and consumption.
This guest post comes courtesy of Tony Baer’s OnStrategies blog. Tony is a principal analyst, covering Big Data,at Ovum.
By Tony Baer 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. As we’ve previously noted, something’s got to give on the skills front. Hadoop in the enterprise faces limits – the data problems are getting bigger and more complex for sure, but resources and skills are far more finite. So we envision tools and solutions addressing two areas:
By Tony Baer 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. As we’ve previously noted, something’s got to give on the skills front. Hadoop in the enterprise faces limits – the data problems are getting bigger and more complex for sure, but resources and skills are far more finite. So we envision tools and solutions addressing two areas:
- Products that address “clusterophobia” – organizations that seeks the scalable analytics of Hadoop but lack the appetite to erect infinite data centers out in the fields or hire the necessary skillsets. Obviously, using the cloud is one option – but the questions there revolve around whether corporate policies allow maintenance of data off premises, and also, as data store size grows, whether the cloud is still economical.
- The other side of the coin is consummability – tools that simplify access to and manipulation of the data.