Demystifying Data Virtualization: Why it’s Now Critical for Your Data Strategy

A shift to the cloud is a common element of any current data strategy. However, a successful transition to the cloud is not easy and can take years. It comes with security challenges, changes in downstream and upstream applications, and new ways to operate and deploy software. An abstraction layer that decouples data access from storage and processing can be a key element to enable a smooth journey to the cloud.

What makes data scientists happy? Of course data. They want it fast and flexible, and they want to do it themselves. But most classic data warehouses (DW) and data lakes are not easy to deal with for agile data access. A more practical solution is the logical data warehouse (LDW), which has shown to be a more agile foundation for delivering and transforming data and makes it easy to quickly plug in new data sources.

5G and the applications it enables, including IoT, requires a significant upgrade and overhaul of carrier infrastructure beyond the radio networks (RAN). To support the diversity of applications, explosion in number and types of devices, capacity and scale of traffic, CSPs (communications service providers) are turning to adoption of data center innovation adapted and re-developed for telco networks. Virtualization, software-defined controls, distributed computing technologies in the form of NFV, SDN and MEC play key roles in the new telco infrastructure. One of the key technologies is SDN, which is important to creating flexibility in the network to support 5G constructs such as network slicing. SDN’s centralized management and distributed controls over complex software-defined networks across the mobile infrastructure is also a key element in 5G scalability.

"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).