Big Data in the Automotive Industry 2017 - 2030 : $2.8 Billion Opportunities, Challenges, Strategies & Forecasts

ResearchMoz added Latest Research Report titled " Big Data in the Automotive Industry: 2017 - 2030 - Opportunities, Challenges, Strategies & Forecasts " to it's Large Report database.

“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 and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The automotive industry is no exception to this trend, where Big Data has found a host of applications ranging from product design and manufacturing to predictive vehicle maintenance and autonomous driving.

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SNS Research estimates that Big Data investments in the automotive industry will account for over $2.8 Billion in 2017 alone. Led by a plethora of business opportunities for automotive OEMs, tier-1 suppliers, insurers, dealerships and other stakeholders, these investments are further expected to grow at a CAGR of approximately 12% over the next three years.

The “Big Data in the Automotive Industry: 2017 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the automotive industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2017 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 4 application areas, 18 use cases, 6 regions and 35 countries.

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Table of Contents

1 Chapter 1: Introduction 22
1.1 Executive Summary 22
1.2 Topics Covered 24
1.3 Forecast Segmentation 25
1.4 Key Questions Answered 28
1.5 Key Findings 29
1.6 Methodology 30
1.7 Target Audience 31
1.8 Companies & Organizations Mentioned 32

2 Chapter 2: An Overview of Big Data 36
2.1 What is Big Data? 36
2.2 Key Approaches to Big Data Processing 36
2.2.1 Hadoop 37
2.2.2 NoSQL 39
2.2.3 MPAD (Massively Parallel Analytic Databases) 39
2.2.4 In-Memory Processing 40
2.2.5 Stream Processing Technologies 40
2.2.6 Spark 41
2.2.7 Other Databases & Analytic Technologies 41
2.3 Key Characteristics of Big Data 42
2.3.1 Volume 42
2.3.2 Velocity 42
2.3.3 Variety 42
2.3.4 Value 43

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