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1889198

全球自治資料庫市場：預測至 2032 年－按組件、部署方式、組織規模、功能集、用例、最終用戶和地區進行分析

Autonomous Database Market Forecasts to 2032 - Global Analysis By Component (Solution and Services), Deployment Mode, Organization Size, Feature Set, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球自治資料庫市場價值將達到 21.6 億美元，到 2032 年將達到 74.1 億美元，在預測期內的複合年成長率為 19.2%。

自治資料庫是一種智慧資料平台，它利用自動化和人工智慧技術進行自我管理、保護和維護。它透過自動執行配置、最佳化、更新、備份和資源擴展等任務來簡化操作。透過減少人工干預，它提高了系統的可靠性、性能和災難復原能力。企業正在採用自治資料庫來減輕營運負擔、提高效率並及時獲取洞察，從而幫助他們應對現代資料密集型工作負載和應用程式。

對即時洞察的需求

自主資料庫能夠實現持續監控和自動最佳化，確保無需人工干預即可獲得洞察。隨著各行業採用人工智慧驅動的工作流程，對即時分析的需求變得愈發迫切。企業正在利用自主系統處理來自物聯網感測器、客戶互動和數位平台的串流數據。這種轉變有助於實現主動營運、預測智慧和提升客戶體驗。因此，對即時資料可見性的追求正在加速自主資料庫解決方案的普及。

數據品質問題

結構不良、不一致或不完整的資料會降低自動化流程的準確性。即使是先進的人工智慧驅動系統，如果底層資料不可信，也難以發揮最佳效能。企業在整合舊有系統時常常面臨挑戰，容易引入不一致和錯誤。這些問題促使人們需要額外的檢驗工具和資料管治框架。因此，對資料品質的擔憂持續阻礙自治資料庫的全面普及。

雲端原生採用

企業正將工作負載遷移到雲端，以獲得可擴展性、柔軟性和降低基礎設施成本等優勢。自治資料庫可與雲端環境無縫整合，並支援自我調優、自我修復和自動更新。隨著混合雲和多重雲端策略的興起，各組織正在考慮採用自治系統來提高營運效率。數位轉型計畫的興起正推動企業對其資料架構進行現代化改造。這種向雲原生生態系統的轉變顯著拓展了市場的成長前景。

資料隱私和安全漏洞

隨著資料庫自動化程度的提高，針對配置錯誤和漏洞的網路攻擊也可能隨之增加。儲存在雲端環境中的敏感資料尤其容易受到未授權存取。諸如GDPR和CCPA等法規結構進一步加劇了合規性的挑戰。資料外洩可能會削弱人們對自主系統的信任，並阻礙其在風險規避型產業中的應用。因此，持續存在的網路安全風險對市場擴張構成了重大障礙。

新冠疫情的感染疾病：

新冠疫情加速了數位化基礎設施和自動化數據系統的轉型。各組織機構紛紛採用自主資料庫來支援遠端營運並維持業務永續營運。這一轉變導致供應鏈、醫療保健和客戶參與流程對即時分析的依賴顯著增強。然而，疫情初期的一些干擾延緩了實施進度，並影響了IT支出。最終，疫情反而強化了市場成長動能。

在預測期內，解決方案細分市場將佔據最大的市場佔有率。

由於對自管理資料庫平台的需求不斷成長，預計在預測期內，解決方案領域將佔據最大的市場佔有率。這些解決方案提供自動化的效能調優、備份、修補程式和安全控制。企業更傾向於選擇能夠減少人工干預並提高可靠性的整合解決方案。人工智慧和機器學習的進步正使自主資料庫解決方案變得更加智慧。企業正在部署這些系統，以支援大規模分析、關鍵任務工作負載和雲端遷移策略。

在預測期內，醫療保健和生命科學產業的複合年成長率將最高。

在預測期內，醫療保健和生命科學領域預計將保持最高的成長率，這主要得益於對高效數據管理日益成長的需求。自主資料庫有助於即時臨床分析、病患監測和研究數據處理。遠端醫療和數位健康平台的興起進一步推動了對自動化數據解決方案的需求。人工智慧技術能夠實現更快速的診斷、預測分析和個人化治療。嚴格的監管要求正在推動安全合規的自主資料庫系統的應用。

佔比最大的地區：

由於北美擁有強大的技術基礎設施和較高的雲端採用率，預計該地區將在預測期內佔據最大的市場佔有率。該地區的主要企業是人工智慧驅動資料庫系統的早期採用者。主要技術提供者的存在正在加速創新和應用。金融、醫療保健和零售等高度依賴即時分析的行業正在推動市場成長。政府支持數位轉型的措施進一步增強了該地區的需求。

年複合成長率最高的地區：

由於新興經濟體數位化的快速推進，預計亞太地區在預測期內將呈現最高的複合年成長率。該地區的企業正在採用雲端基礎的系統來實現其IT營運的現代化。對人工智慧、自動化和高階分析領域不斷成長的投資正在推動自主資料庫的普及。電子商務、銀行、金融和保險（BFSI）以及通訊等產業對即時數據平台的使用日益增加。政府主導的智慧基礎設施計劃正在進一步加速市場應用。

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目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
  • 分析材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球自主資料庫市場（按組件分類）

  • 解決方案
  • 服務
    • 專業服務
    • 託管服務

6. 全球自治資料庫市場依部署方式分類

  • 公共雲端
  • 私有雲端
  • 混合雲端

7. 按組織規模分類的全球自治資料庫市場

  • 主要企業
  • 中小企業

8. 全球自治資料庫市場（依功能集分類）

  • 自動駕駛
    • 自動配置
    • 自動縮放
  • 自衛
    • 自動修補
    • 威脅偵測
  • 自癒
    • 自動恢復
    • 故障檢測
  • 自主性能最佳化
  • 自動化備份生命週期管理

9. 全球自治資料庫市場（按應用分類）

  • 資料倉儲
  • 分析與報告
  • 事務處理（OLTP）
  • 備份和災害復原
  • 財務規劃與會計
  • 資產和庫存管理
  • 客戶體驗與客戶關係管理
  • 詐欺偵測和風險管理

10. 全球自治資料庫市場（以最終用戶分類）

  • 雲端服務供應商
  • 公司
  • 數據分析公司
  • IT服務和託管服務供應商
  • 其他最終用戶

11. 全球自治資料庫市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第13章：企業概況

  • Oracle Corporation
  • Amazon Web Services, Inc.
  • Microsoft Corporation
  • Google LLC
  • IBM Corporation
  • Snowflake Inc.
  • Teradata Corporation
  • Databricks, Inc.
  • SAP SE
  • Alibaba Cloud
  • Huawei Technologies Co., Ltd.
  • MongoDB, Inc.
  • Cockroach Labs, Inc.
  • Couchbase, Inc.
  • DataStax, Inc.
Product Code: SMRC32658

According to Stratistics MRC, the Global Autonomous Database Market is accounted for $2.16 billion in 2025 and is expected to reach $7.41 billion by 2032 growing at a CAGR of 19.2% during the forecast period. An autonomous database refers to an intelligent data platform that independently manages, secures, and maintains itself using automation and AI technologies. It streamlines operations by automatically executing activities like setup, optimization, updating, backup, and resource scaling. By reducing the need for manual involvement, it improves system reliability, performance, and protection against failures. Businesses adopt autonomous databases to cut operational effort, boost efficiency, and gain timely insights, making it valuable for handling modern, data-intensive workloads and applications.

Market Dynamics:

Driver:

Need for real-time insights

Autonomous databases enable continuous monitoring and automated optimization, ensuring insights are delivered without manual intervention. As industries adopt AI-driven workflows, the need for instant analytics becomes even more crucial. Businesses are leveraging autonomous systems to handle streaming data from IoT sensors, customer interactions, and digital platforms. This shift supports proactive operations, predictive intelligence, and improved customer experiences. Consequently, the push for real-time data visibility is accelerating the adoption of autonomous database solutions.

Restraint:

Data quality issues

Poorly structured, inconsistent, or incomplete data reduces the accuracy of automated processes. Even advanced AI-driven systems struggle to perform optimally when underlying data is unreliable. Organizations often face challenges in integrating legacy systems, leading to discrepancies and errors. These issues increase the need for additional validation tools and data governance frameworks. As a result, data quality concerns continue to slow down the full-scale deployment of autonomous databases.

Opportunity:

Cloud-native adoption

Businesses are migrating workloads to the cloud to benefit from scalability, flexibility, and reduced infrastructure overhead. Autonomous databases integrate seamlessly with cloud environments, enabling self-tuning, self-healing, and automated updates. As hybrid and multi-cloud strategies gain momentum, organizations are exploring autonomous systems for improved operational efficiency. The rise of digital transformation initiatives is pushing enterprises to modernize data architectures. This shift toward cloud-native ecosystems greatly expands market growth prospects.

Threat:

Data privacy and security breaches

As databases become more automated, cyberattacks targeting misconfigurations or vulnerabilities can increase. Sensitive data stored in cloud environments is particularly exposed to unauthorized access. Regulatory frameworks like GDPR and CCPA further heighten compliance challenges. Breaches can undermine trust in autonomous systems, discouraging adoption among risk-averse industries. Thus, ongoing cybersecurity risks create significant hurdles for market expansion.

Covid-19 Impact:

The Covid-19 pandemic accelerated the shift toward digital infrastructure and automated data systems. Organizations adopted autonomous databases to support remote operations and maintain business continuity. This transition increased reliance on real-time analytics for supply chain, healthcare, and customer engagement processes. However, initial disruptions slowed implementation timelines and impacted IT spending. As a result, the pandemic ultimately strengthened the market's growth trajectory.

The solution segment is expected to be the largest during the forecast period

The solution segment is expected to account for the largest market share during the forecast period, due to increasing demand for self-managing database platforms. These solutions offer automated performance tuning, backup, patching, and security controls. Organizations prefer integrated offerings that reduce manual workload and improve reliability. Advancements in AI and machine learning are enhancing the intelligence of autonomous database solutions. Enterprises are adopting these systems to support large-scale analytics, mission-critical workloads, and cloud migration strategies.

The healthcare and life sciences segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare and life sciences segment is predicted to witness the highest growth rate, due to growing needs for efficient data management. Autonomous databases support real-time clinical analysis, patient monitoring, and research data processing. The rise of telemedicine and digital health platforms further increases the demand for automated data solutions. AI-powered capabilities enable faster diagnosis, predictive analytics, and treatment personalization. Strict regulatory requirements drive adoption of secure, compliant, and self-governing database systems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to strong technological infrastructure and high cloud adoption. Major enterprises in the region are early adopters of AI-driven database systems. The presence of key technology providers accelerates innovation and deployment. Industries such as finance, healthcare, and retail rely heavily on real-time analytics, boosting market growth. Government initiatives supporting digital transformation further strengthen regional demand.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to rapid digitization across emerging economies. Organizations in the region are adopting cloud-based systems to modernize IT operations. Growing investments in AI, automation, and advanced analytics are propelling autonomous database adoption. Industries such as e-commerce, BFSI, and telecom are expanding their use of real-time data platforms. Government-led smart infrastructure projects further accelerate market uptake.

Key players in the market

Some of the key players in Autonomous Database Market include Oracle Corp, Amazon W, Microsoft, Google LLC, IBM Corp, Snowflake, Teradata C, Databricks, SAP SE, Alibaba Cl, Huawei Te, MongoDB, Cockroach, Couchbase, and DataStax.

Key Developments:

In November 2025, IBM and the University of Dayton announced an agreement for the joint research and development of next-generation semiconductor technologies and materials. The collaboration aims to advance critical technologies for the age of AI including AI hardware, advanced packaging, and photonics.

In October 2025, Oracle announced collaboration with Microsoft to develop an integration blueprint to help manufacturers improve supply chain efficiency and responsiveness. The blueprint will enable organizations using Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) to improve data-driven decision making and automate key supply chain processes by capturing live insights from factory equipment and sensors through Azure IoT Operations and Microsoft Fabric.

Components Covered:

  • Solution
  • Services

Deployment Modes Covered:

  • Public Cloud
  • Private Cloud
  • Hybrid Cloud

Organization Sizes Covered:

  • Large Enterprises
  • Small & Medium Enterprises (SMEs)

Feature Sets Covered:

  • Self-Driving
  • Self-Securing
  • Self-Repairing
  • Autonomous Performance Optimization
  • Automated Backup & Lifecycle Management

Applications Covered:

  • Data Warehousing
  • Analytics & Reporting
  • Transaction Processing (OLTP)
  • Backup & Disaster Recovery
  • Financial Planning & Accounting
  • Asset & Inventory Management
  • Customer Experience & CRM
  • Fraud Detection & Risk Management

End Users Covered:

  • Cloud Service Providers
  • Enterprises
  • Data Analytics Companies
  • IT Service & Managed Service Providers
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Autonomous Database Market, By Component

  • 5.1 Introduction
  • 5.2 Solution
  • 5.3 Services
    • 5.3.1 Professional Services
    • 5.3.2 Managed Services

6 Global Autonomous Database Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Public Cloud
  • 6.3 Private Cloud
  • 6.4 Hybrid Cloud

7 Global Autonomous Database Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Large Enterprises
  • 7.3 Small & Medium Enterprises (SMEs)

8 Global Autonomous Database Market, By Feature Set

  • 8.1 Introduction
  • 8.2 Self-Driving
    • 8.2.1 Automated provisioning
    • 8.2.2 Auto-scaling
  • 8.3 Self-Securing
    • 8.3.1 Automated patching
    • 8.3.2 Threat detection
  • 8.4 Self-Repairing
    • 8.4.1 Automated recovery
    • 8.4.2 Fault detection
  • 8.5 Autonomous Performance Optimization
  • 8.6 Automated Backup & Lifecycle Management

9 Global Autonomous Database Market, By Application

  • 9.1 Introduction
  • 9.2 Data Warehousing
  • 9.3 Analytics & Reporting
  • 9.4 Transaction Processing (OLTP)
  • 9.5 Backup & Disaster Recovery
  • 9.6 Financial Planning & Accounting
  • 9.7 Asset & Inventory Management
  • 9.8 Customer Experience & CRM
  • 9.9 Fraud Detection & Risk Management

10 Global Autonomous Database Market, By End User

  • 10.1 Introduction
  • 10.2 Cloud Service Providers
  • 10.3 Enterprises
  • 10.4 Data Analytics Companies
  • 10.5 IT Service & Managed Service Providers
  • 10.6 Other End Users

11 Global Autonomous Database Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Oracle Corporation
  • 13.2 Amazon Web Services, Inc.
  • 13.3 Microsoft Corporation
  • 13.4 Google LLC
  • 13.5 IBM Corporation
  • 13.6 Snowflake Inc.
  • 13.7 Teradata Corporation
  • 13.8 Databricks, Inc.
  • 13.9 SAP SE
  • 13.10 Alibaba Cloud
  • 13.11 Huawei Technologies Co., Ltd.
  • 13.12 MongoDB, Inc.
  • 13.13 Cockroach Labs, Inc.
  • 13.14 Couchbase, Inc.
  • 13.15 DataStax, Inc.

List of Tables

  • Table 1 Global Autonomous Database Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Autonomous Database Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Autonomous Database Market Outlook, By Solution (2024-2032) ($MN)
  • Table 4 Global Autonomous Database Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global Autonomous Database Market Outlook, By Professional Services (2024-2032) ($MN)
  • Table 6 Global Autonomous Database Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 7 Global Autonomous Database Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 8 Global Autonomous Database Market Outlook, By Public Cloud (2024-2032) ($MN)
  • Table 9 Global Autonomous Database Market Outlook, By Private Cloud (2024-2032) ($MN)
  • Table 10 Global Autonomous Database Market Outlook, By Hybrid Cloud (2024-2032) ($MN)
  • Table 11 Global Autonomous Database Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 12 Global Autonomous Database Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 13 Global Autonomous Database Market Outlook, By Small & Medium Enterprises (SMEs) (2024-2032) ($MN)
  • Table 14 Global Autonomous Database Market Outlook, By Feature Set (2024-2032) ($MN)
  • Table 15 Global Autonomous Database Market Outlook, By Self-Driving (2024-2032) ($MN)
  • Table 16 Global Autonomous Database Market Outlook, By Automated provisioning (2024-2032) ($MN)
  • Table 17 Global Autonomous Database Market Outlook, By Auto-scaling (2024-2032) ($MN)
  • Table 18 Global Autonomous Database Market Outlook, By Self-Securing (2024-2032) ($MN)
  • Table 19 Global Autonomous Database Market Outlook, By Automated patching (2024-2032) ($MN)
  • Table 20 Global Autonomous Database Market Outlook, By Threat detection (2024-2032) ($MN)
  • Table 21 Global Autonomous Database Market Outlook, By Self-Repairing (2024-2032) ($MN)
  • Table 22 Global Autonomous Database Market Outlook, By Automated recovery (2024-2032) ($MN)
  • Table 23 Global Autonomous Database Market Outlook, By Fault detection (2024-2032) ($MN)
  • Table 24 Global Autonomous Database Market Outlook, By Autonomous Performance Optimization (2024-2032) ($MN)
  • Table 25 Global Autonomous Database Market Outlook, By Automated Backup & Lifecycle Management (2024-2032) ($MN)
  • Table 26 Global Autonomous Database Market Outlook, By Application (2024-2032) ($MN)
  • Table 27 Global Autonomous Database Market Outlook, By Data Warehousing (2024-2032) ($MN)
  • Table 28 Global Autonomous Database Market Outlook, By Analytics & Reporting (2024-2032) ($MN)
  • Table 29 Global Autonomous Database Market Outlook, By Transaction Processing (OLTP) (2024-2032) ($MN)
  • Table 30 Global Autonomous Database Market Outlook, By Backup & Disaster Recovery (2024-2032) ($MN)
  • Table 31 Global Autonomous Database Market Outlook, By Financial Planning & Accounting (2024-2032) ($MN)
  • Table 32 Global Autonomous Database Market Outlook, By Asset & Inventory Management (2024-2032) ($MN)
  • Table 33 Global Autonomous Database Market Outlook, By Customer Experience & CRM (2024-2032) ($MN)
  • Table 34 Global Autonomous Database Market Outlook, By Fraud Detection & Risk Management (2024-2032) ($MN)
  • Table 35 Global Autonomous Database Market Outlook, By End User (2024-2032) ($MN)
  • Table 36 Global Autonomous Database Market Outlook, By Cloud Service Providers (2024-2032) ($MN)
  • Table 37 Global Autonomous Database Market Outlook, By Enterprises (2024-2032) ($MN)
  • Table 38 Global Autonomous Database Market Outlook, By Data Analytics Companies (2024-2032) ($MN)
  • Table 39 Global Autonomous Database Market Outlook, By IT Service & Managed Service Providers (2024-2032) ($MN)
  • Table 40 Global Autonomous Database Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.