全球記憶體資料庫市場規模（按地區、範圍和預測）

記憶體資料庫的市場規模與預測

記憶體資料庫市場規模預計在 2024 年價值 98.4 億美元，到 2031 年將達到 355.2 億美元，2024 年至 2031 年的複合年增長率為 19.20%。記憶體資料庫 (IMDB) 是一種資料庫管理系統，它將資料儲存在電腦的主記憶體 (RAM) 而不是硬碟上。記憶體存取時間明顯快於磁碟 I/O 操作，因此可以更快地定位和處理資料。 IMDB 通常用於需要即時數據處理和高吞吐量的應用程序，例如金融交易、通訊、遊戲和分析平台。與依賴磁碟持久性的傳統資料庫不同，IMDB 透過快照和複製等技術提供資料持久性。

記憶體資料庫有望在資料驅動技術的發展中發揮關鍵作用。隨著人工智慧、大數據分析和物聯網 (IoT) 等領域對更快資料處理的需求不斷增長，IMDB 將在提供低延遲效能方面發揮關鍵作用。由於 RAM 成本的下降以及非揮發性記憶體技術的發展，IMDB 的採用預計會增加。此外，融合記憶體和磁碟儲存的混合資料庫可能會變得更加普遍，為各種用例提供速度和耐用性的平衡。

全球記憶體資料庫市場動態

主要市場驅動因子

對即時分析的需求不斷增加：

對即時資料處理和分析的需求不斷增長是記憶體資料庫市場發展的關鍵推動因素。根據Gartner的研究，到2025年，70%的新企業應用程式將使用低程式碼或無程式碼技術，其中許多將利用記憶體資料庫進行即時資料處理。此外，IDC 預測，到 2025 年，所有資料中將有近 30% 是即時產生的，這凸顯了記憶體資料庫快速資料處理能力的需求。

物聯網和大數據技術的採用日益廣泛：

物聯網 (IoT) 設備的普及加上大數據的迅猛增長，推動了對更有效率的資料管理解決方案的需求。國際數據公司（IDC）預測，到2025年，將有416億台物聯網設備實現聯網，產生79.4ZB的數據。大量資料的湧入需要能夠高速管理大量資訊的高效能資料庫，這使得記憶體資料庫成為處理物聯網和大數據應用的公司的一個有吸引力的選擇。

醫療保健和生命科學領域的需求不斷增長：

記憶體資料庫在基因組研究、病患資料分析和藥物發現的應用越來越多。根據美國國立衛生研究院 (NIH) 的數據，人類基因組定序的成本已從 2001 年的 1 億美元降至 2020 年的 1,000 美元，從而促使基因組數據大量增加。為了有效分析如此大量的數據，需要一個強大的記憶體資料庫。根據 Global Industry Insights 的數據，到 2024 年醫療分析產業規模預計將接近 500 億美元，其中很大一部分依賴記憶體資料庫進行即時患者數據分析和預測建模。

主要問題

資料波動性與持久性：

記憶體資料庫面臨保證資料壽命的問題，因為它們主要依賴易失性 RAM。除非有適當的持久保存方法，否則系統崩潰或斷電可能會導致資料完全遺失。實施頻繁的磁碟快照和交易日誌等措施可以幫助降低這種風險，但通常會以效能為代價。維護資料一致性和故障後復原會增加複雜性，從而降低記憶體資料庫的優勢，尤其是對於高可用性應用程式而言。

查詢最佳化複雜性：

記憶體資料庫中的查詢最佳化比典型的基於磁碟的資料庫更為複雜。儘管資料在 RAM 中可用且查詢速度通常很快，但是低效的查詢或糟糕的索引可能會降低效能。為了充分發揮記憶體資料庫的潛力，開發人員需要仔細考慮如何格式化、索引和搜尋他們的資料。這種複雜性需要專業知識和技能，這增加了對高技能資料庫管理員的需求，並可能為公司帶來招募和培訓問題。

對大規模資料分析的支持有限：

記憶體資料庫以其快速的事務處理而聞名，但在處理複雜、大規模資料分析時卻有其限制。記憶體是一個固有的限制，由於需要管理不斷增長的資料集，記憶體很快就會成為瓶頸。一些混合解決方案旨在將大型資料集卸載到磁碟，但這會降低效能。對於需要對海量資料集進行高級分析的企業來說，記憶體資料庫可能不夠用，他們必須使用並行系統和將記憶體操作與基於磁碟的儲存相結合的複雜架構。

主要趨勢：

混合記憶體架構：

為了因應不斷上漲的 RAM 成本，混合記憶體架構在記憶體資料庫公司中越來越受歡迎。這些架構將 RAM 與非揮發性記憶體 (NVM) 或固態硬碟 (SSD) 結合，以實現效能和成本效益的平衡。這種趨勢使得公司能夠將重要資料儲存在 RAM 中，同時將不常存取的資料儲存在更具成本效益的 NVM 中。混合架構為希望擴展記憶體資料庫而又無需承擔高昂硬體成本的企業提供了一種經濟高效的解決方案，使其更容易被更廣泛的行業所使用。

雲採用：

雲端運算的興起正在加速記憶體資料庫即服務 (DBaaS) 的採用。 AWS、Azure 和 Google Cloud 等雲端供應商提供託管記憶體資料庫解決方案，讓企業無需進行昂貴的基礎架構投資即可從這些高效能系統中受益。基於雲端的記憶體資料庫的可擴展性、靈活性和即用即付模式對於希望降低前期成本和營運複雜性的企業來說具有吸引力。隨著越來越多的企業採用雲，記憶體 DBaaS 預計將成為主流。

邊緣運算與物聯網整合：

隨著物聯網 (IoT) 和邊緣運算變得越來越普及，記憶體資料庫對於處理更靠近來源的資料變得越來越重要。設備和感測器會產生大量即時數據，這些數據需要低延遲處理才能在製造業、交通運輸和智慧城市等行業做出關鍵決策。記憶體資料庫能夠即時處理和分析數據，是邊緣運算應用的理想選擇。隨著企業希望透過處理邊緣資料而不是僅僅依賴集中式雲端服務來優化營運並最大限度地減少延遲，這個想法正在獲得支持。

目錄

第 1 章簡介

• 市場定義
• 市場區隔
• 研究方法

第 2 章執行摘要

• 主要發現 市場概況 市集亮點

第3章 市場概覽

• 市場規模與成長潛力
• 市場趨勢
• 市場推動因素
• 市場限制
• 市場機會
• 波特五力分析

第 4 章 記憶體資料庫市場（依應用程式劃分）

• 交易處理
• 報告與分析
• 其他應用

第 5 章 記憶體資料庫市場（按處理類型）

• 線上事務處理 (OLTP)
• 線上分析處理 (OLAP)

第 6 章 記憶體資料庫市場（依部署模式）

• 本地
• 基於雲端

第 7 章 區域分析

• 北美洲
• 美國
• 加拿大
• 墨西哥
• 歐洲
• 英國
• 德國
• 法國
• 義大利 亞太地區
• 中國
• 日本
• 印度
• 澳大利亞
• 拉丁美洲
• 巴西
• 阿根廷
• 智利
• 中東和非洲
• 南非
• 沙烏地阿拉伯
• 阿拉伯聯合大公國

第 8 章 市場動態

• 市場推動因素
• 市場限制
• 市場機會
• COVID-19 市場影響

第 9 章 競爭格局

• 大型公司
• 市佔率分析

第10章 公司簡介

• IBM
• Microsoft
• Oracle
• SAP
• Teradata
• Aerospike
• Altibase
• Couchbase
• DataStax
• GridGain
• TIBCO Software
• Redis Labs
• VoltDB

第 11 章 市場展望與機會

• 新興技術
• 未來市場趨勢
• 投資機會

第 12 章附錄

• 縮寫列表
• 來源與參考文獻

In-Memory Database Market Size And Forecast

The In-Memory Database Market size was valued at USD 9.84 Billion in 2024 and is projected to reach USD 35.52 Billion by 2031 , growing at a CAGR of 19.20% from 2024 to 2031. An In-Memory Database (IMDB) is a database management system that stores data in a computer's main memory (RAM) rather than on a hard drive. Due to memory access times being substantially faster than disk I/O operations, data retrieval and processing can be completed more quickly. IMDBs are commonly used in applications requiring real-time data processing and high throughput, such as financial trading, telecommunications, gaming, and analytics platforms. Unlike traditional databases, which rely on disk durability, IMDBs provide data persistence through techniques such as snapshotting and replication.

In terms of in-memory databases are expected to play an important part in the evolution of data-driven technology. As the demand for quicker data processing increases in domains such as artificial intelligence, big data analytics, and the Internet of Things (IoT), IMDBs will play an important role in providing low-latency performance. With the falling cost of RAM and developments in non-volatile memory technologies, IMDB adoption is projected to increase. Furthermore, hybrid databases that blend in-memory and disk-based storage may become more common, providing a balance of speed and persistence for a variety of use cases.

Global In-Memory Database Market Dynamics

The key market dynamics that are shaping the global in-memory database market include:

Key Market Drivers:

Increased Demand for Real-Time Analytics:

The increased demand for real-time data processing and analytics is a key driver of the in-memory database market. According to Gartner research, by 2025, 70% of new enterprise apps will use low-code or no-code technologies, with many relying on in-memory databases for real-time data processing. Furthermore, IDC projects that by 2025, nearly 30% of all data will be generated in real time, underscoring the need for in-memory databases' quick data processing capabilities.

Rising adoption of IoT and big data technologies:

The proliferation of Internet of Things (IoT) devices, combined with the exponential expansion of big data, is driving demand for more efficient data management solutions. The International Data Corporation (IDC) projects that by 2025, there will be 41.6 billion linked IoT devices, creating 79.4 zettabytes of data. This tremendous influx of data necessitates high-performance databases capable of managing vast amounts of information fast, making in-memory databases an appealing option for enterprises dealing with IoT and big data applications.

Rising Demand in Healthcare and Life Sciences:

In-memory databases are increasingly used in genomics research, patient data analysis, and medication discovery. According to the National Institutes of Health (NIH), the cost of sequencing a human genome has fallen from $100 million in 2001 to $1,000 in 2020, resulting in a massive increase in genomic data. To analyze this massive amount of data efficiently, strong in-memory databases are required. The Global Industry Insights research estimates that the healthcare analytics industry will approach $50 billion by 2024, with a sizable share relying on in-memory databases for real-time patient data analysis and predictive modeling.

Key Challenges:

Data Volatility and Durability:

In-memory databases confront issues in assuring data longevity because they rely mostly on volatile RAM. A system crash or power outage might result in total data loss unless suitable persistence methods are in place. Implementing measures like frequent disk snapshots or transaction logging can help to limit this risk, but they often come at a performance cost. Preserving data consistency and recovery after failures increases complexity and may reduce some of the benefits of in-memory databases, particularly in high-availability applications.

Complexity in Query Optimization:

Query optimization in in-memory databases can be more sophisticated than in typical disk-based databases. While the data is available in RAM and query speeds are often rapid, inefficiencies in querying or poor indexing might cause performance to decrease. To fully realize the possibilities of an in-memory database, developers must carefully consider how data is formatted, indexed, and searched. This complexity necessitates specialized knowledge and skills, raising the demand for highly skilled database administrators, which can pose a hiring and training issue for businesses.

Limited Support for Large-Scale Data Analytics:

Although in-memory databases are noted for their quick transaction processing, their capacity to handle complicated, large-scale data analytics is sometimes constrained. Memory can quickly become a bottleneck due to its intrinsic constraints and the need to manage ever-increasing datasets. Some hybrid solutions aim to offload huge datasets to disk; however, this can degrade performance. Companies that require advanced analytics on enormous datasets may find in-memory databases insufficient, necessitating the use of parallel systems or complex architectures that combine in-memory operations and disk-based storage.

Key Trends:

Hybrid Memory Architectures:

In reaction to the increasing cost of RAM, hybrid memory architectures are gaining popularity in the in-memory database companies. These architectures combine RAM with non-volatile memory (NVM) or solid-state drives (SSD) to achieve a balance of performance and cost-effectiveness. This trend enables enterprises to store less often accessible data on more cost-effective NVM while preserving vital data in RAM. Hybrid architectures offer a cost-effective solution for businesses wishing to extend their in-memory databases without incurring prohibitively high hardware expenses, making them more accessible to a broader variety of industries.

Cloud Adoption:

The increased popularity of cloud computing is accelerating the adoption of in-memory databases as a service (DBaaS). Cloud providers such as AWS, Azure, and Google Cloud provide managed in-memory database solutions, allowing organizations to benefit from these high-performance systems without the need for costly infrastructure expenditures. The scalability, flexibility, and pay-as-you-go pricing model of cloud-based in-memory databases makes them appealing to enterprises aiming to reduce upfront costs and operating complexity. As more businesses go to the cloud, in-memory DBaaS is projected to become the dominant trend.

Edge Computing and IoT Integration:

As the Internet of Things (IoT) and edge computing grow in popularity, in-memory databases are becoming increasingly important for processing data closer to its source. Devices and sensors generate huge amounts of real-time data, which necessitates low-latency processing for important decision-making in industries such as manufacturing, transportation, and smart cities. As of their capacity to process and analyze data in real-time, in-memory databases are ideal for edge computing applications. This idea is gaining traction as organizations seek to optimize operations and minimize latency by processing data at the edge rather than relying only on centralized cloud services.

What's inside a VMR industry report?

Our reports include actionable data and forward-looking analysis that help you craft pitches, create business plans, build presentations and write proposals.

Global In-Memory Database Market Regional Analysis

Here is a more detailed regional analysis of the global in-memory database market:

North America:

North America continues to lead the in-memory database market, owing to the region's rapid adoption of new technology and the presence of big IT behemoths. North America is expected to dominate the market over this time period, owing to major expenditures in data-intensive technologies. The U.S. Bureau of Labor Statistics predicts a 15% increase in computer and information technology occupations between 2021 and 2031, showing a growing demand for data management solutions. According to a NewVantage Partners poll, 91.9% of major firms are expanding their investments in big data and artificial intelligence, cementing North America's position as a hub for in-memory database consumption.

The proliferation of in-memory databases in North America, the growth of data-centric industries, along a strong push for digital transformation, are driving firms to seek faster and more efficient data processing solutions. The U.S. Federal Data Strategy 2021 Action Plan emphasizes the government's emphasis on improving data-driven decision-making, hence stimulating the market. Furthermore, the COVID-19 pandemic has expedited the digitalization of company operations and consumer contacts, resulting in increased demand for high-performance database technologies such as in-memory databases to allow real-time analytics and rapid decision-making.

Asia-Pacific:

The Asia-Pacific region is experiencing enormous growth in the in-memory database market, owing to its large population, rapid urbanization, and increasing digitization. According to the Asian Development Bank (ADB), Southeast Asia's digital economy is predicted to reach USD 1 trillion by 2030, up from USD 174 billion in 2021, indicating a growing demand for superior data management solutions. China's developing big data market, valued at around USD 10 billion in 2020 with a 16.0% growth rate, and India's Digital India plan, which seeks to propel the digital economy to USD 1 trillion by 2025, highlight the region's growing demand for high-performance databases.

The in-memory database market is rapidly expanding in Asia-Pacific. The COVID-19 pandemic has hastened the region's digital transformation, resulting in a huge increase in digital adoption, with McKinsey reporting that Asia-Pacific achieved a decade's worth of growth in just 90 days. This transition generates a strong demand for rapid and effective data processing solutions.

Furthermore, urbanization trends, with the United Nations forecasting that 66% of Asia's population will live in urban regions by 2050, are boosting the demand for enhanced data management in smart city programs. Countries such as Singapore, Hong Kong, and South Korea are at the forefront of cloud adoption, establishing a solid platform for the integration of in-memory database technology and accelerating market growth.

Global In-Memory Database Market: Segmentation Analysis

The Global In-Memory Database Market is Segmented on the basis of Industry Size, End User, Data Type, And Geography.

In-Memory Database Market, By Industry Size

• Small
• Medium
• Large

Based on Industry Size, the market is fragmented into small, medium, and large. The large segment dominates the in-memory database market due to its demand for high-performance, scalable solutions capable of handling massive data volumes and complicated analytics. Large organizations make significant investments in these complex databases to meet their substantial real-time data processing and integration needs. The medium-sized market is fast expanding as companies in this category increasingly use in-memory databases to improve their data processing capabilities. Medium-sized businesses are drawn to these solutions due to their cost-effectiveness and performance, allowing them to harness real-time analytics and enhance productivity without incurring the financial burden that comes with large-scale projects.

In-Memory Database Market, By End User

• BFSI
• Retail
• Logistics

Based on End User, the market is segmented into BFSI, Retail, and Logistics. The BFSI (Banking, Financial Services, and Insurance) segment leads the in-memory database market due to its important need for real-time data processing, fraud detection, and transaction management. Financial firms demand high-performance databases to efficiently process massive amounts of transactions and complicated analytical queries. The retail industry is expanding rapidly as more businesses use in-memory databases to improve consumer experiences through real-time inventory management, tailored marketing, and dynamic pricing tactics. The demand for immediate data access and analysis to support flawless operations and increase customer engagement is driving tremendous growth in this category.

In-Memory Database Market, By Data Type

• Relational
• NoSQL
• NewSQL

Based on Data Type, the market is divided into Relational, NoSQL, and NewSQ. The Relational sector dominates due to its robust support for structured data and complicated queries. Relational databases have strong consistency, ACID (Atomicity, Consistency, Isolation, Durability) qualities, and comprehensive integration capabilities, making them a popular choice for businesses with traditional data management requirements and high transaction volumes. The NoSQL market is rapidly expanding due to its capacity to handle unstructured or semi-structured data, making it perfect for applications that require scalability and rapid data access. This growth is being driven by the growing need for real-time analytics and big data processing across industries.

In-Memory Database Market, By Geography

• North America
• Europe
• Asia Pacific
• Rest of the world
• On the basis of geographical analysis, the Global In-Memory Database Market is classified into North America, Europe, Asia Pacific, and rest of the world. North America is currently dominating the in-memory database market, owing to a large presence of key technological companies and the early adoption of novel technologies. This region has a well-established IT infrastructure and a large number of businesses that use in-memory databases for real-time analytics, high-performance transaction processing, and other demanding applications. Asia-Pacific is the fastest-growing region for in-memory databases. This expansion is being driven by rapid economic development, more digitalization, and the expanding adoption of cloud-based technologies. The region's enormous population and various businesses create substantial prospects for in-memory database providers.

Key Players

• The "Global In-Memory Database Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are
• IBM, Microsoft, Oracle, SAP, Redis, VoltDB, Aerospike, Amazon Redshift, Google Big Query, and Azure Synapse Analytics
• . The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

• In-Memory Database Market Recent Developments
• In May 2022, IBM and SAP announced an extension of their collaboration to optimize business operations using RISE and SAP S/4HANA Cloud.
• In December 2022, the National Stock Exchange, India's largest stock exchange, selected the Raima Database Manager (RDM) Workgroup 12.0 in-memory system as a core component for the next versions of its trading platform front-end, the National Exchange for Automated Trading (NEAT).
• In November 2022, Redis, a provider of real-time in-memory databases, and Amazon Web Services announced a multi-year strategic partnership.

TABLE OF CONTENTS

1. Introduction

• Market Definition
• Market Segmentation
• Research Methodology

2. Executive Summary

• Key Findings
• Market Overview
• Market Highlights

3. Market Overview

• Market Size and Growth Potential
• Market Trends
• Market Drivers
• Market Restraints
• Market Opportunities
• Porter's Five Forces Analysis

4. In-Memory Database Market, By Application

• Transaction processing
• Reporting and analytics
• Other applications

5. In-Memory Database Market, By Processing Type

• Online Transaction Processing (OLTP)
• Online Analytical Processing (OLAP)

6. In-Memory Database Market, By Deployment Model

• On-premise
• Cloud-based

7. Regional Analysis

• North America
• United States
• Canada
• Mexico
• Europe
• United Kingdom
• Germany
• France
• Italy
• Asia-Pacific
• China
• Japan
• India
• Australia
• Latin America
• Brazil
• Argentina
• Chile
• Middle East and Africa
• South Africa
• Saudi Arabia
• UAE

8. Market Dynamics

• Market Drivers
• Market Restraints
• Market Opportunities
• Impact of COVID-19 on the Market

9. Competitive Landscape

• Key Players
• Market Share Analysis

10. Company Profiles

• IBM
• Microsoft
• Oracle
• SAP
• Teradata
• Aerospike
• Altibase
• Couchbase
• DataStax
• GridGain
• TIBCO Software
• Redis Labs
• VoltDB

11. Market Outlook and Opportunities

• Emerging Technologies
• Future Market Trends
• Investment Opportunities

12. Appendix

• List of Abbreviations
• Sources and References