Big Data, a contemporary popular adoption in academia and industry, is the biggest trump card for a data-driven world. When fathomed and processed till the best depths, the Hercules amount of data that is generated on a momentary basis, which intertwines structural and functional units at the core, can provide a plethora of consolidated information. Such information is skeletal to any action at individual or organisational level for the decisionmaking ball game, the acumen of our life-cycle. The amplified hustle that hovers Big Data is entailed to the sperm of data flow and cost cordial, resource optimized management of the namesake. The mushrooming of data compounds to an annual approximate of 40%. Prospects are very high that by the next fiscals, in 2020, the Internet will have about 50 billion devices corded to it leading to an estimated data production escalation by 44 times to 2009, reaching nearly to a 45 ZB; thus stipulating the volume growth rate of business data to a doubling in every 1.2 years. Such explosion entails along with, a myriad of challenges in terms of data collection, curation, processing, storage, management, maintenance, security, analysis, transfer, visualization, retention, flexibility; which need to be addressed carefully and efficiently, as inherent to Big Data Analytics. This study encircles the arena of Big Data aiming to collectively unearth and delve into the terminologies, attributes, definitions, characteristics, tools, technologies and components related to Big Data. There is a parallel focus on SWOT Analysis of Big Data Analytics, addressing in a jiffy the advantages, challenges, future research scopes and open issues and limitations, associated with Big Data and its components. Lastly, this study also intends to survey and summarise the step-wise Big Data processing cycle, in association with the functionalities of the different constituent tools and technologies in anutshell.

Keywords: big data, technologies, terminologies, opportunities, volume, variety, velocity, business intelligence, data warehouse, data lake, growth, exponential
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