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1435937

記憶體內運算資料庫：市場佔有率分析、產業趨勢與統計、成長預測（2024-2029）

In Memory Data Grid - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
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

記憶體內運算資料庫市場規模預計到 2024 年為 38 億美元，預計到 2029 年將達到 91.7 億美元，預測期內（2024-2029 年）複合年成長率為 19.23%。

記憶體資料格 - 市場

隨著對即時詐欺和風險管理能力的需求增加，記憶體內運算資料庫解決方案的採用預計會增加。

主要亮點

  • 記憶體內運算資料庫解決方案擴大被採用，因為它們可以提供高速資料處理和分析能力。隨著雲端運算的發展，企業擴大採用基於雲端基礎的記憶體內運算資料庫解決方案，這些解決方案提供了處理大量資料所需的彈性和擴充性，而無需本地基礎設施。
  • 此外，疫情凸顯了即時資料處理和分析的重要性，這是記憶體內運算資料庫解決方案的關鍵特徵。因此，各行業的公司已開始投資這些解決方案，以加快決策速度並提高整體營運效率，從而推動市場需求。
  • 實施和管理記憶體內運算資料庫解決方案非常複雜，需要技術專業知識，這阻礙了技術資源有限的公司採用的市場成長。此外，高成本和資料安全等因素進一步限制了市場的成長。
  • 疫情促使人們迅速轉向遠端工作、電子商務和線上服務，從而促使對記憶體內運算資料庫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不斷增加，從而增加了對記憶體內運算資料庫產品的需求。
  • 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，IT 預算的減少和企業面臨的財務限制也減少了記憶體內運算資料庫解決方案的採用。

記憶體內運算資料庫市場趨勢

BFSI 對即時資料處理不斷成長的需求推動了市場成長

  • 日益數位化迫使金融公司開發精益、靈活和高效的方法來服務客戶。金融機構處理敏感訊息，如果處理不當，可能會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尋求記憶體內運算資料庫解決方案來即時處理資料並改進關鍵業務應用程式。
  • BFSI 行業擴大採用雲端運算也推動了記憶體內運算資料庫的成長，因為與本地傳統解決方案相比，雲端基礎的記憶體資料網格解決方案提供了更大的彈性、擴充性和成本效率。需求。這些是 BFSI 組織的合適選擇。
  • 此外，BFSI 產業對即時資料處理的需求不斷成長，增加了對記憶體內運算資料庫、快速資料存取以及雲端基礎的部署在記憶體中儲存和處理大量資料的適用性的需求。
  • 主要銀行嚴重依賴 GridGain Systems Inc. （記憶體內運算資料庫的著名提供者之一）來幫助提供統一的全通路銀行業務體驗。透過 GridGain 解決方案，組織可以提高數位管道的速度和規模，開放先前孤立的資料並跨通路無縫共用，並利用即時串流分析、機器和深度學習。使用進程內 HTAP 主動交付端到端的銀行業務體驗。
  • 此外，由於 COVID-19感染疾病，銀行內部和外部詐欺案件數量急劇增加。 COVID-19感染疾病帶來的救濟措施導致詐騙、虛假申請和其他詐騙增加。金融機構和政府機構建立的許多系統都要求對申請人的身份和申請進行適當的驗證。例如，根據警察廳的數據，日本警方2022年記錄了1,136起網路銀行業務詐騙案件，與前一年相比大幅增加。

預計北美將佔據主要佔有率

  • 由於各組織之間的監管合規性不斷增強，以推動企業採用記憶體內運算資料庫記憶體內運算資料庫，預計北美在預測期內將在記憶體資料網格市場中佔據更大佔有率，這表明該市場的潛在成長。
  • 該地區記憶體內運算資料庫的採用正在增加。這主要是由於對巨量資料的高速處理和分析的需求激增，再加上不同資料來源數量的增加而促使架構簡化的需求。增強技術以最佳化整體擁有成本也是推動市場成長的因素。
  • 隨著各種資料來源的增加，新業務洞察的成長將有助於美國市場的擴張。公司正在利用巨量資料來增強行銷和客戶體驗，並識別可以直接提高業務成果的詐欺和風險。根據美國反保險詐欺聯盟的統計，美國和加拿大保險公司 5% 到 10% 的保險申請成本是由於詐騙造成的。一些保險公司估計總金額高達申請成本的 20%。如果將北美地區所有保險產品總合，預計成本在800億美元至900億美元之間。
  • 醫療保健產業也正在成為領先的資料來源，採用雲端來儲存電子健康記錄 (EHR)資料和其他企業應用程式。例如，根據美國資料分析公司 GNS Healthcare 的數據，美國美國產業每年大約產生 12 億份臨床護理文件。因此，最終用戶行業資料的增加預計將創造即時處理，從而為市場創造機會。
  • 該地區知名企業的存在繼續得到全球 2000 家組織的快速採用，其中包括許多世界領先的金融機構，如摩根大通、澳大利亞國民銀行、勞埃德銀行業務集團和瑞銀產生收入。

記憶體內運算資料庫產業概述

記憶體內運算資料庫市場是分散的，由不同的供應商組成，例如 GridGain、Hazelcast、Software AG、Oracle Corporation 和 GigaSpaces Technologies Inc。供應商正在部署多種有機和無機成長策略，例如夥伴關係和協作、新產品發布以及併購，以增強其影響力並在市場中競爭。

2022 年 3 月 Hazelcast 宣布推出 InApps 技術，這是一種開放原始碼、輕量級記憶體內流處理引擎，可實現智慧家居感測器、店內電子商務系統和社交媒體平台等資料密集型應用程式的近即時處理。 。 、日誌分析、監控和詐欺偵測。該公司還發布了 Hazelcast IMDG 3.8 版本。它包括用於管理持久性和多資料部署的高級功能。

2022 年 3 月 Hazelcast 為其記憶體內運算資料庫軟體新增了 SQL 串流資料功能和分層功能，以便您可以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，這使得存取、處理和分析資料的速度比從 SSD 或磁碟機順序讀取資料快得多。

其他福利

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

目錄

第1章 簡介

  • 研究假設和市場定義
  • 調查範圍

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
    • 買方議價能力
    • 供應商的議價能力
    • 新進入者的威脅
    • 替代產品的威脅
    • 競爭公司之間的敵意強度
  • 評估新型冠狀病毒感染疾病（COVID-19）對市場的影響

第5章市場動態

  • 市場促進因素
    • 對達到前所未有的資料處理速度水準的需求日益成長
    • 巨量資料的成長
  • 市場課題
    • 維護資料安全

第6章市場區隔

  • 依成分分類
    • 解決方案
    • 服務
  • 依部署類型
    • 本地
    • 雲
  • 依最終用戶產業
    • 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
  • 依地區
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲

第7章 競爭形勢

  • 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 3.80 billion in 2024, and is expected to reach USD 9.17 billion by 2029, growing at a CAGR of 19.23% during the forecast period (2024-2029).

In Memory Data Grid - Market

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