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
[132 Pages Report] The In-Memory Data Grid Market predicted to grow from $1.4 billion in 2018 to $2.3 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 10.8% during 20182023. Major growth factors for the market include an increasing need for distributed architecture to enhance the limited storage capacity of the main memory and eliminating the need for relational data model and database.
By business application, the transaction processing segment to hold the largest In-Memory Data Grid Market size during the forecast period
All aspects of business involve some form of transaction. Transactional applications, such as shopping and retailer apps, require high-performance data management. Various industry verticals have undertaken initiatives for digital transformation, and it has led to an increase in transactional applications. In-memory data grid aids transaction processing by providing high data consistency, thereby resulting in faster and smoother transactions. It can even support a large number of concurrent transactions that involve terabytes of operational data. In-memory data grid offers elastic scalability, as the number of transactions increase. It can boost the efficiency of payment processing that is crucial in areas of trading and online shopping apps.
By deployment type, the cloud to grow at a fastest CAGR rate during the forecast period
In the cloud deployment type, in-memory data grid solutions are deployed on public or private cloud located remotely. The cloud deployment type is expected to be the faster-growing deployment type in the In-Memory Data Grid Market. Implementing the cloud-based IMGD solutions and services help SMEs (Small and Medium-sized Enterprises) and large enterprises focus on their core competencies rather than IT processes. Furthermore, it helps reduce the IT budget, as the tool only requires an internet connection. For organizations, which have limited budgets, the cloud-based in-memory data grid solutions are a good option because of its speed and scalability. The cloud-based deployment type also provides flexible service by offering data governance on demand, as customers have to pay according to their utilization of services.
The BFSI segment to hold the largest In-Memory Data Grid Market size during the forecast period
Growing digitalization are compelling financial organizations to build a lean, flexible, and efficient approach to cater to customers. Financial institutions deal with critical information, which, if not properly processed, can lead to severe financial and ethical implications. Hence, 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. The BFSI vertical includes organizations that are into banking services, such as core banking, corporate, retail, investment; financial services that include payment gateways, stock broking, and mutual funds; and insurance services covering both life and non-life insurance. 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.
North America to hold the largest market size during the forecast period
North America is expected to hold the largest market size and dominate the managed services market by region from 2018 to 2023. The region has a high concentration of large multinational companies which largely contribute to the growth of the In-Memory Data Grid Market. However, APAC is expected to provide lucrative opportunities for the in-memory data grid providers, owing to the increasing data volumes across industries and rise in adoption of the connected devices in the region. The increasing number of government regulations and compliances in various regions could affect the adoption of in-memory data grid.
Key Players
Major vendors of In-Memory Data Grid Market 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).
IBM, a leader in offering an extensive set of solutions under its in-memory data grid portfolio, caters to the needs of different verticals across the globe. The companys leading in-memory data grid platform, WebSphere eXtreme Scale, is built on the elastic data caching technology for helping organizations improve the performance scalability and reliability of their business applications. The platform is deeply integrated with the WebSphere Application Server (WAS) and can perform data analysis and processing by reducing input/output operations in computer memory. Furthermore, IBM constantly focuses on growth strategies to enhance its offerings and expand its market reach with the help of strategic partnerships, mergers and acquisitions, business expansions, and product and service developments.
Recent developments
- In July 2017, IBM launched IBM Elastic Storage Server (ESS), a high-performance data and file management solution, for its enterprise clients. The solution is powered by IBM Spectrum Scale software, which has the capability to run complex business applications and support data processing engines, such as Hadoop and Spark.
- In March 2018, Oracle opened a new office in Texas, US. This expansion would enable the company to provide the necessary resources and training to cloud sales professionals and drive the growth of its cloud business.
- In May 2018, Red Hat partnered with IBM to target the hybrid cloud sector by integrating technologies and services. Following the partnership, IBM and Red Hat customers benefitted from the combined power of both the companies technologies deployed on private and public cloud.
Key questions addressed by the In-Memory Data Grid Market report
- What are the major challenges faced by enterprises while deploying various in-memory data grid solutions?
- Where and to what extent organizations need in-memory data grid solutions?
- What is the level of preparedness of enterprises to deal with unforeseen business risks?
- Which of the talent and technology gaps of organizations can affect business operations?
- What are the challenges faced by in-memory data grid solutions providers while integrating innovative technologies with a clients existing IT infrastructure?
To speak to our analyst for a discussion on the above findings, click Speak to Analyst
Table of Contents
1 Introduction (Page No. - 16)
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 (Page No. - 20)
2.1 Research Data
2.1.1 Secondary Data
2.1.2 Primary Data
2.1.2.1 Breakup of Primary Profiles
2.1.2.2 Key Industry Insights
2.2 Market Breakup and Data Triangulation
2.3 Market Size Estimation
2.3.1 Bottom-Up Approach
2.3.2 Top-Down Approach
2.4 Assumptions for the Study
2.5 Limitations of the Study
3 Executive Summary (Page No. - 27)
4 Premium Insights (Page No. - 30)
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 (Page No. - 33)
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 (Page No. - 39)
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 Market, By Business Application (Page No. - 46)
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 (Page No. - 51)
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 Market, By Organization Size (Page No. - 61)
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 (Page No. - 65)
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 Market, By Region (Page No. - 69)
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 (Page No. - 94)
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 (Page No. - 100)
13.1 Introduction
(Business Overview, Products & Solutions, Key Insights, Recent Developments, SWOT Analysis, MnM View)*
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
*Details on Business Overview, Products & Solutions, Key Insights, Recent Developments, SWOT Analysis, MnM View Might Not Be Captured in Case of Unlisted Companies.
14 Appendix (Page No. - 125)
14.1 Discussion Guide
14.2 Knowledge Store: Marketsandmarkets Subscription Portal
14.3 Available Customizations
14.4 Related Reports
14.5 Author Details
List of Tables (67 Tables)
Table 1 In-Memory Data Grid Market Size, By Component, 20162023 (USD Million)
Table 2 Solution: Market Size By Region, 20162023 (USD Million)
Table 3 Professional Services Market Size, By Type, 20162023 (USD Million)
Table 4 Professional Services Market Size, By Region, 20162023 (USD Million)
Table 5 Consulting Market Size, By Region, 20162023 (USD Million)
Table 6 Support and Maintenance Market Size, By Region, 20162023 (USD Million)
Table 7 Education Market Size, By Region, 20162023 (USD Million)
Table 8 Business Application Market Size, By Type, 20162023 (USD Million)
Table 9 Transaction Processing: Market Size By Region, 20162023 (USD Million)
Table 10 Fraud and Risk Management: Market Size By Region, 20162023 (USD Million)
Table 11 Supply Chain Optimization: Market Size By Region, 20162023 (USD Million)
Table 12 Sales and Marketing Optimization: Market Size By Region, 20162023 (USD Million)
Table 13 In-Memory Data Grid Market Size, By Industry Vertical, 20162023 (USD Million)
Table 14 Banking, Financial Services, and Insurance: Market Size By Region, 20162023 (USD Million)
Table 15 Media and Entertainment: In-Memory Data Grid Market Size By Region, 20162023 (USD Million)
Table 16 Consumer Goods and Retail: Market Size By Region, 20162023 (USD Million)
Table 17 Healthcare and Life Sciences: Market Size By Region, 20162023 (USD Million)
Table 18 Manufacturing: Market Size By Region, 20162023 (USD Million)
Table 19 Telecom and It: In-Memory Data Grid Market Size By Region, 20162023 (USD Million)
Table 20 Transportation and Logistics: Market Size By Region, 20162023 (USD Million)
Table 21 Others: Market Size By Region, 20162023 (USD Million)
Table 22 In-Memory Data Grid Market Size, By Organization Size, 20162023 (USD Million)
Table 23 Large Enterprises: Market Size By Region, 20162023 (USD Million)
Table 24 Small and Medium-Sized Enterprises: Market Size By Region, 20162023 (USD Million)
Table 25 Market Size By Deployment Type, 20162023 (USD Million)
Table 26 On-Premises: Market Size By Region, 20162023 (USD Million)
Table 27 Cloud: Market Size By Region, 20162023 (USD Million)
Table 28 In-Memory Data Grid Market Size, By Region, 20162023 (USD Million)
Table 29 North America: Market Size By Component, 20162023 (USD Million)
Table 30 North America: Market Size By Professional Service, 20162023 (USD Million)
Table 31 North America: Market Size By Business Application, 20162023 (USD Million)
Table 32 North America: In-Memory Data Grid Market Size By Industry Vertical, 20162023 (USD Million)
Table 33 North America: Market Size By Organization Size, 20162023 (USD Million)
Table 34 North America: Market Size By Deployment Type, 20162023 (USD Million)
Table 35 North America: Market Size By Country, 20162023 (USD Million)
Table 36 Europe: In-Memory Data Grid Market Size, By Component, 20162023 (USD Million)
Table 37 Europe: Market Size By Professional Service, 20162023 (USD Million)
Table 38 Europe: Market Size By Business Application, 20162023 (USD Million)
Table 39 Europe: In-Memory Data Grid Market Size By Industry Vertical, 20162023 (USD Million)
Table 40 Europe: Market Size By Organization Size, 20162023 (USD Million)
Table 41 Europe: Market Size By Deployment Type, 20162023 (USD Million)
Table 42 Europe: Market Size By Country, 20162023 (USD Million)
Table 43 Asia Pacific: In-Memory Data Grid Market Size, By Component, 20162023 (USD Million)
Table 44 Asia Pacific: Market Size By Professional Service, 20162023 (USD Million)
Table 45 Asia Pacific: Market Size By Business Application, 20162023 (USD Million)
Table 46 Asia Pacific: In-Memory Data Grid Market Size By Industry Vertical, 20162023 (USD Million)
Table 47 Asia Pacific: Market Size By Organization Size, 20162023 (USD Million)
Table 48 Asia Pacific: Market Size By Deployment Type, 20162023 (USD Million)
Table 49 Asia Pacific: Market Size By Country, 20162023 (USD Million)
Table 50 Middle East and Africa: In-Memory Data Grid Market Size, By Component, 20162023 (USD Million)
Table 51 Middle East and Africa: Market Size By Professional Service, 20162023 (USD Million)
Table 52 Middle East and Africa: Market Size By Business Application, 20162023 (USD Million)
Table 53 Middle East and Africa: In-Memory Data Grid Market Size By Industry Vertical, 20162023 (USD Million)
Table 54 Middle East and Africa: Market Size By Organization Size, 20162023 (USD Million)
Table 55 Middle East and Africa: Market Size By Deployment Type, 20162023 (USD Million)
Table 56 Middle East and Africa: Market Size By Country, 20162023 (USD Million)
Table 57 Latin America: In-Memory Data Grid Market Size, By Component, 20162023 (USD Million)
Table 58 Latin America: Market Size By Professional Service, 20162023 (USD Million)
Table 59 Latin America: Market Size By Business Application, 20162023 (USD Million)
Table 60 Latin America: Market Size By Industry Vertical, 20162023 (USD Million)
Table 61 Latin America: Market Size By Organization Size, 20162023 (USD Million)
Table 62 Latin America: In-Memory Data Grid Market Size By Deployment Type, 20162023 (USD Million)
Table 63 Latin America: Market Size By Country, 20162023 (USD Million)
Table 64 Product/Service/Solution Launches and Enhancements, 20152018
Table 65 Business Expansions, 20162018
Table 66 Acquisitions, 20152018
Table 67 Partnerships, 20142018
List of Figures (29 Figures)
Figure 1 Market Segmentation
Figure 2 In-Memory Data Grid Market: Research Design
Figure 3 Market Bottom-Up Approach
Figure 4 Market Top-Down Approach
Figure 5 Large Enterprises Segment Holds the Highest Market Share in the In-Memory Data Grid Market in 2018
Figure 6 Transaction Processing Segment Accounts for the Largest Market Size in 2018
Figure 7 North America Accounts for the Highest Share of the Market in 2018
Figure 8 Need for Distributed Data Storage Architecture for Faster Data Processing to Drive the Growth of the Market
Figure 9 Transaction Processing Segment and the US Account for the Highest Shares in the North America Market in 2018
Figure 10 Australia and New Zealand to Grow at the Highest Rate During the Forecast Period
Figure 11 In-Memory Data Grid Market: Drivers, Restraints, Opportunities, and Challenges
Figure 12 Professional Services Segment to Grow at A Higher CAGR During the Forecast Period
Figure 13 Education Segment to Grow at the Highest CAGR During the Forecast Period
Figure 14 Fraud and Risk Management Segment to Grow at the Highest CAGR During the Forecast Period
Figure 15 Consumer Goods and Retail Industry Vertical to Grow at the Highest CAGR During the Forecast Period
Figure 16 Small and Medium-Sized Enterprises Segment to Grow at A Higher CAGR During the Forecast Period
Figure 17 Cloud Segment to Grow at A Higher CAGR During the Forecast Period
Figure 18 North America to Hold the Highest Market Share in 2018
Figure 19 Asia Pacific to Grow at the Highest CAGR During the Forecast Period
Figure 20 North America: Market Snapshot
Figure 21 Asia Pacific: Market Snapshot
Figure 22 Key Developments By the Leading Players in the In-Memory Data Grid Market, 20142018
Figure 23 Geographic Revenue Mix of the Top Market Players
Figure 24 Oracle: Company Snapshot
Figure 25 IBM: Company Snapshot
Figure 26 Red Hat: Company Snapshot
Figure 27 Software AG: Company Snapshot
Figure 28 Pivotal: Company Snapshot
Figure 29 Hitachi: Company Snapshot
The study involved 4 major activities to estimate the current market size for in memory data grid market. Exhaustive secondary research was done to collect information on the market. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain using primary research. Both top-down and bottom-up approaches were employed to estimate the complete market size. Thereafter, the market breakup and data triangulation procedures were used to estimate the market size of the segments and subsegments of the market.
Secondary researchIn the secondary research process, various secondary sources, such as D&B Hoovers and Bloomberg BusinessWeek have been referred to for identifying and collecting information for this study. Secondary sources included annual reports, press releases and investor presentations of companies, whitepapers, certified publications and articles by recognized authors, gold standard and silver standard websites, regulatory bodies, trade directories, and databases.
Primary researchThe in memory data grid market comprises several stakeholders, such as in-memory data grid providers, system integrators, technology partners, research organizations, resellers and distributors, enterprise users, and technology providers. The demand-side of the market consists of enterprises across industry verticals: BFSI, media and entertainment, consumer goods and retail, healthcare and life sciences, manufacturing, telecom and IT, transportation and logistics, travel and hospitality, and energy and utilities. The supply-side includes in-memory data grid providers offering in-memory data grid solution and services. Various primary sources from both the supply and demand sides of the market were interviewed to obtain qualitative and quantitative information.
Following is the breakup of the primary respondents profiles:
To know about the assumptions considered for the study, download the pdf brochure
Market size estimationBoth top-down and bottom-up approaches were used to estimate and validate the total size of the in memory data grid market. These methods were also used extensively to estimate the size of various subsegments in the market. The research methodology used to estimate the market size includes the following:
- The key players in the industry and markets have been identified through extensive secondary research.
- The industrys supply chain and market size, in terms of value, have been determined through primary and secondary research processes.
- All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources.
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, the data triangulation and market breakup procedures were employed, wherever applicable. The data was triangulated by studying various factors and trends from demand and supply sides.
Report objectives- To define, describe, and forecast the in memory data grid market based on components, business applications, organization size, deployment types, industry verticals, and regions
- To provide detailed information about the major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the growth of the market
- To analyze the market with respect to individual growth trends, prospects, and contributions to the total market
- To forecast the market size of 5 main regions, namely, North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America
- To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the market
- To profile the key players in the market and comprehensively analyze their core competencies in each microsegment
- To analyze the competitive developments, such as agreements, alliances, joint ventures, and mergers and acquisitions, in the in memory data grid market
Scope of report
|
Report Metrics |
Details |
|
Market size available for years |
2016-2023 |
|
Base year considered |
2017 |
|
Forecast period |
20182023 |
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Forecast units |
Value (USD) |
|
Segments covered |
Component, Business Application, Industry Vertical, Organization Size, Deployment Type, and Region |
|
Geographies covered |
North America, Europe, APAC, and RoW |
|
Companies covered |
IBM (US), Oracle (US), Red Hat (US), Software AG (Germany), Pivotal (US), Hitachi (Japan), Hazelcast (US), TIBCO Software (US), GridGain Systems (US), ScaleOut Software (US), GigaSpaces (US), Alachisoft (US), and TmaxSoft (US) |
This research report categorizes the market to forecast revenues and analyze trends in each of the following submarkets:
On the basis of component, the in memory data grid market has been segmented as follows:- Solution
- Professional Services
- Transaction Processing
- Fraud and Risk Management
- Supply Chain Optimization
- Sales and Marketing Optimization
- Large Enterprises
- SMEs
- Cloud
- On-premises
- BFSI
- Media and Entertainment
- Consumer Goods and Retail
- Healthcare and Life Sciences
- Manufacturing
- Telecom and IT
- Transportation and Logistics
- Others (Travel and Hospitality, and Energy and Utilities)
- North America
- US
- Canada
- Europe
- UK
- Germany
- France
- Rest of Europe
- APAC
- Australia and New Zealand (ANZ)
- Japan
- China
- Rest of APAC
- MEA
- Kingdom of Saudi Arabia (KSA)
- South Africa
- United Arab Emirates
- Rest of MEA
- Latin America
- Brazil
- Mexico
- Rest of Latin America
With the given market data, MarketsandMarkets offers customizations as per the companys specific needs. The following customization options are available for the report:
Product analysis- Product matrix gives a detailed comparison of the product portfolio of each company
- Further breakup of the European in memory data grid market into countries
- Further breakup of the APAC market into countries
- Further breakup of the MEA market into countries
- Further breakup of the Latin American market into countries
- Detailed analysis and profiling of additional market players
Growth opportunities and latent adjacency in In-Memory Data Grid Market