Whether any of the desktop virtualization technologies are applicable to your enterprise is wholly dependent on the nature of the business. Call center and health care treatment room terminals are a relative no-brainer, but you can quickly run into problems with noncompliant applications in other implementations. As the blend of desktop virtualization technologies reaches a critical mass, the wide variety of ways to ship a Start menu to a user offers a better chance that at least one will apply in every instance. Certainly, if the world turns its back on fat clients at every desk, IT will be a happier place. As for the users, the client hypervisor may give both IT and the most ardent fat client holdouts what they need.
Why on earth would InfoWorld pick a programming framework for distributed data processing as the most important emerging technology of 2009? Because MapReduce enables enterprises to plunge into analyzing undreamed of quantities of data at commodity prices, a capability that promises to change business forever.
IDC has predicted a tenfold growth in digital information between 2006 and 2011, from just under 180 exabytes to 1,800 exabytes (that's 1 trillion and 800 billion gigabytes!). This explosion represents a challenge, of course (how to store, retrieve, and archive all that data), but also a huge opportunity for enterprises. After all, everything in that sea of data is potentially information -- information that could be used to guide business decisions.
Until recently, enterprises that might want to process petabytes of independent data to find business-relevant relationships would need an extremely good reason to invest in such a venture; the costs and time required were prohibitive. But this is quickly changing as enterprises begin to adopt highly distributed processing techniques, most notably MapReduce, a programming framework that has enabled Google, Yahoo, Facebook, MySpace, and others to process their vast data sets.
In its simplest form, MapReduce divides processing into many small blocks of work, distributes them throughout a cluster of computing nodes (typically commodity servers), and collects the results. Supporting highly scalable parallel processing, MapReduce is fast, cheap, and safe. If one node goes down, the lost work is confined to that individual node.
Google introduced the MapReduce framework in 2004, but there are many implementations today, including Apache Hadoop, Qizmt, Disco, Skynet, and Greenplum. Apache Hadoop is the leading open source implementation. Amazon taps Hadoop to offer MapReduce as an Amazon Web Service. Cloudera, which bills itself as offering "Apache Hadoop for the Enterprise," is making significant inroads.
Support for MapReduce programming is also delivered in several enterprise software products such as GigaSpaces eXtreme Application Platform, GridGain Cloud Development Platform, IBM WebSphere eXtreme Scale, and Oracle Coherence, to name a few.
The inexorable growth of data is a fact of life. As vendors drive the MapReduce framework into product offerings, we have a new window into what all those petabytes mean. It's difficult to imagine how, just 30 years ago, businesses could function without the benefit of business intelligence software or even spreadsheets. When MapReduce becomes part of the culture, business strategists in the not-too-distant future may look back on our era in the same way.
-- Savio Rodrigues
This story, "InfoWorld's Top 10 emerging enterprise technologies," was originally published at InfoWorld.com. For more on technology awards, go to InfoWorld.com.