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記憶體內資料網格：市場佔有率分析、產業趨勢與統計、成長預測（2025-2030）

In Memory Data Grid - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 115 Pages | 商品交期: 2-3個工作天內

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簡介目錄

記憶體內資料網格市場規模預計在 2025 年為 45.3 億美元，預計到 2030 年將達到 109.2 億美元，預測期內（2025-2030 年）的複合年成長率為 19.23%。

記憶體資料網格-市場-IMG1

隨著對即時詐欺和風險管理能力的需求不斷成長，記憶體內資料網格解決方案的採用預計也會成長。

主要亮點

  • 記憶體內資料網格解決方案因其能夠提供高速資料處理和分析能力而變得越來越流行。雲端運算的發展導致雲端基礎的記憶體內資料網格解決方案的採用率不斷提高，該解決方案提供了處理大量資料所需的靈活性和擴充性，而無需內部部署基礎設施。
  • 此外，疫情凸顯了即時資料處理和分析的重要性，這是記憶體內資料網格解決方案的關鍵特性。因此，各行各業的企業都開始投資這些解決方案，以便更快地做出決策並提高整體業務效率，從而推動市場需求。
  • 記憶體內資料網格解決方案的實現和管理很複雜，需要技術專長，這阻礙了技術資源有限的企業採用它們。此外，高成本和資料安全等因素進一步抑制了市場成長。
  • 疫情導致人們突然轉向遠端工作、電子商務和線上服務，從而對記憶體內資料網格解決方案的需求激增。隨著越來越多的人遠端工作，對可靠、高效的資料處理和分析解決方案的需求也隨之增加，從而導致對記憶體內資料網格產品的需求增加。
  • 然而，供應鏈中斷導致產品發布和交貨延遲，影響了市場成長。此外，企業面臨的 IT 預算減少和財務限制也導致記憶體內資料網格解決方案的採用率下降。

記憶體內資料網格市場趨勢

BFSI 對即時資料處理的需求不斷成長，推動市場成長

  • 數位化的興起迫使金融機構開發精益、靈活、有效率的方法來服務客戶。金融機構處理敏感訊息，如果處理不當，可能產生嚴重的財務和道德影響。因此，世界各地的金融機構都在轉向記憶體內資料網格解決方案來即時處理資料並為其業務關鍵型應用程式提供支援。
  • 雲端處理在 BFSI 行業中的日益普及也推動了對記憶體內資料網格的需求。雲端基礎的記憶體內資料網格解決方案比傳統的內部部署解決方案更靈活、擴充性且更具成本效益，是 BFSI 組織的正確選擇。
  • 此外，BFSI 行業對即時資料處理的需求日益成長，這推動了對記憶體內資料網格可在記憶體中儲存和處理大量資料、快速資料存取以及適合雲端基礎的部署。
  • 領先的銀行嚴重依賴 GridGain Systems Inc.（記憶體內資料網格的著名提供者之一）來提供整合的全通路銀行體驗。 GridGain 的解決方案使公司能夠提高數位管道的速度和規模，解鎖先前孤立的資料以便跨通路無縫共用，使用即時串流分析、機器學習和深度學習實現流程內 HTAP，並主動監控和增強端到端銀行體驗。
  • 此外，由於新冠疫情爆發，銀行內部和外部詐騙案件激增。新冠疫情救助計畫導致詐騙、虛假索賠和其他詐騙的增加。許多金融機構和政府機構實施的系統都要求對申請人的身份和索賠進行廣泛的驗證。例如，根據日本警察廳統計，2022年日本警方記錄了1,136起網路銀行詐騙案件，較前一年大幅增加。

預計北美將佔很大佔有率

  • 預計預測期內北美將佔據記憶體內資料網格市場的大部分佔有率。這是由於企業之間的監管合規性不斷提高，推動了整個企業採用記憶體內資料網格，顯示潛在的市場成長。
  • 該地區對記憶體內資料網格的採用日益廣泛，主要是由於對巨量資料快速處理和分析的需求激增，以及隨著各種資料來源的增加，簡化架構的需求日益成長。最佳化整體擁有成本的技術改進也是推動市場成長的因素之一。
  • 在美國，新商業洞察力的成長加上各種資料來源的增加正在促進市場擴張。公司正在利用巨量資料來增強行銷、客戶體驗，並透過識別詐欺和風險直接提高業務績效。據美國反保險詐欺聯盟稱，詐騙占美國和加拿大保險公司索賠成本的5-10％。一些保險公司估計總合可能高達索賠成本的 20%。北美所有業務線的成本估計在 800 億美元至 900 億美元之間。
  • 隨著醫療保健行業採用雲端運算來儲存電子健康記錄(EHR)資料和其他企業應用程式，該行業也正在成為主要的資料來源。例如，根據美國資料分析公司GNS Healthcare估計，美國醫療保健產業每年產生12億份臨床照護文件。因此，預計終端用戶行業資料的成長將推動即時處理，從而創造市場機會。
  • 知名參與者的存在，以及包括摩根大通、澳洲國民銀行、勞埃德銀行集團和瑞銀等許多世界領先金融機構在內的全球 2000 強企業的迅速採用，為該地區的產生收入做出了貢獻。

記憶體內資料網格產業概覽

記憶體內資料網格市場由各種供應商分割，例如 GridGain、Hazelcast、Software AG、Oracle Corporation、GigaSpaces Technologies Inc.為了擴大市場佔有率並保持競爭優勢，供應商正在實施有機和無機成長策略，例如夥伴關係、協作、新產品推出和併購。

2022 年 3 月，Hazelcast 宣布推出 InApps 技術，開放原始碼、輕量級、記憶體內流處理引擎，可近乎即時地處理資料密集型應用程式，例如智慧家居感測器、店內電子商務系統、社交媒體平台、日誌分析、監控和詐欺檢測。我們還發布了 Hazelcast IMDG 3.8 版本，具有管理持久性和多資料部署的高級功能。

2022 年 3 月，Hazelcast 在其記憶體內資料網格軟體中添加了更多 SQL 流資料功能和分層功能，允許同時查詢即時資訊和舊資訊。該公司基本上將大量資料儲存在記憶體中，以便能夠比從 SSD 或磁碟機順序讀取資料更快地存取、處理和分析資料。

其他福利：

  • Excel 格式的市場預測 (ME) 表
  • 3 個月的分析師支持

目錄

第 1 章 簡介

  • 研究假設和市場定義
  • 研究範圍

第2章調查方法

第3章執行摘要

第4章 市場洞察

  • 市場概況
  • 產業吸引力－波特五力分析
    • 購買者/消費者的議價能力
    • 供應商的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭對手之間的競爭強度
  • COVID-19 市場影響評估

第5章 市場動態

  • 市場促進因素
    • 需要前所未有的資料處理速度
    • 巨量資料的成長
  • 市場挑戰
    • 維護資料安全

第6章 市場細分

  • 按組件
    • 解決方案
    • 按服務
  • 依部署類型
    • 本地
    • 雲
  • 按最終用戶產業
    • BFSI
    • 資訊科技/通訊
    • 零售
    • 衛生保健
    • 運輸和物流
    • 其他最終用戶產業
  • 按地區
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲

第7章 競爭格局

  • 公司簡介
    • Hazelcast Inc.
    • GridGain Systems Inc.
    • Oracle Corporation
    • IBM Corporation
    • Pivotal（VMware Inc.）
    • GigaSpaces Technologies Inc.
    • Software AG
    • ScaleOut Software
    • Alachisoft
    • TIBCO Software Inc.

第8章投資分析

第9章：市場的未來

簡介目錄
Product Code: 71193

The In Memory Data Grid Market size is estimated at USD 4.53 billion in 2025, and is expected to reach USD 10.92 billion by 2030, at a CAGR of 19.23% during the forecast period (2025-2030).

In Memory Data Grid - Market - IMG1

As the need for real-time fraud and risk management capabilities continues to grow, the adoption of in-memory data grid solutions is expected to increase.

Key Highlights

  • In-memory data grid solutions have been increasingly gaining adoption due to their ability to provide high-speed data processing and analysis capabilities. With the growth of cloud computing, businesses are increasingly adopting cloud-based in-memory data grid solutions that provide the flexibility and scalability needed to handle large amounts of data without the need for on-premises infrastructure.
  • Furthermore, the pandemic emphasized the significance of real-time data processing and analysis, which is a key feature of in-memory data grid solutions. As a result, businesses in various industries began to invest in these solutions in order to enable faster decision-making and improve overall operational efficiency driving the demand in the market.
  • As the implementation and managing in-memory data grid solutions are complex and require technical expertise, their adoption from businesses with limited technical resources is hampering the market growth. Also, the factors such as higher cost and data security are further restraining the market growth.
  • The pandemic led to a sudden shift towards remote working, e-commerce, and online services, which has created a surge in demand for in-memory data grid solutions. With more people working remotely, the need for reliable and efficient data processing and analytics solutions has increased, leading to a rise in demand for in-memory data grid products.
  • However, the supply chain disruptions led to delays in product launches and delivery, which affected the growth of the market. Also, the reduced IT budgets and financial constraints faced by businesses resulted in a decrease in the adoption of in-memory data grid solutions.

In Memory Data Grid Market Trends

Growing Need for Real Time Data Processing in BFSI Driving the Market Growth

  • Growing digitalization is compelling financial companies to build a lean, flexible, and efficient approach to cater to their customers. Financial institutions deal with critical information, which, if not properly processed, can have severe financial and ethical implications. Thus, financial organizations worldwide seek in-memory data grid solutions to process data in real-time and improve their business-critical applications.
  • The growing adoption of cloud computing in the BFSI industry is also driving the demand for in-memory data grids, as cloud-based in-memory data grids solutions provide greater flexibility, scalability, and cost-effectiveness compared to on-premises traditional solutions making them a suitable option for BFSI organizations.
  • Furthermore, the growing need for real-time data processing in the BFSI industry is increasing the demand for in-memory data grids to store and process large volumes of data in memory, high-speed data access, and suitability for cloud-based deployments.
  • Leading banks significantly depend on GridGain Systems Inc., one of the prominent providers of In-memory data grids, to help them offer an integrated omnichannel banking experience. By using the GridGain solution, organizations have added speed and scale to digital channels, opened up previously siloed data for seamless sharing across channels, and implemented in-process HTAP using real-time streaming analytics, machine, and deep learning to monitor and enhance the end-to-end banking experience proactively.
  • Moreover, banks witnessed a sharp rise in internal and external fraud cases from the COVID-19 outbreak. The COVID-19 outbreak rescue package increased fraud, false claims, and other scams. Many of the systems that financial institutions and government agencies in place needed to verify the identity and claims of applicants adequately. For instance, according to National Police Agency Japan, the police in Japan recorded 1,136 online banking fraud cases in 2022, which constituted a substantial increase compared to the previous year.

North America is Expected to Hold Major Share

  • North America is expected to account for a larger share of the In-memory data grid market during the forecast period due to increasing regulatory compliances among organizations to boost in-memory data grid adoption across enterprises, indicating potential market growth.
  • The adoption of an in-memory data grid is rising in the region, primarily attributed to the burgeoning demand for faster processing and analytics on big data coupled with the need for simplifying architecture as the number of various data sources increases. Technology enhancements that optimize the total ownership cost are another factor driving the market growth.
  • The growth of new business insights contributes to expanding the market in the United States as various data sources increase. Multiple companies are leveraging big data to enhance marketing and customer experience and identify fraud and risk that can directly strengthen business performance. According to the US-based Coalition Against Insurance Fraud, fraud accounts for 5-10% of claims costs for American and Canadian insurers. Some insurers expect the total to be as high as 20% of the claims costs. Across all insurance lines in the North American region, the estimated cost is between USD 80 billion and USD 90 billion.
  • The healthcare industry, which embraces the cloud for its Electronic health record (EHR) data and other enterprise applications, is also becoming a great data source. For instance, according to GNS Healthcare, a US-based Data Analytics Company, the United States healthcare industry generates an estimated 1.2 billion clinical care documents annually. Hence, growth in data across end-user industries is anticipated to create real-time processing, thereby creating opportunities for the market.
  • The presence of a prominent player, which continues to see rapid adoption among Global 2000 organizations, including many of the world's leading financial institutions, such as JPMorgan Chase, National Australia Bank, Lloyds Banking Group, UBS, and many more, is contributing to the revenue generation in the region.

In Memory Data Grid Industry Overview

The In-Memory Data Grid market is fragmented consisting of various vendors such as GridGain, Hazelcast, Software AG, Oracle Corporation, GigaSpaces Technologies Inc., and others. Vendors are deploying several organic and inorganic growth strategies, such as partnerships and collaborations, new product launches, and mergers and acquisitions, to strengthen their presence and compete in the market.

In March 2022, Hazelcast launched an open-source lightweight in-memory stream processing engine InApps technology, to enable processing in near real-time for data-intensive applications such as smart home sensors, in-store e-commerce systems, social media platforms, log analysis, monitoring, and fraud detection. The company also released version 3.8 of Hazelcast IMDG, which includes advanced capabilities for managing persistence and multi-data center deployments.

In March 2022, Hazelcast added more SQL streaming data capabilities and tiering to its in-memory data grid software so that real-time and older information can be queried simultaneously. The company basically stores a load of data in memory so it can be accessed, processed, and analyzed much faster than by sequentially reading it from SSDs or disk drives.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Buyers/Consumers
    • 4.2.2 Bargaining Power of Suppliers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitute Products
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Assessment of the Impact of COVID-19 on the Market

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Need for Attaining Unprecedented Levels of Speed at Data Processing
    • 5.1.2 Growth of Big Data
  • 5.2 Market Challenges
    • 5.2.1 Maintaining Data Security

6 MARKET SEGMENTATION

  • 6.1 By Component
    • 6.1.1 Solution
    • 6.1.2 Services
  • 6.2 By Deployment Type
    • 6.2.1 On-premise
    • 6.2.2 Cloud
  • 6.3 By End-user Industry
    • 6.3.1 BFSI
    • 6.3.2 IT and Telecommunication
    • 6.3.3 Retail
    • 6.3.4 Healthcare
    • 6.3.5 Transportation and Logistics
    • 6.3.6 Other End-User Industries
  • 6.4 By Geography
    • 6.4.1 North America
    • 6.4.2 Europe
    • 6.4.3 Asia-Pacific
    • 6.4.4 Latin America
    • 6.4.5 Middle East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Hazelcast Inc.
    • 7.1.2 GridGain Systems Inc.
    • 7.1.3 Oracle Corporation
    • 7.1.4 IBM Corporation
    • 7.1.5 Pivotal (VMware Inc.)
    • 7.1.6 GigaSpaces Technologies Inc.
    • 7.1.7 Software AG
    • 7.1.8 ScaleOut Software
    • 7.1.9 Alachisoft
    • 7.1.10 TIBCO Software Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET