NextGen Hadoop, beyond MapReduce


Hadoop has run into architectural limitations and the community has started working on the Next Generation Hadoop [NGN Hadoop]. NGN Hadoop has some new management features of which multi-tenant application management is the major one. However the key change is that MapReduce no longer is entangled inside the rest of Hadoop. This will allow Hadoop to be used for MPI, Machine Learning, Master-Worker, Iterative Processing, Graph Processing, etc. New tools to better manage Hadoop are also being incubated, e.g. Ambari and HCatalog.

Why is this important for telecom?
Having one platform that allows massive data storage, peta-byte data analytics, complex parallel computations, large-scale machine learning, big data map reduce processing, etc. all in one multi-tenant set-up means that telecom operators could see massive reductions in their architecture costs together with faster go-to-market, better data intelligence, etc.

Telecom applications, that are redesigned around this new paradigm, can all use one shared back-office architecture. Having data centralized into one large Hadoop cluster instead of tens or hundreds of application-specific databases, will enable unseen data analytics possibilities and bring much-needed efficiencies.

Is this shared-architecture paradigm new? Not at all. Google has been using it since 2004 at least when they published Map Reduce and BigTable.

What is needed is that several large operators define this approach as their standard architecture hence telecom solution providers will start incorporating it into their solutions. Commercial support can be easily acquired from companies like Hortonworks, Cloudera, etc.

Having one shared data architecture and multi-tenant application virtualization in the form of a Telco PaaS would allow third-parties to launch new services quickly and cheaply, think days in stead of years…

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