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市场调查报告书
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1859702

图资料库市场预测至2032年：按类型、组件、技术、应用、最终用户和地区分類的全球分析

Graph Database Market Forecasts to 2032 - Global Analysis By Type (SQL-Based Graph Databases and NoSQL-Based Graph Databases), Component, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2025 年，全球图资料库市场规模将达到 29.3 亿美元，到 2032 年将达到 175 亿美元，预测期内复合年增长率为 29.1%。

图资料库是一种NoSQL资料库，旨在储存、管理和查询以节点、边和属性形式结构化的数据，这些节点、边和属性分别代表实体及其关係。与传统的关係型资料库不同，图资料库强调资料之间的关联，从而能够更快、更直观地分析复杂且相互关联的资料集。每个节点代表一个物件（例如人或产品），边代表关係（例如「朋友」或「购买记录」），属性则存储有关这些关係的详细资讯。图资料库非常适合社交网路、诈骗侦测、建议引擎和知识图谱等应用场景，并为关係主导的资料分析和查询提供高效能。

数位转型与云端迁移

企业正从僵化的关係模型转向能够捕捉复杂关係和动态互动的灵活图结构。云端原生图平台支援可扩展储存、即时查询以及与人工智慧/机器学习管道的整合。企业正在使用图资料库来建模分散式环境中的客户旅程、供应链和网路拓扑结构。金融、通讯和医疗保健产业对具备关係感知能力且敏捷的数据基础设施的需求日益增长。这一趋势正在推动云端优先、数据密集型企业采用此类平台。

高昂的实施和营运成本

采用图资料库需要投资于专用基础设施、模式设计和查询最佳化工具。与现有资料湖、ETL管道和分析平台的整合会增加复杂性和开销。缺乏熟练人才和标准化培训会阻碍图数据库的采用和性能调优。缺乏明确用例和资料准备的企业在证明投资报酬率方面面临挑战。这些限制因素阻碍了成本敏感型和营运受限型企业采用图资料库。

在大量使用关係建模的行业中的应用案例

该平台透过基于图的分析，支援诈骗侦测、药物发现、路线优化和影响者映射。与可视化工具和图演算法的集成，实现了模式识别、异常检测和预测建模。在受监管和高交易量行业，对可扩展的、特定领域的图解决方案的需求日益增长。这些趋势正在推动以关係为中心的数据生态系统的创新和平台扩展。

旧有系统整合与迁移挑战

关联资料库和孤立的资料架构本身并不支援图结构和遍历逻辑。迁移需要进行资料转换、模式重新设计以及下游分析工作流程的重新配置。与传统 BI 工具和彙报系统的不相容性阻碍了跨职能协作和相关人员的认可。这些限制持续限制平台在传统系统密集型组织中的成熟度和部署。

新冠疫情的影响：

疫情加速了图资料库的普及，各组织机构纷纷寻求即时洞察供应链、追踪密切接触者和优化数位化互动。企业利用图平台模拟病毒传播、优化物流，并跨远端通路打造个人化数位体验。云端原生架构实现了跨分散式团队和资料来源的快速部署和扩充性。医疗保健、电子商务和公共服务领域对关係感知分析的需求激增。后疫情时代的策略越来越重视资料资料库，将其视为提升资料敏捷性、韧性和创新能力的核心。这种转变强化了对图基础设施和分析平台的长期投资。

预计在预测期内，属性图部分将是最大的部分。

由于其灵活性、表达能力以及在企业应用中的广泛应用，属性图资料库预计将在预测期内占据最大的市场份额。该平台使用带有键值属性标籤的节点和边来建模复杂的关係和元资料。与 Cypher 和 Gremlin 等查询语言的整合支援对动态资料集进行直觉的遍历和模式匹配。客户分析、诈骗侦测、知识图谱等领域对可扩展、与模式无关的图模型的需求日益增长。这些特性正在推动该细分市场在图资料库应用中占据主导地位。

在预测期内，基于 SQL 的图资料库将以最高的复合年增长率成长。

预计在预测期内，基于 SQL 的图资料库领域将实现最高的成长率。各平台将图扩展整合到其 SQL 引擎中，以支援结构化模式中的邻接表、递归查询和图遍历。与现有 BI 工具、资料仓储和合规框架的集成，能够实现更顺畅的部署和管治。金融、通讯和製造业等产业对可互通、低摩擦的图解决方案的需求日益增长。这一趋势正在推动 SQL 原生图平台和分析生态系统的发展。

比最大的地区

在预测期内，北美预计将占据最大的市场份额，这主要得益于其成熟的企业IT环境、云端技术的广泛应用以及贯穿整个资料基础设施的创新文化。美国和加拿大的企业正在金融、医疗保健、零售和政府部门部署图资料库，以支援即时分析和关係建模。对人工智慧、网路安全和数位转型的投资也为该平台的扩充性和整合性提供了支援。主要供应商、系统整合商和开发团体的存在正在推动生态系统的成熟和普及。这些因素共同促成了北美在图资料库部署和商业化领域的领先地位。

复合年增长率最高的地区：

在预测期内，随着数位转型、行动优先策略和数据现代化在亚太地区经济中日益普及，该地区预计将实现最高的复合年增长率。印度、中国、新加坡和澳洲等国家正在通讯、物流、教育和公共服务等领域大规模部署图资料库平台。政府支持的计画为数据基础设施建设、Start-Ups孵化以及人工智慧在图分析中的应用提供了助力。本地供应商和全球服务商提供多语言、高性价比的解决方案，以满足区域合规性和应用场景的需求。这些趋势正在推动该地区图资料库创新和应用的成长。

免费提供客製化服务：

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  • 公司简介
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 原始研究资料
    • 二手研究资料
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球图资料库市场（按类型划分）

  • 基于 SQL 的图资料库
  • 基于 NoSQL 的图资料库

6. 全球图资料库市场（按组件划分）

  • 解决方案
    • 视觉化和查询工具
    • 图资料库引擎
    • 整合中介软体
  • 服务
    • 咨询与实施
    • 培训和支持
    • 託管服务

7. 全球图资料库市场（依技术划分）

  • 属性图
  • RDF（资源说明框架）
  • 原生图引擎与非原生图引擎
  • 查询语言（Cypher、Gremlin、SPARQL）
  • 视觉化和分析工具

第八章：全球图资料库市场（按应用划分）

  • 诈欺侦测
  • 建议引擎
  • 网路和IT维
  • 供应链优化
  • 知识图谱
  • 身分和存取管理
  • 其他用途

9. 全球图资料库市场（依最终用户划分）

  • 银行、金融服务和保险（BFSI）
  • 通讯/IT
  • 零售与电子商务
  • 医疗保健和生命科学
  • 製造业
  • 政府/公共部门
  • 其他最终用户

第十章：全球图资料库市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十二章 企业概况

  • Neo4j
  • Oracle
  • IBM
  • Microsoft
  • Amazon Web Services
  • TigerGraph
  • DataStax
  • ArangoDB
  • Ontotext
  • GraphDB
  • Franz Inc.
  • Cambridge Semantics
  • TerminusDB
  • Dgraph Labs
  • GraphAware
Product Code: SMRC31926

According to Stratistics MRC, the Global Graph Database Market is accounted for $2.93 billion in 2025 and is expected to reach $17.5 billion by 2032 growing at a CAGR of 29.1% during the forecast period. A Graph Database is a type of NoSQL database designed to store, manage, and query data structured as nodes, edges, and properties, representing entities and their relationships. Unlike traditional relational databases, it emphasizes the connections between data, enabling faster and more intuitive analysis of complex, interrelated datasets. Each node represents an object (like a person or product), edges represent relationships (such as "friend" or "purchased"), and properties store details about them. Graph databases are ideal for use cases like social networks, fraud detection, recommendation engines, and knowledge graphs, offering high performance in relationship-driven data analysis and querying.

Market Dynamics:

Driver:

Digital transformation and cloud migrations

Organizations are shifting from rigid relational models to flexible graph structures that capture complex relationships and dynamic interactions. Cloud-native graph platforms support scalable storage, real-time querying, and integration with AI/ML pipelines. Enterprises use graph databases to model customer journeys, supply chains, and network topologies across distributed environments. Demand for agile and relationship-aware data infrastructure is rising across finance, telecom, and healthcare sectors. These dynamics are propelling platform deployment across cloud-first and data-intensive organizations.

Restraint:

High implementation & operational cost

Graph database deployment requires investment in specialized infrastructure, schema design, and query optimization tools. Integration with existing data lakes, ETL pipelines, and analytics platforms increases complexity and overhead. Lack of skilled personnel and standardized training hampers adoption and performance tuning. Enterprises face challenges in justifying ROI without clear use-case alignment or data readiness. These constraints continue to hinder adoption across cost-sensitive and operationally constrained organizations.

Opportunity:

Use-cases in industries with heavy relationship modelling

Platforms support fraud detection, drug discovery, route optimization, and influencer mapping through graph-based analytics. Integration with visualization tools and graph algorithms enables pattern recognition, anomaly detection, and predictive modeling. Demand for scalable and domain-specific graph solutions is rising across regulated and high-volume sectors. These trends are fostering innovation and platform expansion across relationship-centric data ecosystems.

Threat:

Integration & migration challenges with legacy systems

Relational databases and siloed data architectures lack native support for graph structures and traversal logic. Migration requires data transformation, schema redesign, and reconfiguration of downstream analytics workflows. Incompatibility with legacy BI tools and reporting systems hampers cross-functional alignment and stakeholder buy-in. These limitations continue to constrain platform maturity and enterprise-wide deployment across legacy-heavy organizations.

Covid-19 Impact:

The pandemic accelerated graph database adoption as organizations sought real-time insights into supply chains, contact tracing, and digital engagement. Enterprises used graph platforms to model virus transmission, optimize logistics, and personalize digital experiences across remote channels. Cloud-native architecture enabled rapid deployment and scalability across distributed teams and data sources. Demand for relationship-aware analytics surged across healthcare, e-commerce, and public services. Post-pandemic strategies now include graph databases as a core pillar of data agility, resilience, and innovation. These shifts are reinforcing long-term investment in graph infrastructure and analytics platforms.

The property graphs segment is expected to be the largest during the forecast period

The property graphs segment is expected to account for the largest market share during the forecast period due to their flexibility, expressiveness, and widespread adoption across enterprise applications. Platforms use labeled nodes and edges with key-value properties to model complex relationships and metadata. Integration with query languages like Cypher and Gremlin supports intuitive traversal and pattern matching across dynamic datasets. Demand for scalable and schema-agnostic graph models is rising across customer analytics, fraud detection, and knowledge graphs. These capabilities are boosting segment dominance across graph database deployments.

The SQL-based graph databases segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the SQL-based graph databases segment is predicted to witness the highest growth rate as enterprises seek hybrid solutions that combine relational familiarity with graph capabilities. Platforms embed graph extensions into SQL engines to support adjacency lists, recursive queries, and graph traversal within structured schemas. Integration with existing BI tools, data warehouses, and compliance frameworks enables smoother adoption and governance. Demand for interoperable and low-friction graph solutions is rising across finance, telecom, and manufacturing sectors. These dynamics are accelerating growth across SQL-native graph platforms and analytics ecosystems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its mature enterprise IT landscape, cloud adoption, and innovation culture across data infrastructure. U.S. and Canadian firms deploy graph databases across finance, healthcare, retail, and government sectors to support real-time analytics and relationship modeling. Investment in AI, cybersecurity, and digital transformation supports platform scalability and integration. Presence of leading vendors, system integrators, and developer communities drives ecosystem maturity and adoption. These factors are propelling North America's leadership in graph database deployment and commercialization.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as digital transformation, mobile-first strategies, and data modernization converge across regional economies. Countries like India, China, Singapore, and Australia scale graph platforms across telecom, logistics, education, and public services. Government-backed programs support data infrastructure, startup incubation, and AI integration across graph analytics. Local vendors and global providers offer multilingual and cost-effective solutions tailored to regional compliance and use-case needs. These trends are accelerating regional growth across graph database innovation and adoption.

Key players in the market

Some of the key players in Graph Database Market include Neo4j, Oracle, IBM, Microsoft, Amazon Web Services, TigerGraph, DataStax, ArangoDB, Ontotext, GraphDB, Franz Inc., Cambridge Semantics, TerminusDB, Dgraph Labs and GraphAware.

Key Developments:

In September 2025, Neo4j launched Infinigraph, a breakthrough distributed graph architecture supporting 100TB+ scale for unified operational and analytical workloads. Infinigraph enables real-time transactions and analytics in a single system without graph fragmentation or infrastructure duplication. It guarantees full ACID compliance, even with billions of relationships and thousands of concurrent queries, positioning Neo4j for enterprise-grade graph deployments.

In April 2025, IBM expanded its Watson Knowledge Catalog with enhanced graph-based metadata management, enabling enterprise clients to build semantic search and relationship-aware data discovery. The update supports multi-cloud deployments and AI model training, positioning IBM's graph capabilities as foundational for enterprise knowledge graphs and contextual analytics.

Types Covered:

  • SQL-Based Graph Databases
  • NoSQL-Based Graph Databases

Components Covered:

  • Solutions
  • Services

Technologies Covered:

  • Property Graphs
  • RDF (Resource Description Framework)
  • Native vs Non-Native Graph Engines
  • Query Languages (Cypher, Gremlin, SPARQL)
  • Visualization & Analytics Tools

Applications Covered:

  • Fraud Detection
  • Recommendation Engines
  • Network & IT Operations
  • Supply Chain Optimization
  • Knowledge Graphs
  • Identity & Access Management
  • Other Applications

End Users Covered:

  • Banking, Financial Services & Insurance (BFSI)
  • Telecom & IT
  • Retail & E-Commerce
  • Healthcare & Life Sciences
  • Manufacturing
  • Government & Public Sector
  • 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 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 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 Graph Database Market, By Type

  • 5.1 Introduction
  • 5.2 SQL-Based Graph Databases
  • 5.3 NoSQL-Based Graph Databases

6 Global Graph Database Market, By Component

  • 6.1 Introduction
  • 6.2 Solutions
    • 6.2.1 Visualization & Query Tools
    • 6.2.2 Graph Database Engines
    • 6.2.3 Integration Middleware
  • 6.3 Services
    • 6.3.1 Consulting & Implementation
    • 6.3.2 Training & Support
    • 6.3.3 Managed Services

7 Global Graph Database Market, By Technology

  • 7.1 Introduction
  • 7.2 Property Graphs
  • 7.3 RDF (Resource Description Framework)
  • 7.4 Native vs Non-Native Graph Engines
  • 7.5 Query Languages (Cypher, Gremlin, SPARQL)
  • 7.6 Visualization & Analytics Tools

8 Global Graph Database Market, By Application

  • 8.1 Introduction
  • 8.2 Fraud Detection
  • 8.3 Recommendation Engines
  • 8.4 Network & IT Operations
  • 8.5 Supply Chain Optimization
  • 8.6 Knowledge Graphs
  • 8.7 Identity & Access Management
  • 8.8 Other Applications

9 Global Graph Database Market, By End User

  • 9.1 Introduction
  • 9.2 Banking, Financial Services & Insurance (BFSI)
  • 9.3 Telecom & IT
  • 9.4 Retail & E-Commerce
  • 9.5 Healthcare & Life Sciences
  • 9.6 Manufacturing
  • 9.7 Government & Public Sector
  • 9.8 Other End Users

10 Global Graph Database Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Neo4j
  • 12.2 Oracle
  • 12.3 IBM
  • 12.4 Microsoft
  • 12.5 Amazon Web Services
  • 12.6 TigerGraph
  • 12.7 DataStax
  • 12.8 ArangoDB
  • 12.9 Ontotext
  • 12.10 GraphDB
  • 12.11 Franz Inc.
  • 12.12 Cambridge Semantics
  • 12.13 TerminusDB
  • 12.14 Dgraph Labs
  • 12.15 GraphAware

List of Tables

  • Table 1 Global Graph Database Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Graph Database Market Outlook, By Type (2024-2032) ($MN)
  • Table 3 Global Graph Database Market Outlook, By SQL-Based Graph Databases (2024-2032) ($MN)
  • Table 4 Global Graph Database Market Outlook, By NoSQL-Based Graph Databases (2024-2032) ($MN)
  • Table 5 Global Graph Database Market Outlook, By Component (2024-2032) ($MN)
  • Table 6 Global Graph Database Market Outlook, By Solutions (2024-2032) ($MN)
  • Table 7 Global Graph Database Market Outlook, By Visualization & Query Tools (2024-2032) ($MN)
  • Table 8 Global Graph Database Market Outlook, By Graph Database Engines (2024-2032) ($MN)
  • Table 9 Global Graph Database Market Outlook, By Integration Middleware (2024-2032) ($MN)
  • Table 10 Global Graph Database Market Outlook, By Services (2024-2032) ($MN)
  • Table 11 Global Graph Database Market Outlook, By Consulting & Implementation (2024-2032) ($MN)
  • Table 12 Global Graph Database Market Outlook, By Training & Support (2024-2032) ($MN)
  • Table 13 Global Graph Database Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 14 Global Graph Database Market Outlook, By Technology (2024-2032) ($MN)
  • Table 15 Global Graph Database Market Outlook, By Property Graphs (2024-2032) ($MN)
  • Table 16 Global Graph Database Market Outlook, By RDF (Resource Description Framework) (2024-2032) ($MN)
  • Table 17 Global Graph Database Market Outlook, By Native vs Non-Native Graph Engines (2024-2032) ($MN)
  • Table 18 Global Graph Database Market Outlook, By Query Languages (Cypher, Gremlin, SPARQL) (2024-2032) ($MN)
  • Table 19 Global Graph Database Market Outlook, By Visualization & Analytics Tools (2024-2032) ($MN)
  • Table 20 Global Graph Database Market Outlook, By Application (2024-2032) ($MN)
  • Table 21 Global Graph Database Market Outlook, By Fraud Detection (2024-2032) ($MN)
  • Table 22 Global Graph Database Market Outlook, By Recommendation Engines (2024-2032) ($MN)
  • Table 23 Global Graph Database Market Outlook, By Network & IT Operations (2024-2032) ($MN)
  • Table 24 Global Graph Database Market Outlook, By Supply Chain Optimization (2024-2032) ($MN)
  • Table 25 Global Graph Database Market Outlook, By Knowledge Graphs (2024-2032) ($MN)
  • Table 26 Global Graph Database Market Outlook, By Identity & Access Management (2024-2032) ($MN)
  • Table 27 Global Graph Database Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 28 Global Graph Database Market Outlook, By End User (2024-2032) ($MN)
  • Table 29 Global Graph Database Market Outlook, By Banking, Financial Services & Insurance (BFSI) (2024-2032) ($MN)
  • Table 30 Global Graph Database Market Outlook, By Telecom & IT (2024-2032) ($MN)
  • Table 31 Global Graph Database Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 32 Global Graph Database Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 33 Global Graph Database Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 34 Global Graph Database Market Outlook, By Government & Public Sector (2024-2032) ($MN)
  • Table 35 Global Graph 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.