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24 July 2020

Are You Prepared For Virtualized Big Data?

Big Data is no longer a stand-alone technology or specialization. In the last 3-4 years, the Big Data industry has exploded and fragmented into numerous sub-specializations within the Data Science domain. It includes data preparation and analytics running on Artificial Intelligence, Machine Learning, Neural Networking and Speech Recognition, and so on. But, the real value of Big Data is infused when we talk about Virtualization. The importance of learning Big Data for virtualized ecosystems is what the future of Cloud holds.

With virtualization, we mean Virtualized Server and Virtual Machines (VMs) -- a formidable pair that not only stores and ingests large volumes of Big Data in distributed remote co-location sites but also delivers high-speed intelligence and actionable insights with the Big Data analytics flexible IT infrastructure management in the Cloud.

In a simple sentence, Virtualization of IT networking can be considered as a logical arrangement of virtualized hardware (Computers, cameras, GPUs, CPUs) and software (Cloud, applications, endpoint security suites) components connected by internet via a dedicated, secured and seamless interface of heterogeneous and distributed Big Data systems.

All these could have moved without Big Data, but today, the demands of enterprise data management and security compliance are entirely different than what it was merely 18 months ago. The GDPR and CCPA arrangements have put a daunting pressure on all data-driven companies that churn massive amounts of Customer and Device data from various sources. Without server virtualization, running Big Data analytics would be futile-- not to forget, highly volatile especially now, when 90 percent of all global companies have acknowledged being a “Direct Target” and victim of a cyber threat such as data theft and ransomware.

Job-oriented Big Data training in Bangalore, Delhi, and Hyderabad are focusing on industry-relevant Big Data challenges that specifically arise from the adoption of virtualization.

These could be managed by --

Automated Server Operating Systems, involving on-premise or Hybrid Cloud applications that store data in a single physical IT system and partition on a need to use basis, also called on-demand partitioning.

Isolation techniques, to prevent disruption of the data management life cycle if any server or VM fails to connect or simply fails to compute due to crash.

Multi-user, Single-point access, and identity management, to prevent data theft cases and stating accountability through Identity Management Systems for each VM assigned remotely to a user by the IT admin.

This VM management system is a very critical infrastructure in the whole digital transformation of a Big Data analytics company -- and the role of modern IT engineer revolves around managing VMs, virtualized servers, and a host of Identity Management systems.

As we head into a high paced computing era that also seeks high-end security at all points, Big Data virtualization has become a key operation in an enterprise data management company. To exactly understand their relationships in a Virtualized network, you need admin skills as well as computing skills certified from a Big Data Training institute. Start planning today!

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