Global In-Memory Data Grid Market to 2023: Opportunities in Attaining High Throughput With Real-Time Processing & Improving the Performance of Analytical Applications

Dublin, Dec. 17, 2018 (GLOBE NEWSWIRE) -- The "In-Memory Data Grid Market by Component, Business Application (Transaction Processing, Fraud and Risk Management, Supply Chain Optimization), Industry Vertical, Organization Size, Deployment Type, and Region - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering.

Increasing use of distributed architecture to enhance limited storage capacity of main memory to drive the in-memory data grid market

The global in-memory data grid market size is expected to grow from USD 1.4 billion in 2018 to USD 2.3 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 10.8% during the forecast period.

The in-memory data grid market is driven by various factors, such as the use of distributed architecture to enhance limited storage capacity of main memory, and focus on eliminating the need for relational data model and database. However, system or components failure which may result in loss of data, can hinder the growth of the market.

The fraud and risk management segment to grow at the highest CAGR during the forecast period

Organizations use fraud and risk management applications to enhance their risk intelligence capabilities to overcome risk exposures. Risk management has become a top focus for regulatory bodies around the world. It has increasingly become important for several businesses to provide accurate and timely risk reporting to regulatory agencies. Using in-memory data grid helps unify corporate risk data, perform the required analytics, and report relatively easy as compared to traditional methods of data management.

The BFSI vertical to hold the largest market size during the forecast period

Financial organizations across the globe are looking for in-memory data grid solutions which can process data in real time and improve the performance of their business-critical applications. Trading systems with high transaction rates are examples of environments best-suited for the in-memory data grid. It enables financial applications with real-time analytics due to faster data access and high throughput in use cases, such as trading, fraud detection, risk management, and portfolio management. Using in-memory data grid solution would enable financial organizations to improve their marketing strategies and customer retention policies, develop new investment strategies, and mitigate risks.

Asia Pacific (APAC) to record the highest growth rate during the forecast period

APAC is expected to grow at the highest CAGR during the forecast period, due to an increasing demand for in-memory data grid. Major APAC countries, such as China, Australia and New Zealand, India, and Singapore, provide significant opportunities for the adoption of the in-memory data grid solutions across industry verticals. Meanwhile, North America is projected to hold the largest market size during the forecast period.

Major vendors offering in-memory data grid solutions across the globe include IBM (US), Oracle (US), Red Hat (US), Software AG (Germany), Pivotal (US), Hitachi (Japan), Hazelcast (US), TIBCO (US), GridGain (US), ScaleOut Software (US), GigaSpaces (US), Alachisoft (US), and TmaxSoft (US).

Key Topics Covered:

1 Introduction
1.1 Objectives of the Study
1.2 Market Definition
1.3 Market Scope
1.4 Years Considered for the Study
1.5 Currency Considered
1.6 Stakeholders

2 Research Methodology
2.1 Research Data
2.2 Market Breakup and Data Triangulation
2.3 Market Size Estimation
2.4 Assumptions for the Study
2.5 Limitations of the Study

3 Executive Summary

4 Premium Insights
4.1 Attractive Market Opportunities in the In Memory Data Grid Market
4.2 Market By Business Application and Country (2018)
4.3 Market Major Countries

5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers
5.2.1.1 Using Distributed Architecture to Enhance Limited Storage Capacity of the Main Memory
5.2.1.2 Eliminating the Need for Relational Data Model and Database
5.2.2 Restraints
5.2.2.1 System/Component Failure May Result in the Loss of Data
5.2.3 Opportunities
5.2.3.1 Attaining High Throughput With Real-Time Processing
5.2.3.2 Improving the Performance of Analytical Applications
5.2.4 Challenges
5.2.4.1 Maintaining Data Security
5.3 Industry Trends
5.3.1 Use Case 1: Pivotal Software
5.3.2 Use Case 2: Hazelcast
5.3.2.1 Use Case 3: Tibco Software

6 In Memory Data Grid Market, By Component
6.1 Introduction
6.2 Solution
6.2.1 Growing Need to Have Streamlined and High-Performing Applications to Drive the Adoption of the In-Memory Data Grid Solution Among Enterprises
6.3 Professional Services
6.3.1 Consulting
6.3.1.1 Growing Need Among Organizations to Be Technically Well Versed to Drive the Growth of In-Memory Data Grid Consulting Services
6.3.2 Support and Maintenance
6.3.2.1 Focus on Improving the Performance of Applications to Drive the Growth of In-Memory Data Grid Support and Maintenance Services
6.3.3 Education
6.3.3.1 Need to Educate Employees on How to Use In-Memory Data Grid Solutions to Drive the Adoption of In-Memory Data Grid Educational Services

7 In Memory Data Grid Market, By Business Application
7.1 Introduction
7.2 Transaction Processing
7.2.1 Growing Need for Faster and Smoother Transactions to Drive the Adoption of In-Memory Data Grid in the Transaction Processing Business Application
7.3 Fraud and Risk Management
7.3.1 Regulatory Compliances Among Organizations to Boost the Adoption of In-Memory Data Grid in the Fraud and Risk Management Business Application
7.4 Supply Chain Optimization
7.4.1 Growing Need for Handling Large Datasets to Drive the Adoption of In-Memory Data Grid in the Supply Chain Optimization Business Application
7.5 Sales and Marketing Optimization
7.5.1 Adopting In-Memory Data Grid for Analyzing Customer Data to Improve Sales and Marketing Operations

8 In Memory Data Grid Market, By Industry Vertical
8.1 Introduction
8.2 Banking, Financial Services, and Insurance
8.2.1 Demand for Real-Time Analysis of Data Generated From Financial Applications to Drive the Growth of the Market
8.3 Media and Entertainment
8.3.1 Growing Focus of Organizations to Deliver Quality Content at Fast Pace to Drive the Growth of the Market
8.4 Consumer Goods and Retail
8.4.1 Driving Sales By Improving Operational Efficiencies to Drive the Growth of the Market
8.5 Healthcare and Life Sciences
8.5.1 Demand for Proactive Diagnostic Services to Enhance Patient Experience to Drive the Growth of the Market
8.6 Manufacturing
8.6.1 Increasing Need for Real-Time Production Planning and Demand Forecasting to Drive the Growth of the Grid Market
8.7 Telecom and It
8.7.1 Growing Need for Organizations to Cater Dynamic Customer Preferences to Drive the Growth of the Market
8.8 Transportation and Logistics
8.8.1 Demand From Organizations for Accurate Information to Make Better Business Decisions to Drive the Growth of the Market
8.9 Others

9 In Memory Data Grid Market, By Organization Size
9.1 Introduction
9.2 Large Enterprises
9.2.1 Demand for High-Performance Computing to Drive the Growth of the Market
9.3 Small and Medium-Sized Enterprises
9.3.1 Need for Cost-Effective Solutions Offering High Scalability and Enhanced System Performance to Drive the Growth of Market

10 In Memory Data Grid Market, By Deployment Type
10.1 Introduction
10.2 On-Premises
10.2.1 Security Concerns Among Enterprises to Drive the Adoption of the On-Premises In-Memory Data Grid Solution
10.3 Cloud
10.3.1 Scalability and Cost-Effectiveness are the Major Advantages to Adopt A Cloud-Based In-Memory Data Grid Solution

11 In Memory Data Grid Market, By Region
11.1 Introduction
11.2 North America
11.2.1 United States
11.2.1.1 Early Adoption of Technology and Strong R&D Investments to Boost the Growth of the US In-Memory Data Grid Industry
11.2.2 Canada
11.2.2.1 Growing Demand for the Analytical-Based Solutions to Drive the Growth of the Market in Canada
11.3 Europe
11.3.1 United Kingdom
11.3.1.1 Increasing Focus of Organizations to Effectively Handle Large Data Volumes Leads to the Growth of the UK In-Memory Data Grid Industry
11.3.2 Germany
11.3.2.1 Demand for High Technological Solutions to Drive the Growth of Germany In-Memory Data Grid Industry
11.3.3 France
11.3.3.1 Increasing Investments of Organizations in Real-Time Analytics Solutions Leads to the Growth of the Market in France
11.3.4 Rest of Europe
11.4 Asia Pacific
11.4.1 China
11.4.1.1 Increasing Data Volumes Across Industry Verticals to Contribute to the Growth of the Market in China
11.4.2 Japan
11.4.2.1 Rise in the Adoption of Connected Devices to Contribute to the Growth of the Market in Japan
11.4.3 Australia and New Zealand
11.4.3.1 Growing Need to Offer Personalized Products and Services Leads to the Growth of the Market in Australia and New Zealand
11.4.4 Rest of Asia Pacific
11.5 Middle East and Africa
11.5.1 Kingdom of Saudi Arabia
11.5.1.1 Increasing Adoption of Advanced It Infrastructure in the Energy and Utilities Industry Vertical to Contribute to the Growth of the Market in Ksa
11.5.2 United Arab Emirates
11.5.2.1 State-Of-The-Art Infrastructure and the Implementation of the Cutting-Edge Technology Lead to the Growth of the In Memory Data Grid Market in the UAE
11.5.3 South Africa
11.5.3.1 Increasing Adoption of In-Memory Computing Technologies to Spur the Demand for the Market in South Africa
11.5.4 Rest of Middle East and Africa
11.6 Latin America
11.6.1 Brazil
11.6.1.1 Increasing Proliferation of Consumer Data and Rising Demand for Data-Driven Enterprises for Quick and Real-Time Access to This Data to Boost the Demand for the Market in Brazil
11.6.2 Mexico
11.6.2.1 Government Initiatives in the Market Lead to Increasing Infrastructural Investments From Several Global Investors to Drive the Growth of the Overall Market in Mexico
11.6.3 Rest of Latin America

12 Competitive Landscape
12.1 Overview
12.2 Competitive Scenario
12.2.1 Product/Service/Solution Launches and Enhancements
12.2.2 Business Expansions
12.2.3 Acquisitions
12.2.4 Partnerships

13 Company Profiles
13.1 Introduction
13.2 Oracle
13.3 IBM
13.4 Hazelcast
13.5 Scale Out Software
13.6 Tibco Software
13.7 Red Hat
13.8 Software AG
13.9 Gigaspaces
13.10 Gridgain Systems
13.11 Alachisoft
13.12 Pivotal
13.13 Tmaxsoft
13.14 Hitachi

For more information about this report visit https://www.researchandmarkets.com/research/fv45bl/global_inmemory?w=12

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