Tips for Passing the VMware vSphere Exam

Self-service Analytics BI is often quoted by many - ie, allow users to discover and access data without having to ask IT to create a data mart, or by allowing users to directly export/copy the data from the data sources themselves into their analytics tools and systems. The challenge is not just to provide access to the data – even from Excel this can be done - but to do this in real time without creating processing overhead, while getting trusted data, with the best response time possible, in a managed, governed and secure way in order for these users to trust the output of the analysis. Data Virtualization provides a data access platform that allows users to access the data they need from multiple data sources, when they need it, and with the best possible response time. In addition, a Data Marketplace built on top of this proven technology enables Self Service Analytics by exposing consistent and governed data sets to be discovered by users, providing the trusted foundation for a successful Self-Service Analytics initiative.

The major carrier goals for SDN and NFV are service agility and automation removing manual processes from their service and network operations, from customer order to network reconfiguration to SLA enforcement. Join thought leaders for this webinar.

NSX-T can enhance and simplify the data centre environment with network virtualisation, as well as deliver immediate benefits to the business workload level to prevent the lateral spread of threats, and, due to its true cloud hybridity, security rules follow applications and workloads even across multi-cloud environments.

"Data is the new science. Big Data holds the answers." - Pat Gelsinger, the CEO of VMware, Inc. Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. Apache Spark is a fast and general engine for large-scale data processing ,built around speed, ease of use, and sophisticated analytics. It was originally developed at UC Berkeley in 2009.Spark is a good fit for the Hadoop open-source community as its built on top of the Hadoop Distributed File System (HDFS).