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市場調查報告書
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1859702

圖資料庫市場預測至2032年：按類型、組件、技術、應用、最終用戶和地區分類的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2025 年，全球圖資料庫市場規模將達到 29.3 億美元，到 2032 年將達到 175 億美元，預測期內複合年成長率為 29.1%。

圖資料庫是一種NoSQL資料庫，旨在儲存、管理和查詢以節點、邊和屬性形式結構化的數據，這些節點、邊和屬性分別代表實體及其關係。與傳統的關係型資料庫不同，圖資料庫強調資料之間的關聯，從而能夠更快、更直覺地分析複雜且相互關聯的資料集。每個節點代表一個物件（例如人或產品），邊代表關係（例如「朋友」或「購買記錄」），屬性則存儲有關這些關係的詳細資訊。圖資料庫非常適合社交網路、詐騙偵測、建議引擎和知識圖譜等應用場景，並為關係主導的資料分析和查詢提供高效能。

數位轉型與雲端遷移

企業正從僵化的關係模型轉向能夠捕捉複雜關係和動態互動的靈活圖結構。雲端原生圖平台支援可擴展儲存、即時查詢以及與人工智慧/機器學習管道的整合。企業正在使用圖資料庫來建模分散式環境中的客戶旅程、供應鏈和網路拓撲結構。金融、通訊和醫療保健產業對具備關係感知能力且敏捷的數據基礎設施的需求日益成長。這一趨勢正在推動雲端優先、數據密集型企業採用此類平台。

高昂的實施和營運成本

採用圖資料庫需要投資於專用基礎設施、模式設計和查詢最佳化工具。與現有資料湖、ETL管道和分析平台的整合會增加複雜性和開銷。缺乏熟練人才和標準化培訓會阻礙圖數據庫的採用和性能調優。缺乏明確用例和資料準備的企業在證明投資報酬率方面面臨挑戰。這些限制因素阻礙了成本敏感型和營運受限型企業採用圖資料庫。

在大量使用關係建模的行業中的應用案例

該平台透過基於圖的分析，支援詐騙偵測、藥物發現、路線最佳化和影響者映射。與可視化工具和圖演算法的整合，實現了模式識別、異常檢測和預測建模。在受監管和高交易量行業，對可擴展的、特定領域的圖解決方案的需求日益成長。這些趨勢正在推動以關係為中心的數據生態系統的創新和平台擴展。

舊有系統整合與遷移挑戰

關聯資料庫和孤立的資料架構本身並不支援圖結構和遍歷邏輯。遷移需要進行資料轉換、模式重新設計以及下游分析工作流程的重新配置。與傳統 BI 工具和彙報系統的不相容性阻礙了跨職能協作和相關人員的認可。這些限制持續限制平台在傳統系統密集型組織中的成熟度和部署。

新冠疫情的影響：

疫情加速了圖資料庫的普及，各組織機構紛紛尋求即時洞察供應鏈、追蹤密切接觸者和最佳化數位化互動。企業利用圖平台模擬病毒傳播、最佳化物流，並跨遠端通路打造個人化數位體驗。雲端原生架構實現了跨分散式團隊和資料來源的快速部署和擴充性。醫療保健、電子商務和公共服務領域對關係感知分析的需求激增。後疫情時代的策略越來越重視資料資料庫，將其視為提升資料敏捷性、韌性和創新能力的核心。這種轉變強化了對圖基礎設施和分析平台的長期投資。

預計在預測期內，屬性圖部分將是最大的部分。

由於其靈活性、表達能力以及在企業應用中的廣泛應用，屬性圖資料庫預計將在預測期內佔據最大的市場佔有率。該平台使用帶有鍵值屬性標籤的節點和邊來建模複雜的關係和元資料。與 Cypher 和 Gremlin 等查詢語言的整合支援對動態資料集進行直覺的遍歷和模式匹配。客戶分析、詐騙偵測、知識圖譜等領域對可擴展、與模式無關的圖模型的需求日益成長。這些特性正在推動該細分市場在圖資料庫應用中佔據主導地位。

在預測期內，基於 SQL 的圖資料庫將以最高的複合年成長率成長。

預計在預測期內，基於 SQL 的圖資料庫領域將實現最高的成長率。各平台將圖擴展整合到其 SQL 引擎中，以支援結構化模式中的鄰接表、遞歸查詢和圖遍歷。與現有 BI 工具、資料倉儲和合規框架的整合，能夠實現更順暢的部署和管治。金融、通訊和製造業等產業對可互通、低摩擦的圖解決方案的需求日益成長。這一趨勢正在推動 SQL 原生圖平台和分析生態系統的發展。

比最大的地區

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其成熟的企業IT環境、雲端技術的廣泛應用以及貫穿整個資料基礎設施的創新文化。美國和加拿大的企業正在金融、醫療保健、零售和政府部門部署圖資料庫，以支援即時分析和關係建模。對人工智慧、網路安全和數位轉型的投資也為該平台的擴充性和整合性提供了支援。主要供應商、系統整合商和開發團體的存在正在推動生態系統的成熟和普及。這些因素共同促成了北美在圖資料庫部署和商業化領域的領先地位。

複合年成長率最高的地區：

在預測期內，隨著數位轉型、行動優先策略和數據現代化在亞太地區經濟中日益普及，該地區預計將實現最高的複合年成長率。印度、中國、新加坡和澳洲等國家正在通訊、物流、教育和公共服務等領域大規模部署圖資料庫平台。政府支持的計畫為數據基礎設施建設、Start-Ups孵化以及人工智慧在圖分析中的應用提供了助力。本地供應商和全球服務商提供多語言、具成本效益的解決方案，以滿足區域合規性和應用場景的需求。這些趨勢正在推動該地區圖資料庫創新和應用的成長。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 原始研究資料
    • 二手研究資料
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 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
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球圖資料庫市場（按類型分類）

  • 基於 SQL 的圖資料庫
  • 基於 NoSQL 的圖資料庫

6. 全球圖資料庫市場（按組件分類）

  • 解決方案
    • 視覺化和查詢工具
    • 圖資料庫引擎
    • 整合中介軟體
  • 服務
    • 諮詢與實施
    • 培訓和支持
    • 託管服務

7. 全球圖資料庫市場（依技術分類）

  • 屬性圖
  • RDF（資源說明框架）
  • 原生圖引擎與非原生圖引擎
  • 查詢語言（Cypher、Gremlin、SPARQL）
  • 視覺化和分析工具

第8章：全球圖資料庫市場（按應用分類）

  • 詐欺偵測
  • 建議引擎
  • 網路和IT維
  • 供應鏈最佳化
  • 知識圖譜
  • 身分和存取管理
  • 其他用途

9. 全球圖資料庫市場（依最終用戶分類）

  • 銀行、金融服務和保險（BFSI）
  • 通訊/IT
  • 零售與電子商務
  • 醫療保健和生命科學
  • 製造業
  • 政府/公共部門
  • 其他最終用戶

第10章：全球圖資料庫市場（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
    • 南美洲其他地區
  • 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11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與併購
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第12章 企業概況

  • 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.