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 nearly $40 Billion in 2015 alone. These investments are further expected to grow at a CAGR of 14% over the next 5 years.
The Big Data Market: 2015 – 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 2015 through to 2030. Historical figures are also presented for 2010, 2011, 2012, 2013 and 2014. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 35 countries.
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Table Of Content Of Big Data Market:
1 Chapter 1: Introduction 18
1.1 Executive Summary 18
1.2 Topics Covered 20
1.3 Historical Revenue & Forecast Segmentation 21
1.4 Key Questions Answered 23
1.5 Key Findings 24
1.6 Methodology 25
1.7 Target Audience 26
1.8 Companies & Organizations Mentioned 27
2 Chapter 2: An Overview of Big Data 31
2.1 What is Big Data? 31
2.2 Key Approaches to Big Data Processing 31
2.2.1 Hadoop 32
2.2.2 NoSQL 33
2.2.3 MPAD (Massively Parallel Analytic Databases) 33
2.2.4 In-memory Processing 34
2.2.5 Stream Processing Technologies 34
2.2.6 Spark 35
2.2.7 Other Databases & Analytic Technologies 35
3 Chapter 3: Vertical Opportunities & Use Cases for Big Data 43
3.1 Automotive, Aerospace & Transportation 43
3.1.1 Predictive Warranty Analysis 43
3.1.2 Predictive Aircraft Maintenance & Fuel Optimization 44
3.1.3 Air Traffic Control 44
3.1.4 Transport Fleet Optimization 44
3.2 Banking & Securities 46
3.2.1 Customer Retention & Personalized Product Offering 46
3.2.2 Risk Management 46
3.2.3 Fraud Detection 46
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