Take control of the growing volume, variety & velocity of your big data with Lenovo's high-performance, cost-effective data management & analytics solutions.
(Optane Memory, Optane SSD, NVMe SSD, etc.) can be used to improve the storage performance. Meanwhile HDFS provides all kinds of nice methodologies like HDFS Cache, Heterogeneous Storage Management (HSM) and Erasure Coding (EC), but it is a big challenge for users to make full utilization ...
Nutanix delivers bare metal performance while significantly simplifying and optimizing infrastructure for your virtualized big data and analytics installations.
Data Quality has been an active and attractive research area for several years [2,3]. In the context of Big Data, quality assessment processes are hard to implement, since they are time- and cost-consuming, especially for the pre-processing activities. These issues have got intensified since ...
Syntelli is top big data consulting services and solutions provider, that offers data science, advanced predictive analytics, artificial intelligence, MDM & IoT to help companies transition from gut-driven to big data-driven strategies.
New data management components Over the last decade, developments within hybrid cloud, artificial intelligence, the Internet of Things (IoT) andedge computing have led to the exponential growth of big data, creating even more complexity for enterprises to manage. New components continue to improve...
Roadmap for D&A Initiatives What is “big data?” The term “big data” has been used for decades to describe data characterized by high volume, high velocity and high variety, and other extreme conditions. However, the big data era is epitomized for businesses by its associated opportunities...
Data management systems are built on data management platforms and can include databases, data lakes and data warehouses, big data management systems, data analytics, and more. All these components work together as a “data utility” to deliver the data management capabilities an organization ...
Research directions on Big Data differ between industry and academia. Industry scientists mainly focus on the technical implementations, infrastructures, and solutions for Big Data management, whereas researchers from academia tackle theoretical issues of Big Data. Academia’s efforts mainly include the dev...
Also, a process model of big data driven smart energy management is proposed. Then taking smart grid as the research background, we provide a systematic review of big data analytics for smart energy management. It is discussed from four major aspects, namely power generation side management, ...