关联资料库市场－全球产业规模、份额、趋势、机会、预测：按类型、部署方式、最终用户、地区和竞争格局划分，2021-2031年

全球关联资料库市场预计将从 2025 年的 692.2 亿美元大幅成长至 2031 年的 1,423.6 亿美元，复合年增长率为 12.77%。

关联资料库作为数位储存库，将资讯结构化为由行和列组成的预先定义表格，并在资料点之间建立逻辑关係。这一市场趋势主要受企业资料快速成长以及财务和业务系统对可靠事务一致性的迫切需求所驱动。此外，核心业务应用中对结构化资料管理的持续需求，透过确保资料完整性和准确性，也持续推动市场成长。 IEEE 的报告显示，SQL 在 2024 年的就业市场排名中保持领先地位，这反映了业界对这些基础技术的持续依赖。

儘管市场扩张势头强劲，但仍面临可能阻碍成长的重大挑战。关係型系统在横向扩展性方面通常存在固有的局限性，尤其是在处理大量非结构化资讯时。与更灵活的替代架构相比，这些技术限制使得关係型系统难以在不付出庞大财务和效能成本的情况下，跟上现代巨量资料工作负载的多样性和速度。

市场驱动因素

随着企业为了提高敏捷性和成本效益而逐步放弃本地基础设施，基于云端的资料库服务和资料库即服务 (DBaaS) 模型的广泛应用正在从根本上重塑市场格局。企业越来越多地利用完全託管的平台来减轻修补程式、扩展和备份等管理负担，从而使技术团队能够专注于创新而非维护。产业数据量化了这一迁移趋势，显示企业正在迅速转型为更灵活的环境。根据 Redgate 于 2024 年 2 月发布的《2024 年资料库格局报告》，到 2023 年，主要或完全在云端託管资料库的企业比例已上升至 36%，这反映出企业正在明显地摆脱对传统资料中心的依赖。

同时，对即时数据分析和商业智慧日益增长的需求正在推动市场发展，这要求资料库能够高速处理事务并支援复杂的分析查询。现代应用需要从大量资料集中即时获取洞察，而关係型系统则需要与人工智慧和机器学习工作流程深度整合。正如Google云端在2024年4月发布的《2024年数据与人工智慧趋势报告》中所述，84%的数据领导者认为生成式人工智慧将帮助企业更快获得洞察，这凸显了能够支援快速决策的数据平台的重要性。这种发展趋势也影响技术选择。根据Stack Overflow在2024年进行的一项调查，PostgreSQL成为49%开发者的首选，这表明市场普遍倾向于能够处理这些高级分析需求的强大且开放标准的系统。

市场挑战

关联资料库在横向扩展方面的僵化架构是其市场扩张的一大障碍。随着企业接收大量非结构化资讯（例如感测器日誌和社群媒体动态），这些系统基于固定表的结构难以有效率地将工作负载分配到多个伺服器上。这种限制迫使企业依赖昂贵的纵向扩展技术和复杂的变更来维持效能，而这往往会导致延迟增加和营运成本上升。因此，无法原生处理现代巨量资料流的速度和多样性已成为一项技术瓶颈，限制了关係型系统在高成长、资料密集应用中的普及。

这种限制直接影响市场动能，促使企业投资更灵活的非关係型架构。由于将动态资料强制转换为结构化模式会带来巨大的财务和技术负担，企业正越来越多地选择可扩展性更佳的替代方案。开发人员对能够规避这些特定限制的工具的偏好也印证了这一趋势。 Stack Overflow 2024 年的一项调查发现，约 25% 的专业开发人员正在使用文件导向的资料库MongoDB，这表明相当一部分工业工作负载正在从关係模型迁移到非结构化模型，以管理非结构化资料需求。这种转变凸显了扩充性挑战如何有效地限制了关联资料库在不断扩展的巨量资料管理领域中的潜在市场份额。

市场趋势

将向量搜寻功能整合到生成式人工智慧中，扩展了关联式资料库引擎的效用，使其能够对高维嵌入式资料进行原生查询。这种融合使企业能够支援搜寻增强型生成式工作流程，而无需维护单独的专用向量存储，从而避免架构上的复杂性。透过将这些功能直接整合到资料库核心，企业可以在确保事务一致性的同时，推动现代机器学习应用的发展。这一整合趋势得到了近期行业数据的支持。根据 Retool 于 2024 年 6 月发布的《2024 年人工智慧现状报告》，向量资料库的采用率预计将在 2024 年飙升至 63.6%，其中关係型资料库扩展 pgvector 获得了 21.3% 的受访者支持，使其几乎与专业的细分市场竞争对手不相上下。

分散式 SQL 和 NewSQL 架构的兴起满足了市场对兼具横向扩充性和强大事务保证的系统这一关键需求。与传统单体资料库在扩展过程中经常出现停机不同，这些现代架构能够自动将资料分布到多个节点和区域，从而确保持续可用性。这种容错能力已成为全球企业因应服务中断所带来的财务风险的关键选择标准。 Cockroach Labs 于 2024 年 10 月发布的《2025 年韧性状况》报告指出，营运现实凸显了这项转型的迫切性。该报告发现，100% 的技术主管在过去一年中都经历过因服务中断造成的收入损失，凸显了分散式 SQL 所提供的容错设计的紧迫性。

目录

第一章概述

第二章：调查方法

第三章执行摘要

第四章：客户心声

第五章：全球关联资料库市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 按类型（记忆体内、磁碟型、其他）
  • 部署方式（云端部署、本机部署）
  • 按最终用户（银行/金融/保险、IT/电信、零售/电子商务、製造业、医疗保健、其他）
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美关联资料库市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国别分析
  • 我们
  • 加拿大
  • 墨西哥

第七章：欧洲关联资料库市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国别分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

第八章：亚太地区关联资料库市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国别分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

第九章：中东和非洲关联资料库市场展望

• 市场规模及预测
• 市占率及预测
• 中东与非洲：国别分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美洲关联资料库市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国别分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 促进因素
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 近期趋势

第十三章：全球关联资料库市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的潜力
• 供应商的议价能力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• Oracle Corporation
• Microsoft Corporation
• IBM Corporation
• Google LLC
• SAP SE
• MongoDB, Inc.
• Huawei Technologies Co., Ltd.
• Amazon.com, Inc.
• Rackspace Technology, Inc.
• Snowflake Inc.

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Relational Database Market is projected to expand significantly, rising from USD 69.22 Billion in 2025 to USD 142.36 Billion by 2031, reflecting a CAGR of 12.77%. Relational databases function as digital repositories that structure information into predefined tables featuring rows and columns, establishing logical connections between data points. This market trajectory is primarily driven by the exponential growth of enterprise data and the indispensable need for reliable transactional consistency within financial and operational systems. Furthermore, sustained demand for structured data management in core business applications continues to support growth by ensuring data integrity and accuracy. Highlighting the enduring industrial reliance on these foundational technologies, the IEEE reported in 2024 that SQL maintained the top position in job market rankings.

Despite this robust expansion, the market faces a significant challenge that could hinder growth. Relational systems often encounter inherent limitations regarding horizontal scalability, particularly when processing massive volumes of unstructured information. These technical constraints make it difficult to accommodate the variety and velocity of modern big data workloads without incurring substantial financial and performance costs when compared to more flexible alternative architectures.

Market Driver

The surging adoption of cloud-based database services and Database-as-a-Service (DBaaS) models is fundamentally reshaping the market as enterprises migrate from on-premises infrastructure to achieve greater agility and cost-efficiency. Organizations are increasingly utilizing fully managed platforms to offload administrative burdens such as patching, scaling, and backups, which allows technical teams to focus on innovation rather than maintenance. This migration trend is quantified by industry data highlighting a rapid operational shift toward flexible environments. According to Redgate's "State of the Database Landscape 2024" report released in February 2024, the percentage of organizations hosting their databases mostly or fully in the cloud rose to 36% in 2023, reflecting a definitive move away from traditional data centers.

Simultaneously, the market is being propelled by heightened demand for real-time data analytics and business intelligence, necessitating databases capable of supporting high-velocity transaction processing and complex analytical queries. Modern applications now require immediate insights derived from massive datasets, pushing relational systems to integrate deeper support for AI and machine learning workflows. As noted by Google Cloud in their "2024 Data and AI Trends Report" from April 2024, 84% of data leaders believe generative AI will help their organization reduce time-to-insight, underscoring the critical role of data platforms in enabling rapid decision-making. This evolution is also influencing technology choices; according to Stack Overflow in 2024, PostgreSQL emerged as the preferred choice for 49% of developers, indicating a broader market preference for robust, open-standard systems capable of handling these advanced analytical requirements.

Market Challenge

The rigid architecture of relational databases regarding horizontal scalability presents a substantial hurdle to market expansion. As enterprises ingest massive volumes of unstructured information, such as sensor logs and social media feeds, the fixed table-based structure of these systems struggles to distribute workloads efficiently across multiple servers. This limitation forces organizations to rely on expensive vertical scaling methods or complex modifications to maintain performance, which frequently leads to increased latency and operational costs. Consequently, the inability to natively accommodate the velocity and variety of modern big data streams creates a technical ceiling that restricts the adoption of relational systems for high-growth, data-intensive applications.

This constraint directly impacts market momentum by diverting investment toward more flexible non-relational architectures. When businesses face the financial and technical burden of forcing dynamic data into structured schemas, they increasingly opt for alternative solutions that offer superior elasticity. This trend is evident in developer preferences for tools that bypass these specific limitations. According to Stack Overflow in 2024, approximately 25 percent of professional developers reported utilizing MongoDB, a document-oriented database, indicating a measurable portion of the industrial workload is shifting away from relational models to manage unstructured data requirements. This migration demonstrates how scalability challenges effectively cap the potential market share of relational databases in the expanding sector of big data management.

Market Trends

The integration of vector search capabilities for generative AI is expanding the utility of relational engines by allowing them to natively query high-dimensional embeddings. This convergence enables enterprises to support retrieval-augmented generation workflows without the architectural complexity of maintaining separate, specialized vector stores. By embedding these features directly into the core database, organizations can ensure transactional consistency while powering modern machine learning applications. This consolidation trend is substantiated by recent industrial data; according to Retool's "State of AI 2024" report from June 2024, vector database utilization surged to 63.6% in 2024, with the relational extension pgvector securing 21.3% of respondent preference, effectively rivaling purpose-built niche competitors.

The rise of distributed SQL and NewSQL architectures is addressing the critical market need for systems that combine horizontal elasticity with strict transactional guarantees. Unlike legacy monolithic databases that often suffer from downtime during scaling events, these modern architectures automatically distribute data across multiple nodes and geographies to ensure continuous availability. This resilience has become a primary selection criterion for global enterprises facing the financial risks of service interruptions. The urgency of this shift is highlighted by operational realities noted by Cockroach Labs in the "State of Resilience 2025" report from October 2024, where 100% of technology executives reported experiencing revenue losses due to outages in the past year, underscoring the imperative for the fault-tolerant design that distributed SQL provides.

Key Market Players

• Oracle Corporation
• Microsoft Corporation
• IBM Corporation
• Google LLC
• SAP SE
• MongoDB, Inc.
• Huawei Technologies Co., Ltd.
• Amazon.com, Inc.
• Rackspace Technology, Inc.
• Snowflake Inc.

Report Scope

In this report, the Global Relational Database Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Relational Database Market, By Type

• In-memory
• Disk-based
• Others

Relational Database Market, By Deployment

• Cloud-based
• On-premises

Relational Database Market, By End User

• BFSI
• IT & Telecom
• Retail & E-commerce
• Manufacturing
• Healthcare
• Others

Relational Database Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Relational Database Market.

Available Customizations:

Global Relational Database Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Relational Database Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Type (In-memory, Disk-based, Others)
  • 5.2.2. By Deployment (Cloud-based, On-premises)
  • 5.2.3. By End User (BFSI, IT & Telecom, Retail & E-commerce, Manufacturing, Healthcare, Others)
  • 5.2.4. By Region
  • 5.2.5. By Company (2025)
• 5.3. Market Map

6. North America Relational Database Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Type
  • 6.2.2. By Deployment
  • 6.2.3. By End User
  • 6.2.4. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Relational Database Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Type
      • 6.3.1.2.2. By Deployment
      • 6.3.1.2.3. By End User
  • 6.3.2. Canada Relational Database Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Type
      • 6.3.2.2.2. By Deployment
      • 6.3.2.2.3. By End User
  • 6.3.3. Mexico Relational Database Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Type
      • 6.3.3.2.2. By Deployment
      • 6.3.3.2.3. By End User

7. Europe Relational Database Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Type
  • 7.2.2. By Deployment
  • 7.2.3. By End User
  • 7.2.4. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Relational Database Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Type
      • 7.3.1.2.2. By Deployment
      • 7.3.1.2.3. By End User
  • 7.3.2. France Relational Database Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Type
      • 7.3.2.2.2. By Deployment
      • 7.3.2.2.3. By End User
  • 7.3.3. United Kingdom Relational Database Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Type
      • 7.3.3.2.2. By Deployment
      • 7.3.3.2.3. By End User
  • 7.3.4. Italy Relational Database Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Type
      • 7.3.4.2.2. By Deployment
      • 7.3.4.2.3. By End User
  • 7.3.5. Spain Relational Database Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Type
      • 7.3.5.2.2. By Deployment
      • 7.3.5.2.3. By End User

8. Asia Pacific Relational Database Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Type
  • 8.2.2. By Deployment
  • 8.2.3. By End User
  • 8.2.4. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Relational Database Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Type
      • 8.3.1.2.2. By Deployment
      • 8.3.1.2.3. By End User
  • 8.3.2. India Relational Database Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Type
      • 8.3.2.2.2. By Deployment
      • 8.3.2.2.3. By End User
  • 8.3.3. Japan Relational Database Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Type
      • 8.3.3.2.2. By Deployment
      • 8.3.3.2.3. By End User
  • 8.3.4. South Korea Relational Database Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Type
      • 8.3.4.2.2. By Deployment
      • 8.3.4.2.3. By End User
  • 8.3.5. Australia Relational Database Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Type
      • 8.3.5.2.2. By Deployment
      • 8.3.5.2.3. By End User

9. Middle East & Africa Relational Database Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Type
  • 9.2.2. By Deployment
  • 9.2.3. By End User
  • 9.2.4. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Relational Database Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Type
      • 9.3.1.2.2. By Deployment
      • 9.3.1.2.3. By End User
  • 9.3.2. UAE Relational Database Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Type
      • 9.3.2.2.2. By Deployment
      • 9.3.2.2.3. By End User
  • 9.3.3. South Africa Relational Database Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Type
      • 9.3.3.2.2. By Deployment
      • 9.3.3.2.3. By End User

10. South America Relational Database Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Type
  • 10.2.2. By Deployment
  • 10.2.3. By End User
  • 10.2.4. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Relational Database Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Type
      • 10.3.1.2.2. By Deployment
      • 10.3.1.2.3. By End User
  • 10.3.2. Colombia Relational Database Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Type
      • 10.3.2.2.2. By Deployment
      • 10.3.2.2.3. By End User
  • 10.3.3. Argentina Relational Database Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Type
      • 10.3.3.2.2. By Deployment
      • 10.3.3.2.3. By End User

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Relational Database Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. Oracle Corporation
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Microsoft Corporation
• 15.3. IBM Corporation
• 15.4. Google LLC
• 15.5. SAP SE
• 15.6. MongoDB, Inc.
• 15.7. Huawei Technologies Co., Ltd.
• 15.8. Amazon.com, Inc.
• 15.9. Rackspace Technology, Inc.
• 15.10. Snowflake Inc.

16. Strategic Recommendations

17. About Us & Disclaimer