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市场调查报告书
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1851622

高效能资料分析：市场占有率分析、产业趋势、统计资料和成长预测（2025-2030 年）

High-Performance Data Analytics - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3个工作天内

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简介目录

预计到 2025 年，高效能资料分析市场规模将达到 1,259.9 亿美元，到 2030 年将达到 3,359.3 亿美元，复合年增长率为 21.67%。

高性能资料分析市场-IMG1

人工智慧、云端运算和海量企业数据的融合推动了这一趋势。金融服务业仍然是领先的采用者，因为即时诈欺分析对于安全的交易银行至关重要。虽然软体收入占46.2%，但藉助专业的AI咨询服务，服务业务正在快速成长。目前，本地部署占据主导地位，市场份额高达57.8%，但云端基础方案显然是成长引擎，年复合成长率高达30.1%，这得益于供应商不断扩大全球GPU容量。从区域来看，北美占35.4%的市场份额，但亚太地区预计将实现最快成长，这得益于其快速推进的数位转型计画。大型企业仍占据主导地位，但中小企业正在缩小差距，这主要得益于GPU租赁价格的暴跌，例如H100实例每小时仅需3.35美元，比超大规模资料中心的标价低90%以上。

全球高性能资料分析市场趋势与洞察

金融服务业加速采用即时分析技术进行诈欺侦测

金融机构的社交工程诈骗增加了十倍，目前已占数位银行诈骗案件的23%。道明银行凭藉其企业级即时监控能力荣获2024年FICO决策奖。其人工智慧平台在处理1Gbps资料流的同时，实现了98.5%的侦测准确率，且无延迟。因此，银行、金融服务和保险（BFSI）机构正在将低延迟分析技术融入支付系统、信用风险评分和客户调查中，以保护其声誉和财务资本。这些应用推动了高效能资料分析市场整体5.2%的复合年增长率。

人工智慧/机器学习模型训练的快速成长需要Petabyte级资料处理。

生成式人工智慧模型中的参数数量每六个月翻一番，这需要Petabyte的资料撷取和百万兆级计算群集。与人工智慧工作负载相关的超大规模资料中心投资将从2024年的1,627.9亿美元增加到2030年的6,085.4亿美元。微软和Google等供应商正合计投入1,550亿美元用于下一代人工智慧设施建设。这些资本支出将推动对分散式檔案系统、高吞吐量互连和高阶调度软体的需求，从而推动市场成长6.8%。

专用高效能运算丛集的总拥有成本高

预计到2025年，数据中心建设的资本支出将超过2,500亿美元，而不断增长的电力需求预计到2030年还将增加5,000亿美元。在许多开发中国家，电力短缺阻碍了本地高效能运算（HPC）设施的推出。总合设备、冷却和熟练员工的成本，企业难以证明部署本地丛集的合理性，这限制了在资源匮乏地区的部署，最终导致整体复合年增长率仅2.1%。

细分市场分析

到2024年，软体业务将贡献46.2%的收入，反映出市场对使用者友善分析引擎、资料架构层和AI编配工具的需求。供应商正在整合工作流程自动化和特征储存功能，以加速跨业务部门的模型部署。 DevOps的整合正在加强回馈循环，授权模式也正转向基于使用量的收费，以使成本与价值创造保持一致。硬体收入依然至关重要，这主要得益于晶片技术的进步，例如NVIDIA Blackwell Ultra GPU，它为变压器工作负载提供了高密度的张量核心。

服务是成长最快的细分市场，预计到 2030 年将以 25.4% 的复合年增长率成长。咨询团队现在将资料策略设计、MLOps 实施和持续模型调校服务打包在一起，以填补复杂混合架构中的专业知识缺口。服务提供者开始提供人工智慧即服务 (AIaaS) 产品，包括託管特征工程、偏差审核和联邦学习编配。这种转变扩大了潜在需求，并推动了高效能资料分析即服务合约的市场规模成长，尤其是在首次采用者群体中。

到2024年，本地部署将占市场份额的57.8%，这主要得益于对延迟和数据主权要求较高的行业，例如政府和银行业。企业表示，直接控制硬体以及遵守严格的资料居住法规是其主要动机。许多企业也利用现有资料中心的沉没成本，透过更新节点来优化运转率，而不是完全迁移到云端。

受弹性扩展、按需付费和全球边缘区域部署的推动，云端平台正以 30.1% 的复合年增长率成长。为了缓解监管方面的担忧，服务提供者扩展了机密运算实例和主权云端区域。混合云和多重云端模式如今已成为绿地计画的主流，将本地加速器与用于人工智慧训练的突发容量相结合。这种转变扩大了高效能资料分析的市场规模（该市场采用按需付费模式），同时也降低了资源受限企业的进入门槛。

区域分析

2024年，北美地区营收维持35.4%的成长，这得益于其庞大的超大规模资料中心网路和企业级人工智慧的早期应用。美国资料中心供应量年增26%至5.2吉瓦，与人工智慧推理需求的激增相符。像道明银行这样的银行正在利用全国范围的支付遥测数据进行即时诈骗评分，这凸显了该行业的成熟度。 2024年，北维吉尼亚的租金将上涨41.6%，显示产能紧张正在推动资料中心建设的持续进行。

亚太地区是成长最快的地区，预计复合年增长率将达到28%。印度计划在2026年将其装置容量翻一番，达到约1.8吉瓦，这得益于国内外投资者数十亿美元的投资承诺。台湾预计到2028年将投资超过30亿美元用于晶片设计模拟和大规模语言模型训练。中国正在缩小与美国在模型品质方面的差距，地方补贴正在推动下一代人工智慧框架的发展。然而，严格的资料本地化规定迫使企业建构特定国家的分析架构，而不是统一的全球架构。

欧洲正在扩大边缘到云端的转型计划，以实现製造业和关键基础设施的现代化。欧盟的目标是到2030年实现75%的企业云采用率，并部署1万个气候友善边缘节点。各国政府正在资助6G测试平台、通讯边缘云端试点计画以及需要低延迟分析的工业元宇宙示范计画。欧洲首家人工智慧工厂将于2024年投入运营，为希望在不汇出资料的情况下训练模型的汽车、航太和能源公司提供自主运算服务。

其他福利：

  • Excel格式的市场预测（ME）表
  • 3个月的分析师支持

目录

第一章 引言

  • 研究假设和市场定义
  • 调查范围

第二章调查方法

第三章执行摘要

第四章 市场情势

  • 市场概览
  • 市场驱动因素
    • 加速北美银行、金融服务和保险 (BFSI) 行业采用即时分析技术进行诈欺检测
    • 亚洲人工智慧/机器学习模型训练需求激增，需要Petabyte级资料处理。
    • 欧洲智慧製造领域边缘到云端高效能运算的成长
    • 中东各国政府的国防巨量资料现代化项目
    • 可再生能源和电网优化措施推动南美洲高效能运算分析的发展
    • GPU/CPU丛集的单核心价格正在下降，使得全球中小企业都能负担得起高效能运算（HPC）服务。
  • 市场限制
    • 加勒比海和非洲专用高效能运算丛集的总拥有成本高昂
    • 欧洲和大洋洲高效能运算和平行程式设计专家短缺
    • 资料主权法规限制了亚洲的跨境云分析。
    • 新兴市场的基础设施可靠性问题阻碍了数据的连续传输。
  • 监理展望
  • 技术展望
    • 高效能丛集运算的演进
    • 网格计算
    • 记忆体内分析
    • 资料库库内分析
  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争对手之间的竞争
  • 投资分析

第五章 市场规模与成长预测

  • 按组件
    • 硬体
    • 软体
    • 服务
  • 按部署模式
    • 本地部署
    • 按需/云端
  • 按组织规模
    • 小型企业
    • 大公司
  • 按最终用户行业划分
    • 银行、金融服务和保险（BFSI）
    • 政府和国防部
    • 能源与公共产业
    • 零售与电子商务
    • 医疗保健和生命科学
    • 资讯科技和通讯服务
    • 製造业
  • 按地区
    • 北美洲
      • 美国
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 智利
      • 秘鲁
      • 南美洲其他地区
    • 欧洲
      • 德国
      • 英国
      • 法国
      • 义大利
      • 西班牙
      • 其他欧洲地区
    • 亚太地区
      • 中国
      • 日本
      • 韩国
      • 印度
      • 澳洲
      • 纽西兰
      • 亚太其他地区
    • 中东
      • 阿拉伯聯合大公国
      • 沙乌地阿拉伯
      • 土耳其
      • 其他中东地区
    • 非洲
      • 南非
      • 其他非洲地区

第六章 竞争情势

  • 策略发展
  • 供应商定位分析
  • 公司简介
    • Amazon Web Services, Inc.（AWS）
    • Google LLC
    • Microsoft Corporation
    • IBM Corporation
    • Hewlett Packard Enterprise（HPE）
    • Dell Technologies Inc.
    • SAS Institute Inc.
    • Oracle Corporation
    • Fujitsu Limited
    • Intel Corporation
    • ATOS SE
    • Juniper Networks Inc.
    • NEC Corporation
    • Cisco Systems, Inc.
    • Teradata Corporation
    • Cray Inc.（HPE Cray）
    • Altair Engineering Inc.
    • Cloudera, Inc.
    • Huawei Technologies Co., Ltd.
    • Hitachi Vantara LLC
    • Super Micro Computer, Inc.

第七章 市场机会与未来展望

简介目录
Product Code: 61778

The high-performance data analytics market is valued at USD 125.99 billion in 2025 and is forecast to reach USD 335.93 billion by 2030, registering a 21.67% CAGR.

High-Performance Data Analytics - Market - IMG1

Momentum comes from the convergence of AI, cloud computing, and the swelling volume of enterprise data. Financial services remain a prime adopter as real-time fraud analytics become essential for secure transaction banking. Software accounts for 46.2% revenue, while services are expanding fastest on the back of specialized AI consulting. On-premise deployments presently lead with 57.8% share, yet cloud-based solutions are the clear growth engine at a 30.1% CAGR as providers scale global GPU capacity. Regionally, North America commands 35.4% share, but Asia-Pacific is on track for the quickest gains given sweeping digital-transformation programs. Large enterprises dominate adoption, though SMEs are narrowing the gap thanks to plummeting GPU rental rates, exemplified by USD 3.35-per-hour H100 instances that undercut hyperscaler list prices by more than 90%.

Global High-Performance Data Analytics Market Trends and Insights

Accelerating Adoption of Real-Time Analytics in BFSI for Fraud Detection

Financial institutions have recorded a tenfold rise in social-engineering scams, now 23% of digital-banking fraud cases, prompting rapid rollouts of high-performance fraud-detection engines. TD Bank achieved enterprise-wide real-time monitoring after winning the 2024 FICO Decisions Award. AI-enabled platforms are attaining 98.5% detection accuracy while processing 1 Gbps data streams without latency. As a result, BFSI institutions are embedding low-latency analytics into payment rails, credit-risk scoring, and know-your-customer checks to safeguard reputational and financial capital. These deployments underpin a 5.2% lift in the overall CAGR for the high-performance data analytics market.

Surge in AI/ML Model Training Requiring Petabyte-Scale Data Processing

Generative-AI models are doubling in parameter count every six months, demanding petabyte-scale data ingestion and exascale compute clusters. Hyperscale data-center investment tied to AI workloads is set to climb from USD 162.79 billion in 2024 to USD 608.54 billion by 2030. Providers such as Microsoft and Google have earmarked a combined USD 155 billion for next-generation AI facilities. This capital outlay elevates demand for distributed file systems, high-throughput interconnects, and advanced scheduling software, translating into a 6.8% positive push on market growth.

High Total Cost of Ownership for Dedicated HPC Clusters

Capital expenditure on data-center builds is projected to surpass USD 250 billion in 2025, and expanding power needs add a further USD 500 billion through 2030. Many developing nations face electricity shortfalls that hinder the launch of local HPC facilities. Organizations struggle to justify on-premise clusters once equipment, cooling, and skilled-staff costs are tallied, curbing adoption in resource-constrained regions and trimming overall CAGR by 2.1%.

Other drivers and restraints analyzed in the detailed report include:

  1. Growth of Edge-to-Cloud HPC for Smart Manufacturing
  2. Falling Cost-Per-Core for GPU/CPU Clusters Enabling Affordable HPC for SMEs
  3. Shortage of Skilled HPC & Parallel-Programming Professionals

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

The software segment generated 46.2% of revenue in 2024, reflecting demand for user-friendly analytics engines, data-fabric layers, and AI orchestration tools. Vendors are embedding workflow automation and feature-store capabilities that hasten model deployment across business units. DevOps integration is tightening feedback cycles, and license structures are shifting toward consumption-based billing that aligns cost with value creation. Hardware sales remain foundational, propelled by silicon advances such as NVIDIA Blackwell Ultra GPUs that field higher tensor-core density for transformer workloads.

Services are the quickest-expanding line, projected at 25.4% CAGR through 2030. Consulting teams now bundle data-strategy design, MLOps implementation, and continuous-model-tuning services, filling expertise gaps in complex hybrid stacks. Providers are launching AI-as-a-Service offerings that include managed feature engineering, bias auditing, and federated-learning orchestration. These shifts broaden addressable demand and lift the high-performance data analytics market size for service engagements, especially among first-time enterprise adopters.

On-premise deployments held 57.8% share in 2024, anchored by sectors that guard latency or sovereignty, including government and banking. Organizations cite direct hardware control and compliance with strict data-residency statutes as prime motives. Many firms also leverage existing data-center sunk costs, optimizing occupancy rates by refreshing nodes rather than migrating wholesale to cloud.

Cloud platforms are climbing at a 30.1% CAGR, propelled by elastic scaling, consumption pricing, and global edge-zone rollouts. Providers have broadened confidential-computing instances and sovereign-cloud regions to mollify regulatory concerns. Hybrid and multi-cloud patterns now dominate greenfield projects, combining local accelerators with burst capacity for AI training. The shift is enlarging the high-performance data analytics market size attached to consumption models while easing entry for resource-constrained enterprises.

The High-Performance Data Analytics Market Report is Segmented by Component (Hardware, Software, and Services), Deployment Model (On-Premise, and On-Demand/Cloud), Organization Size (Small and Medium Enterprises, and Large Enterprises), End-User Industry (BFSI, Government and Defense, Energy and Utilities, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America maintained 35.4% revenue leadership in 2024, buoyed by deep hyperscaler footprints and early enterprise AI adoption. U.S. data-center supply rose by 26% year on year to 5.2 GW, matching proliferating AI inference demand. Banks such as TD leverage national payment telemetry for instant fraud scoring, underscoring sector maturity. Rental rates in Northern Virginia advanced 41.6% in 2024, evidencing tight capacity that spurs continued build-outs.

Asia-Pacific is the fastest-growing region with a 28% CAGR outlook. India plans to double installed data-center capacity to nearly 1.8 GW by 2026, underwritten by multibillion-dollar commitments from domestic and global investors. Taiwan's facility builds are forecast to exceed USD 3 billion by 2028 to service chip-design simulations and large-language-model training. China is closing the model-quality gap with the United States, with provincial grants catalyzing next-generation AI frameworks. Yet, stringent data-localization rules are compelling firms to engineer country-specific analytics stacks rather than unified global fabrics.

Europe is scaling edge-to-cloud initiatives to modernize manufacturing and critical infrastructure. The EU aims to reach 75% business-cloud adoption and deploy 10,000 climate-neutral edge nodes by 2030. National programs channel capital toward 6G testbeds, telco-edge cloud pilots, and industrial metaverse demonstrators that require low-latency analytics. The opening of the first European AI factories in 2024 provides sovereign compute for automotive, aerospace, and energy firms seeking to train models without exporting data.

  1. Amazon Web Services, Inc. (AWS)
  2. Google LLC
  3. Microsoft Corporation
  4. IBM Corporation
  5. Hewlett Packard Enterprise (HPE)
  6. Dell Technologies Inc.
  7. SAS Institute Inc.
  8. Oracle Corporation
  9. Fujitsu Limited
  10. Intel Corporation
  11. ATOS SE
  12. Juniper Networks Inc.
  13. NEC Corporation
  14. Cisco Systems, Inc.
  15. Teradata Corporation
  16. Cray Inc. (HPE Cray)
  17. Altair Engineering Inc.
  18. Cloudera, Inc.
  19. Huawei Technologies Co., Ltd.
  20. Hitachi Vantara LLC
  21. Super Micro Computer, Inc.

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 Landscape

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Accelerating Adoption of Real-Time Analytics in BFSI for Fraud Detection in North America
    • 4.2.2 Surge in AI/ML Model Training Requiring Petabyte-Scale Data Processing in Asia
    • 4.2.3 Growth of Edge-to-Cloud HPC for Smart Manufacturing in Europe
    • 4.2.4 National Defense Big-Data Modernization Programs Across Middle East Governments
    • 4.2.5 Renewable-Energy Grid Optimization Initiatives Driving HPC Analytics in South America
    • 4.2.6 Falling Cost-per-Core for GPU/CPU Clusters Enabling Affordable HPC for SMEs Globally
  • 4.3 Market Restraints
    • 4.3.1 High Total Cost of Ownership for Dedicated HPC Clusters in Caribbeans and Africa
    • 4.3.2 Shortage of Skilled HPC and Parallel Programming Professionals in Europe and Oceania
    • 4.3.3 Data-Sovereignty Regulations Limiting Cross-Border Cloud Analytics in Asia
    • 4.3.4 Infrastructure Reliability Issues in Emerging Markets Hampering Continuous Data Streams
  • 4.4 Regulatory Outlook
  • 4.5 Technological Outlook
    • 4.5.1 High-Performance Cluster Computing Evolution
    • 4.5.2 Grid Computing
    • 4.5.3 In-Memory Analytics
    • 4.5.4 In-Database Analytics
  • 4.6 Porter's Five Forces Analysis
    • 4.6.1 Bargaining Power of Suppliers
    • 4.6.2 Bargaining Power of Buyers
    • 4.6.3 Threat of New Entrants
    • 4.6.4 Threat of Substitutes
    • 4.6.5 Intensity of Competitive Rivalry
  • 4.7 Investment Analysis

5 Market Size and Growth Forecasts

  • 5.1 By Component
    • 5.1.1 Hardware
    • 5.1.2 Software
    • 5.1.3 Services
  • 5.2 By Deployment Model
    • 5.2.1 On-Premise
    • 5.2.2 On-Demand/Cloud
  • 5.3 By Organization Size
    • 5.3.1 Small and Medium Enterprises (SMEs)
    • 5.3.2 Large Enterprises
  • 5.4 By End-User Industry
    • 5.4.1 Banking, Financial Services and Insurance (BFSI)
    • 5.4.2 Government and Defense
    • 5.4.3 Energy and Utilities
    • 5.4.4 Retail and E-Commerce
    • 5.4.5 Healthcare and Life Sciences
    • 5.4.6 Telecommunication and IT Services
    • 5.4.7 Manufacturing
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 South America
      • 5.5.2.1 Brazil
      • 5.5.2.2 Argentina
      • 5.5.2.3 Chile
      • 5.5.2.4 Peru
      • 5.5.2.5 Rest of South America
    • 5.5.3 Europe
      • 5.5.3.1 Germany
      • 5.5.3.2 United Kingdom
      • 5.5.3.3 France
      • 5.5.3.4 Italy
      • 5.5.3.5 Spain
      • 5.5.3.6 Rest of Europe
    • 5.5.4 Asia-Pacific
      • 5.5.4.1 China
      • 5.5.4.2 Japan
      • 5.5.4.3 South Korea
      • 5.5.4.4 India
      • 5.5.4.5 Australia
      • 5.5.4.6 New Zealand
      • 5.5.4.7 Rest of Asia-Pacific
    • 5.5.5 Middle East
      • 5.5.5.1 United Arab Emirates
      • 5.5.5.2 Saudi Arabia
      • 5.5.5.3 Turkey
      • 5.5.5.4 Rest of Middle East
    • 5.5.6 Africa
      • 5.5.6.1 South Africa
      • 5.5.6.2 Rest of Africa

6 Competitive Landscape

  • 6.1 Strategic Developments
  • 6.2 Vendor Positioning Analysis
  • 6.3 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Products and Services, and Recent Developments)
    • 6.3.1 Amazon Web Services, Inc. (AWS)
    • 6.3.2 Google LLC
    • 6.3.3 Microsoft Corporation
    • 6.3.4 IBM Corporation
    • 6.3.5 Hewlett Packard Enterprise (HPE)
    • 6.3.6 Dell Technologies Inc.
    • 6.3.7 SAS Institute Inc.
    • 6.3.8 Oracle Corporation
    • 6.3.9 Fujitsu Limited
    • 6.3.10 Intel Corporation
    • 6.3.11 ATOS SE
    • 6.3.12 Juniper Networks Inc.
    • 6.3.13 NEC Corporation
    • 6.3.14 Cisco Systems, Inc.
    • 6.3.15 Teradata Corporation
    • 6.3.16 Cray Inc. (HPE Cray)
    • 6.3.17 Altair Engineering Inc.
    • 6.3.18 Cloudera, Inc.
    • 6.3.19 Huawei Technologies Co., Ltd.
    • 6.3.20 Hitachi Vantara LLC
    • 6.3.21 Super Micro Computer, Inc.

7 Market Opportunities and Future Outlook

  • 7.1 White-space and Unmet-Need Assessment