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Global In-Memory Data Grid Market to reach USD 6.08 billion by the end of 2030.

Global In-Memory Data Grid Market Size study & Forecast, by Component (Solution, Services) By Deployment Type (On-premise, Cloud) By End User Industry (BFSI, IT and Telecommunication, Retail, Healthcare, Transportation and Logistics, Others) and Regional Analysis, 2023-2030

Product Code: ICTICTI-69864329
Publish Date: 10-06-2023
Page: 200

Global In-Memory Data Grid Market is valued approximately USD 2.32 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 12.81% over the forecast period 2023-2030. An in-memory data grid is a distributed data management technology that stores and manages data in the main memory of multiple interconnected servers or nodes. IMDGs provide fast and efficient access to data, making them ideal for high-performance applications that require low latency and high throughput. The In-Memory Data Grid market is expanding because of factors such as increasing demand for cloud and growing demand of IoT devices. IMDGs are often used in applications that require real-time data processing, such as financial trading, online gaming, and e-commerce. They can also be used in big data applications where large volumes of data need to be processed and analyzed quickly. Its importance has progressively increased during the last few decades.

According to the Statista, the market for cloud applications was worth USD 133.6 billion in 2021 and is projected to grow to USD 168.6 billion by 2025. A compound annual growth rate of 4.8% is predicted for the cloud applications software market. Furthermore, In the fourth quarter of 2022, the most popular vendor in the cloud infrastructure services market, Amazon Web Services accounts 32% of the entire market followed by Microsoft Azure with 23% of market share, and Google Cloud with 10% market share. Another important component driving space increase is demand of IoT devices. As per Statista, the number of Internet of Things devices worldwide is forecast to almost triple from 9.7 billion in 2020 to more than 29 billion IoT devices in 2030. In 2030, the highest number of IoT devices will be found in China with around 5 billion consumer devices. In addition, the consumer sector is anticipated to dominate in terms of number of Internet of Things connected devices in 2030, with 17 billion connected devices worldwide. Also, growing technological advancement in Big Data and rising number of social media platforms would create a lucrative growth prospectus for the market over the forecast period. However, the high cost of In-Memory Data Grid stifles market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global In-Memory Data Grid Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the increasing adoption of digitalization and technological advancement in the region. According to the Statista, the memory integrated circuits market in north America is estimated to reach USD 25.89 billion in 2023. Asia Pacific is expected to grow significantly during the forecast period, owing to factors such as growing number of enterprises, particularly in China, Japan, and India, are focusing on managing massive volumes of data effectively in the market space.

Major market player included in this report are:
GigaSpaces Technologies Inc
GridGain Systems, Inc.
Hazelcast, Inc.
IBM Corporation
Oracle Corporation
Parallel Universe
Red Hat, Inc.
ScaleOut Software
Software AG
TIBCO Software Inc.

Recent Developments in the Market:
Ø In November 2022, IBM has launched a new software designed to help enterprises break down data and analytics silos. IBM Business Analytics Enterprise offers users a comprehensive view of data sources throughout their whole business through a set of business intelligence planning, budgeting, reporting, forecasting, and dashboard capabilities. This package also includes a new IBM Analytics Content Hub that makes it easier for customers to find and access analytics and planning tools from many vendors in a single, customized dashboard view.
Global In-Memory Data Grid Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Component, Deployment Type, End User Industry, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component

By Deployment Type

By End User Industry
IT and Telecommunication
Transportation and Logistics

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. In-Memory Data Grid Market, by Region, 2020-2030 (USD Billion)
1.2.2. In-Memory Data Grid Market, by Component, 2020-2030 (USD Billion)
1.2.3. In-Memory Data Grid Market, by Deployment Type, 2020-2030 (USD Billion)
1.2.4. In-Memory Data Grid Market, by End User Industry, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global In-Memory Data Grid Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global In-Memory Data Grid Market Dynamics
3.1. In-Memory Data Grid Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Increasing demand for Cloud Growing demand of IoT devices
3.1.2. Market Challenges Maintaining Data Security of In-Memory Data Grid
3.1.3. Market Opportunities Growing technological advancement in Big Data Rising number of social media platforms
Chapter 4. Global In-Memory Data Grid Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global In-Memory Data Grid Market, by Component
5.1. Market Snapshot
5.2. Global In-Memory Data Grid Market by Component, Performance – Potential Analysis
5.3. Global In-Memory Data Grid Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. In-Memory Data Grid Market, Sub Segment Analysis
5.4.1. Solution
5.4.2. Services
Chapter 6. Global In-Memory Data Grid Market, by Deployment Type
6.1. Market Snapshot
6.2. Global In-Memory Data Grid Market by Deployment Type, Performance – Potential Analysis
6.3. Global In-Memory Data Grid Market Estimates & Forecasts by Deployment Type 2020-2030 (USD Billion)
6.4. In-Memory Data Grid Market, Sub Segment Analysis
6.4.1. On-premise
6.4.2. Cloud
Chapter 7. Global In-Memory Data Grid Market, by End User Industry
7.1. Market Snapshot
7.2. Global In-Memory Data Grid Market by End User Industry, Performance – Potential Analysis
7.3. Global In-Memory Data Grid Market Estimates & Forecasts by End User Industry 2020-2030 (USD Billion)
7.4. In-Memory Data Grid Market, Sub Segment Analysis
7.4.1. BFSI
7.4.2. IT and Telecommunication
7.4.3. Retail
7.4.4. Healthcare
7.4.5. Transportation and Logistics
7.4.6. Others
Chapter 8. Global In-Memory Data Grid Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. In-Memory Data Grid Market, Regional Market Snapshot
8.4. North America In-Memory Data Grid Market
8.4.1. U.S. In-Memory Data Grid Market Component breakdown estimates & forecasts, 2020-2030 Deployment Type breakdown estimates & forecasts, 2020-2030 End User Industry breakdown estimates & forecasts, 2020-2030
8.4.2. Canada In-Memory Data Grid Market
8.5. Europe In-Memory Data Grid Market Snapshot
8.5.1. U.K. In-Memory Data Grid Market
8.5.2. Germany In-Memory Data Grid Market
8.5.3. France In-Memory Data Grid Market
8.5.4. Spain In-Memory Data Grid Market
8.5.5. Italy In-Memory Data Grid Market
8.5.6. Rest of Europe In-Memory Data Grid Market
8.6. Asia-Pacific In-Memory Data Grid Market Snapshot
8.6.1. China In-Memory Data Grid Market
8.6.2. India In-Memory Data Grid Market
8.6.3. Japan In-Memory Data Grid Market
8.6.4. Australia In-Memory Data Grid Market
8.6.5. South Korea In-Memory Data Grid Market
8.6.6. Rest of Asia Pacific In-Memory Data Grid Market
8.7. Latin America In-Memory Data Grid Market Snapshot
8.7.1. Brazil In-Memory Data Grid Market
8.7.2. Mexico In-Memory Data Grid Market
8.8. Middle East & Africa In-Memory Data Grid Market
8.8.1. Saudi Arabia In-Memory Data Grid Market
8.8.2. South Africa In-Memory Data Grid Market
8.8.3. Rest of Middle East & Africa In-Memory Data Grid Market

Chapter 9. Competitive Intelligence
9.1. Key Company SWOT Analysis
9.1.1. Company 1
9.1.2. Company 2
9.1.3. Company 3
9.2. Top Market Strategies
9.3. Company Profiles
9.3.1. GigaSpaces Technologies Inc Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
9.3.2. GridGain Systems, Inc.
9.3.3. Hazelcast, Inc.
9.3.4. IBM Corporation
9.3.5. Oracle Corporation
9.3.6. Parallel Universe
9.3.7. Red Hat, Inc.
9.3.8. ScaleOut Software
9.3.9. Software AG
9.3.10. TIBCO Software Inc.

Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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