Your Big Data Needs Data Virtualization

Big data in the cloud has plenty of promising functional service layers stretched across many clusters, nodes, and tires that it is absolutely easy and simple to feel overwhelmed.

The first step should be to plan all-inclusive cloud data virtualization infrastructure. Virtualized cloud analytics ensures combined access, deployment, modeling, optimization and management of big data as a varied resource.

What’s Data Virtualization?

In programming language, virtualization is to develop a virtual version of a device or resource. Explaining in simple words, just like any other virtualization, it is an approach that enables you to optimize, access and conduct a diverse infrastructure as if it were a single, logically combined resource. This allows you to conceptualize the external interface from the internal implementation of some functionality, service or other resource.

The Centerpiece

The highlight of data virtualization is an abstraction layer, like any other SQL virtualization approaches that support logically combined query, access, reporting predictable analytics, and some other apps against different back-end data repositories like NoSQL, Hadoop and others. Certainly, data virtualization may depend on other layers of infrastructure virtualization, like server and storage platforms, and in some cases it is stretched across various cloud environment and geographic locations.

Although various layers we are discussing, but virtualization is the perfect example of ugly data topics. But it is elementary if you want the big data cloud platform to address these business essentials:

• A cutting-edge analytic source of flexible, fluid topology

• An intense resource that consume information arising in any source, schema and format

• A latency-agile system that accumulates, continues, and processes dynamic mixture of in-motion and at-rest information.  

• An aligned system that spreads across and within value chains, public and private clouds

• A smooth interoperability system that allows you to modify, measure, and develop back-end data platforms without discontinuing interoperability with tools and applications that are in operation.

So, it’s visible, data virtualization is easier said than done. And it’s not at all inexpensive to optimize, implement or administer.

Cloud-based big data will need virtualized foundations of burgeoning difficulty. Data professionals know it’s a tedious task, and prefer to aim their strategic telescopes towards NoSQL, Hadoop etc - that glow brightest in the latest technology market.

As the dimension of the cloudy big data apps increases, you will undoubtedly have to go further down the virtualization path. The adamant variety of hybridized big data clouds will lead you to that direction. Within the private clouds, continuous big data platform churn will need virtualization fabric that links new approaches with your legacy investment.

In the next few years, more complicated analytic rules, models, and information converge on the big data cloud will become a highlight of virtualized execution, access, and administration. And within this modern world, MapReduce which is a programming model and an associated implementation for dealing with, and generating large data sets with a parallel, distributed algorithm on a cluster) will be one of the key development frameworks.

No one has yet come forward to outline the interfaces, abstractions, and layers that will work as a glue to keep cloud big data universe together from one point to another. That is again a request difficult to fulfill.

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