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(Wireless-NewsWire.Com, October 11, 2016 ) Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.
Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D.
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Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for over $46 Billion in 2016 alone. These investments are further expected to grow at a CAGR of 12% over the next four years.
The “Big Data Market: 2016 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2016 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.
The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
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Table of Contents
1 Chapter 1: Introduction 21
1.1 Executive Summary 21
1.2 Topics Covered 23
1.3 Historical Revenue & Forecast Segmentation 24
1.4 Key Questions Answered 26
1.5 Key Findings 27
1.6 Methodology 28
1.7 Target Audience 29
1.8 Companies & Organizations Mentioned 30
2 Chapter 2: An Overview of Big Data 34
2.1 What is Big Data? 34
2.2 Key Approaches to Big Data Processing 34
2.2.1 Hadoop 35
2.2.2 NoSQL 36
2.2.3 MPAD (Massively Parallel Analytic Databases) 36
2.2.4 In-memory Processing 37
2.2.5 Stream Processing Technologies 37
2.2.6 Spark 38
2.2.7 Other Databases & Analytic Technologies 38
2.3 Key Characteristics of Big Data 39
2.3.1 Volume 39
2.3.2 Velocity 39
2.3.3 Variety 39
2.3.4 Value 40
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