Emerging Big Data Storage Architectures: A New Paradigm

Volume 4 Issue 2 June - August 2017

Survey Paper

Emerging Big Data Storage Architectures: A New Paradigm

Aasha Dhanapal*, M. Venkatesh Saravanakuma**, Sabibullah Mohamed Hanifa***

* Research Scholar, Research Department of Computer Science, Sudharsan College of Arts & Science (SCAS), Pudukkottai, Tamilnadu, India.

** Research Scholar, Research Department of Computer Science (SCAS), Pudukkottai, Tamilnadu, India.

*** Associate Professor & Dean, Research Department of Computer Science, SCAS, Pudukkottai, Tamilnadu, India.

Dhanapal, A., Saravanakumar, M. V., and Sabibullah, M. (2017). Emerging Big Data Storage Architectures: A New Paradigm. i-manager’s Journal on Pattern Recognition, 4(2), 31-41. https://doi.org/10.26634/jpr.4.2.13732

Abstract

With the emergence and huge transformational potential capability of Big Data Storage (like store, manage and analyse huge amounts of heterogeneous data), finally derives the benefit of data-driven society and economic impacts. Since, the new wave of heterogeneous data rises from different sources, such as the Internet of Things (IoT), Sensor Networks, Open Data on the Web, data from mobile applications, and social networking, comprising that the natural growth of datasets available within the organisations, that certainly creates a demand for new data management storage strategies provide a new scales of data environment. Health sector is a first-rate scenario in this regard, to provide better health services to the society through the way of best integration and analysis of health related data by using current state-of-the-art in Big Data Storage (BDS) technologies, which identifies data-store related trends and capable of handling massive data. This survey paper discusses about the various emerging paradigms connected with BDS technologies, which gives options to both Hadoop and Spark, a fast and newly impacted computing avatar [i.e. In- Memory cluster (Multiple computers linked together, through a fast LAN, that effectively function as a single coputer) Computing] by replacing capacity of MapReduce through Resilient Distributed Datasets (RDD). It forecasts the entire features and availability options behind BDS, to deliver better data model in any Big Data (BD) dependent applications.