Smart innovation for adoption of big data technology : Hadoop on Virtual Machine.

In recent years, big data workloads have taken advantage of the numerous benefits of virtualization such as scalability, agility, ease of maintenance, and costs savings due to consolidation and better utilization of resources.

As Big Data continues to achieve mainstream status within enterprises, the virtualization of Hadoop and other Big Data workloads has become a key mechanism for organizations to future-proof their Big Data infrastructure investments.

Hadoop clusters can run on physical or virtualized servers and in public, private, or hybrid clouds. A virtual Hadoop cluster is when the Hadoop distribution is installed within the context of a collection of virtual machines (VMs).

Virtualization enables implementation of Hadoop on existing infrastructure. A particular advantage of running a virtual Hadoop cluster is fault isolation. VMs provide a high level of fault isolation – if one workload crashes, others aren’t affected.

Another benefit of virtualized environments is that they provide greater flexibility. In a matter of time, users can switch from one cluster to another, a process that takes days or even weeks in a physical environment.

Other Strengths of VM-hosted Hadoop:

• Physical infrastructure can be reused.
• The cluster size can be expanded or contracted on demand.
• Minimum installation and setup time.

Event: 16 November, 2016

Impact of Big Data & Analytics on Financial Services

Pentation Analytics, which focuses on big data enabled analytics for the Banking, Insurance & Financial Services space had organised an event “Impact of Big Data & Analytics on Financial Services” held at Cricket Club of India, Mumbai on 16th November, 2016. The event saw participation from experts and practitioners from the BFSI and Technology industry segments.
The event had a panel discussion, which comprised senior industry stalwarts. The focus of the panel discussion was the changing business landscape which has prompted organizations to look at analytics as a core process rather than a set of disparate projects.