Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner.
Erin worked for years as a software engineer and consultant at Microsoft managing large data migration projects, and designed toys as an electronics engineer at Mattel before that.
No single program running on a 32-bit processor can, without a lot of fussing, use more than 4GB of memory, which can be a problem when managing large data sets or handling extremely complex graphics.
Mapping MapReduce frameworks to Cloud Architecture became a must in last years because of the need of managing large data sets and Big Data in fast, reliable (and as cheap as possible) way.
However, space overhead is not the only resource to be optimized when managing large data collections; in fact data turn out to be useful only when properly indexed to support search operations that efficiently extract the user-requested information.
Moreover, the group mentions that Big Data exploits cloud resources to manage large data volume extracted from multiple sources [2].
Its technology relies a lot on software to manage its Flash storage, which gives customers the capability to manage large data loads with corresponding high-performance.
MapReduce framework is widely used for processing and managing large data sets in a distributed cluster, which has been used for numerous applications such as, document clustering, access log analysis, generating search indexes and various other data analytical operations.
Over the past two decades, NeuStar began to apply its expertise at managing large data sets to the Web, providing Web, telecom, and other industries with insight and intelligence based on both their telephone and web data.
In addition, NVIDIA today announced that it has partnered with Mesosphere — the container-centric solution for managing large data centers as if they were a single pool of resources.
