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市场调查报告书

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1635137

全球图形资料库市场（按解决方案和应用）- 2030 年预测

Graph Database Market by Solutions (Graph Extension, Graph Processing Engines, Native Graph Database, Knowledge Graph Engines), Application (Data Governance and Master Data Management, Infrastructure and Asset Management) - Global Forecast to 2030

出版日期: 2025年01月13日 | 出版商:

MarketsandMarkets | 英文 369 Pages | 订单完成后即时交付

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简介目录图表

预计 2024 年全球图形资料库市场规模为 5.076 亿美元，到 2030 年将达到 21.43 亿美元，复合年增长率为 27.1%。

图形资料库能够实现更准确、更有洞察力的资料分析，处于人工智慧和机器学习崛起的前沿。图形资料库非常擅长处理互联资料，使得 AI/ML 模型能够找到传统系统无法发现的更深层的关係和隐藏模式。图形资料库对复杂资料结构的支援提高了预测准确性，使其对于诈欺检测、个人化推荐和客户洞察等应用至关重要。人工智慧和机器学习的进步使图形资料库能够支援大量资料集，使得资料主导的决策更加可预测且高度可靠。

研究范围
调查年份	2019-2030
基准年	2024
预测期	2024-2030
单元	100万美元
部分	解决方案、应用程式（资料管治、主资料管理、基础架构和资产管理）
目标区域	北美、欧洲、亚太地区、中东和非洲、拉丁美洲

“从垂直角度来看，BFSI 部门将在预测期内占据最大的市场规模。”

图形资料库将透过实现对复杂互联资料集的即时洞察，彻底改变 BFSI 领域。它对预防付款诈骗特别有效，因为它可以检测到传统分析解决方案所遗漏的跨多个连接的复杂模式。图形资料库可以将内部财务资料连结到外部资料库（例如製裁或政治公众人物 (PEP) 名单）以满足监管合规目的，从而帮助降低风险。它还可以透过分析各种财务记录和交易之间的关係来帮助更好地评估信用风险。在客户参与方面，图形资料库有助于开发完整的 360 度视图，统一来自管道的资料以增强个人化和交叉销售，同时最大限度地减少客户流失。这种整体方法将使 BFSI 机构能够提供符合客户期望和动态市场发展的服务，从而保持相关性。

“预测期内，基础设施和资产管理领域将实现最高增长率。”

图形资料库透过实现复杂资产网路和相互关係的建模，为基础设施和资产管理提供了重要支援。这使组织能够有效地追踪资产的状态、位置和生命週期，提供整个基础设施的即时视图。此功能有助于优化维护计划并识别风险，帮助您就资产利用和升级做出更明智的决策。此外，图形资料库有助于识别模式和依赖关係，从而改善预测性维护和效能。图形资料库关联维护记录、使用情况统计资料和运作条件等资料点，以提高资源利用率、减少停机时间并提高营运效率。

本报告对全球图形资料库市场进行了深入分析，包括关键驱动因素和限制因素、竞争格局和未来趋势。

图形资料库市场主要企业的机会
图形资料库市场（依产品分类）
图形资料库市场（按服务）
专业服务图形资料库市场
图形资料库市场（按应用）
图形资料库市场（按模型类型）
图形资料库市场（按行业）
北美图形资料库市场（按产品和模型类型）

第五章市场概况及产业趋势

市场动态
- 驱动程式
- 限制因素
- 机会
- 任务
图形资料库市场最佳实践
图形资料库市场的演变
生态系分析
案例研究分析
供应链分析
投资金筹措场景
生成式人工智慧对图形资料库市场的影响
图形资料库市场的技术蓝图
监管格局
- 监管机构、政府机构和其他组织
- 主要法规
专利分析
- 调查方法
- 关键专利列表
技术分析
- 关键技术
- 互补技术
- 相关技术
定价分析
- 各国主要企业平均销售价格（2023年）
- 参考价格分析：主要企业（2023 年）
重大会议和活动（2024-2025 年）
波特五力分析
影响客户业务的趋势/中断
主要相关利益者和采购标准

第 6 章图形资料库市场（按产品）

介绍
解决方案
服务

第 7 章图形资料库市场（依模型类型）

介绍
RDF（Resource Description Framework）
属性图

第 8 章图形资料库市场（按应用）

介绍
资料管治与主资料管理
资料分析和商业智慧
知识内容管理
虚拟助理、自助资料、数位资产发现
产品和配置管理
基础设施和资产管理
流程优化与资源管理
风险管理、合规和监管报告
市场/客户情报、销售优化
其他用途

第 9 章图形资料库市场（依垂直产业划分）

介绍
BFSI
零售与电子商务
通讯和技术
医疗、生命科学、製药
政府和公共部门
製造/汽车
媒体与娱乐
能源与公共事业
旅游与饭店
运输和物流
其他行业

第 10 章图形资料库市场（按地区）

介绍
北美洲
- 北美宏观经济展望
- 美国
- 加拿大
欧洲
- 欧洲宏观经济展望
- 英国
- 义大利
- 德国
- 法国
- 西班牙
- 欧洲其他地区
亚太地区
- 亚太宏观经济展望
- 中国
- 印度
- 日本
- 澳洲和纽西兰
- 韩国
- 其他亚太地区
中东和非洲
- 中东和非洲宏观经济展望
- 中东
- 非洲
拉丁美洲
- 拉丁美洲宏观经济展望
- 巴西
- 阿根廷
- 墨西哥
- 其他拉丁美洲国家

第十一章竞争格局

介绍
主要参与企业的策略/优势
市场占有率分析（2024年）
收益分析（2019-2023）
企业评估矩阵：主要企业（2024 年）
公司评估矩阵：Start-Ups/中小企业（2024 年）
竞争场景
比较品牌
公司估值和财务指标

第十二章公司简介

主要企业
- NEO4J
- AMAZON WEB SERVICES, INC
- TIGERGRAPH
- RELATIONALAI
- GRAPHWISE
- IBM CORPORATION
- MICROSOFT CORPORATION, INC.
- ONTOTEXT
- STAR DOG
- ALTAIR
- ORACLE CORPORATION
- PROGRESS SOFTWARE
- FRANZ INC.
- DATASTAX
- DGRAPH LABS
- OPENLINK SOFTWARE
Start-Ups/小型企业
- OXFORD SEMANTIC TECHNOLOGIES
- BITNINE
- ARANGODB
- FLUREE
- BLAZEGRAPH
- MEMGRAPH
- OBJECTIVITY INC
- GRAPHBASE
- GRAPH STORY
- FALKORDB

第十三章相邻及相关市场

介绍
市场定义
云端资料库、DBaaS市场
- 市场定义
- 市场概况
向量资料库市场
- 市场定义
- 按类型分類的向量资料库市场
- 向量资料库市场（按技术）
- 按行业分類的向量资料库市场
- 按地区分類的向量资料库市场

第十四章附录

简介目录图表

Product Code: TC 6341

The Graph Database market is estimated at USD 507.6 million in 2024 to USD 2,143.0 million by 2030, at a Compound Annual Growth Rate (CAGR) of 27.1%. Graph databases are at the forefront of the rise of AI and ML by making it possible to analyze data more accurately and with deeper insights. Graph databases handle interconnected data very well, and this is what enables AI/ML models to find more profound relationships and hidden patterns that traditional systems might miss. Complex data structures are supported by graph databases, improving predictive accuracy and making them indispensable in applications such as fraud detection, personalized recommendations, and customer insights. With AI and ML advancement, graph databases are available to support massive datasets so that the predictability would be higher, and the data-driven decisions could be quite reliable.

Scope of the Report
Years Considered for the Study	2019-2030
Base Year	2024
Forecast Period	2024-2030
Units Considered	USD (Million)
Segments	By Solutions, Application (Data Governance and Master Data Management, Infrastructure and Asset Management)
Regions covered	North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

"By vertical, the BFSI segment will hold the largest market size during the forecast period."

Graph databases revolutionize the BFSI sector by allowing real-time insights into complex, interconnected datasets. It is especially effective in payment fraud because it can detect intricate patterns that stretch over multiple connections, which are otherwise missed by traditional analytics solutions. Graph databases help reduce risks by linking internal financial data with external databases, including sanctions and politically exposed persons (PEP) lists, for regulatory compliance. The databases also help improve credit risk evaluation, analyzing relationships across various financial records and transactions. In customer engagement, graph databases aid in developing a complete 360-degree view and integrate data from channels to enhance personalization and cross-selling while minimizing churn. This holistic approach allows BFSI institutions to provide tailored services and remain relevant in evolving customer expectations and dynamic markets.

"The Infrastructure and Asset Management segment will register the fastest growth rate during the forecast period."

Graph databases provide Infrastructure and Asset Management with crucial support by enabling the modeling of complex asset networks and interrelations. They allow organizations to efficiently track the status, location, and lifecycle of assets to have an overall real-time view of the infrastructure. This facility helps optimize maintenance planning and identifies risk, therefore helping make wise decisions on asset utilization and upgrade. In addition, graph databases help identify patterns and dependencies with predictive maintenance and performance improvement. They enhance resource use, reduce downtime, and improve operational efficiency by correlating data points like maintenance records, usage statistics, and operational conditions.

"Asia Pacific will witness the highest market growth rate during the forecast period."

The graph database market in Asia-Pacific is gaining traction due to businesses and governments seeking more advanced solutions to managing interconnected data. In Japan, Fujitsu has played a critical role in merging knowledge graphs with generative AI technologies to improve logical reasoning and decrease AI hallucinations. Progress made has been immense with such projects as GENIAC. This fusion of AI and graph technology is also being applied to conversational AI, making the outputs of businesses more reliable and accurate. Graph databases are being implemented in India in innovative city initiatives and logistics sectors, with companies such as Neo4j providing solutions to manage big data and enhance real-time decision-making. Similarly, in South Korea, graph databases are being widely implemented across various sectors, from the telecom to the manufacturing industry, to provide better data management and analytics services toward implementing a smart city and Industry 4.0.

In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the Graph Database market.

By Company Type: Tier 1 - 40%, Tier 2 - 35%, and Tier 3 - 25%
By Designation: Directors -25%, Managers - 35%, and Others - 40%
By Region: North America - 37%, Europe - 42%, Asia Pacific - 21

The major players in the Graph Database market include IBM Corporation (US), Oracle (US), Microsoft Corporation (US), AWS (US), Neo4j (US), RelationaAI (US), Progress Software (US), TigerGraph (US), Stardog (US), Datastax (US), Franz Inc (US), Ontotext (Bulgaria), Openlink Software (US), Dgraph Labs (US), Graphwise (US), Altair (US), Bitnine ( South Korea) ArangoDB (US), Fluree (US), Blazegraph (US), Memgraph UK), Objectivity (US), GraphBase (Australia), Graph Story (US), Oxford Semantic Technologies (UK), and FalkorDB (Israel). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, enhancements, and acquisitions to expand their Graph Database market footprint.

Research Coverage

The market study covers the Graph Database market size across different segments. It aims at estimating the market size and the growth potential across various segments, including by offering (solutions (by type (Graph Extension, Graph Processing Engines, Native Graph Database, Knowledge Graph Engines) by deployment type (cloud, on-premises) and services (professional services (consulting services, deployment and integration services, support and maintenance services) managed services) by model type (resource description framework, property graph (Labeled property graph (LPG), Typed property graph)), by application (data governance and master data management , data analytics and business intelligence, knowledge and content management, virtual assistants, self-service data and digital asset discovery, product and configuration management, infrastructure and asset management, process optimization and resource management, risk management, compliance, regulatory reporting, market and customer intelligence, sales optimization, other applications) by vertical (Banking, Financial Services, and Insurance (BFSI), retail and e-commerce, healthcare, life sciences, and pharmaceuticals, telecom and technology, government, manufacturing and automotive, media & entertainment, energy, utilities and infrastructure, travel and hospitality, transportation and logistics, other verticals) and Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The study includes an in-depth competitive analysis of the leading market players, their company profiles, key observations related to product and business offerings, recent developments, and market strategies.

Key Benefits of Buying the Report

The report will help the market leaders/new entrants with information on the closest approximations of the global Graph Database market's revenue numbers and subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market's pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

Analysis of key drivers (the rising demand for generative AI, need to incorporate real-time big data mining with result visualization, growing demand for solutions to process low-latency queries, massive data generation across BFSI, retail, and media & entertainment industries, rapid use of virtualization for big data analytics), restraints (shortage of standardization and programming ease) opportunities (data unification and rapid proliferation of knowledge graphs, provision of semantic knowledgeable graphs to address complex-scientific research, emphasis on the emergence of open knowledge networks), and challenges (lack of technical expertise) influencing the growth of the Graph Database market.

Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Graph Database market.

Market Development: The report provides comprehensive information about lucrative markets and analyses the Graph Database market across various regions.

Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the Graph Database market.

Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading include IBM Corporation (US), Oracle (US), Microsoft Corporation (US), AWS (US), Neo4j (US), RelationalAI (US), Progress Software (US), TigerGraph (US), Stardog (US), Datastax (US), Franz Inc (US), Ontotext (Bulgaria), Openlink Software (US), Dgraph Labs (US), Graphwise (US), Altair (US), Bitnine ( South Korea) ArangoDB (US), Fluree (US), Blazegraph (US), Memgraph UK), Objectivity (US), GraphBase (Australia), Graph Story (US), Oxford Semantic Tecnologies (UK), and FalkorDB (Israel).

1 INTRODUCTION

1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
- 1.3.1 MARKET SEGMENTATION
- 1.3.2 INCLUSIONS AND EXCLUSIONS
- 1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES

2 RESEARCH METHODOLOGY

2.1 RESEARCH DATA
- 2.1.1 SECONDARY DATA
  - 2.1.1.1 Key data from secondary sources
- 2.1.2 PRIMARY DATA
  - 2.1.2.1 Primary interviews with experts
  - 2.1.2.2 Breakdown of primary interviews
  - 2.1.2.3 Key industry insights
2.2 MARKET SIZE ESTIMATION
- 2.2.1 TOP-DOWN APPROACH
  - 2.2.1.1 Supply-side analysis
- 2.2.2 BOTTOM-UP APPROACH
  - 2.2.2.1 Demand-side analysis
2.3 DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
2.5 RESEARCH LIMITATIONS
2.6 RISK ASSESSMENT

3 EXECUTIVE SUMMARY

4 PREMIUM INSIGHTS

4.1 OPPORTUNITIES FOR KEY PLAYERS IN GRAPH DATABASE MARKET
4.2 GRAPH DATABASE MARKET, BY OFFERING
4.3 GRAPH DATABASE MARKET, BY SERVICE
4.4 GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE
4.5 GRAPH DATABASE MARKET, BY APPLICATION
4.6 GRAPH DATABASE MARKET, BY MODEL TYPE
4.7 GRAPH DATABASE MARKET, BY VERTICAL
4.8 NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING AND MODEL TYPE

5 MARKET OVERVIEW AND INDUSTRY TRENDS

5.1 MARKET DYNAMICS
- 5.1.1 DRIVERS
  - 5.1.1.1 Increasing Gen AI applications
  - 5.1.1.2 Surging need for incorporating real-time big data mining with result visualization
  - 5.1.1.3 Rising demand for solutions that can process low-latency queries
  - 5.1.1.4 Rapid use of virtualization for big data analytics
  - 5.1.1.5 Growing demand for semantic search across unstructured content
- 5.1.2 RESTRAINTS
  - 5.1.2.1 Lack of standardization and programming ease
  - 5.1.2.2 Rapid proliferation of data management technologies
  - 5.1.2.3 High implementation costs
- 5.1.3 OPPORTUNITIES
  - 5.1.3.1 Data unification and rapid proliferation of knowledge graphs
  - 5.1.3.2 Provision of semantic knowledgeable graphs to address complex-scientific research
  - 5.1.3.3 Emphasis on emergence of open knowledge networks
- 5.1.4 CHALLENGES
  - 5.1.4.1 Lack of technical expertise
  - 5.1.4.2 Difficulty in demonstrating benefits of knowledge graphs in single application or use case
5.2 BEST PRACTICES IN GRAPH DATABASE MARKET
- 5.2.1 VALIDATION OF USE CASES
- 5.2.2 AVOIDANCE OF INEFFICIENT TRAVERSAL PATTERNS
- 5.2.3 USAGE OF DATA MODELING
- 5.2.4 ENSURING DATA CONSISTENCY
- 5.2.5 PARTITIONING OF COSMOS DB
- 5.2.6 FOSTERING TEAM EXPERTISE IN GRAPH DATABASE
5.3 EVOLUTION OF GRAPH DATABASE MARKET
5.4 ECOSYSTEM ANALYSIS
5.5 CASE STUDY ANALYSIS
- 5.5.1 NEO4J-POWERED KNOWLEDGE GRAPH HELPED INTUIT PROVIDE REAL-TIME INSIGHTS AND FACILITATE SWIFT RESPONSES TO SECURITY THREATS
- 5.5.2 WESTJET IMPROVED ITS CUSTOMER BOOKING EXPERIENCE BY INTEGRATING NEO4J'S GRAPH TECHNOLOGY
- 5.5.3 NEWDAY IMPROVED FRAUD DETECTION CAPABILITIES WITH TIGERGRAPH CLOUD
- 5.5.4 CYBER RESILIENCE LEADER LEVERAGED TIGERGRAPH TO ELEVATE ITS NEXT-GENERATION CLOUD-BASED CYBERSECURITY SERVICES
- 5.5.5 XBOX CHOSE TIGERGRAPH TO EMPOWER ITS GRAPH ANALYTICS CAPABILITIES
- 5.5.6 DGRAPH'S CUTTING-EDGE DATABASE SOLUTION ENABLED MOONCAMP TO STREAMLINE ITS BACKEND OPERATIONS
- 5.5.7 NEO4J'S GRAPH DATABASE AND APPLICATION PLATFORM HELPED KERBEROS CONTROL COMPLEX LEGAL OBLIGATIONS
- 5.5.8 BLAZEGRAPH HELPED YAHOO7 DRIVE NATIVE REAL-TIME ADVERTISING USING GRAPH QUERIES
- 5.5.9 NEO4J ENABLED ICU'S TEAM TO VISUALIZE AND ANALYZE CONNECTIONS BETWEEN ELEMENTS OF PANAMA PAPERS LEAKS
- 5.5.10 NEO4J'S GRAPH TECHNOLOGY HELPED U.S. ARMY BY TRACKING AND ANALYZING EQUIPMENT MAINTENANCE
- 5.5.11 JAGUAR LAND ROVER ACHIEVED REDUCED INVENTORY COSTS AND HIGHER PROFITABILITY USING TIGERGRAPH'S SOLUTION
- 5.5.12 MACY'S REDUCED CATALOG DATA REFRESH TIME BY SIX-FOLD
- 5.5.13 METAPHACTS AND ONTOTEXT ENABLED GLOBAL PHARMA COMPANY TO BOOST R&D KNOWLEDGE DISCOVERY
5.6 SUPPLY CHAIN ANALYSIS
5.7 INVESTMENT AND FUNDING SCENARIO
5.8 IMPACT OF GENERATIVE AI ON GRAPH DATABASE MARKET
- 5.8.1 USE CASES OF GENERATIVE AI IN GRAPH DATABASE
  - 5.8.1.1 Neo4j LLM Knowledge Graph Builder enabled users to extract nodes and relationships from unstructured text
  - 5.8.1.2 Data2's flagship analytics platform, reView, delivered powerful insights by integrating customer data into Neo4j-backed knowledge graph
  - 5.8.1.3 JPMorgan leveraged LLMs to detect fraudulent activities
  - 5.8.1.4 Mastercard leveraged GenAI capabilities to strengthen its fraud detection system
5.9 TECHNOLOGY ROADMAP OF GRAPH DATABASE MARKET
5.10 REGULATORY LANDSCAPE
- 5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
- 5.10.2 KEY REGULATIONS
  - 5.10.2.1 North America
    - 5.10.2.1.1 SCR 17: Artificial Intelligence Bill (California)
    - 5.10.2.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut)
    - 5.10.2.1.3 National Artificial Intelligence Initiative Act (NAIIA)
    - 5.10.2.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada
    - 5.10.2.1.5 Cybersecurity Maturity Model Certification (CMMC) (USA)
  - 5.10.2.2 Europe
    - 5.10.2.2.1 The European Union (EU) - Artificial Intelligence Act (AIA)
    - 5.10.2.2.2 General Data Protection Regulation (Europe)
  - 5.10.2.3 Asia Pacific
    - 5.10.2.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China)
    - 5.10.2.3.2 National AI Strategy (Singapore)
    - 5.10.2.3.3 Hiroshima AI Process Comprehensive Policy Framework (Japan)
  - 5.10.2.4 Middle East & Africa
    - 5.10.2.4.1 National Strategy for Artificial Intelligence (UAE)
    - 5.10.2.4.2 National Artificial Intelligence Strategy (Qatar)
    - 5.10.2.4.3 AI Ethics Principles and Guidelines (Dubai)
  - 5.10.2.5 Latin America
    - 5.10.2.5.1 The Santiago Declaration (Chile)
    - 5.10.2.5.2 Brazilian Artificial Intelligence Strategy-EBIA
5.11 PATENT ANALYSIS
- 5.11.1 METHODOLOGY
- 5.11.2 LIST OF MAJOR PATENTS
5.12 TECHNOLOGY ANALYSIS
- 5.12.1 KEY TECHNOLOGIES
  - 5.12.1.1 Semantic Web
  - 5.12.1.2 Generative AI and natural language processing
  - 5.12.1.3 Graph RAG
- 5.12.2 COMPLEMENTARY TECHNOLOGIES
  - 5.12.2.1 Cloud computing
  - 5.12.2.2 AI and ML
  - 5.12.2.3 Big data & analytics
  - 5.12.2.4 Graph neural networks
  - 5.12.2.5 Vector databases and full-text search engines
  - 5.12.2.6 Multimodal databases
- 5.12.3 ADJACENT TECHNOLOGIES
  - 5.12.3.1 Digital twin
  - 5.12.3.2 IoT
  - 5.12.3.3 Blockchain
  - 5.12.3.4 Edge computing
5.13 PRICING ANALYSIS
- 5.13.1 AVERAGE SELLING PRICE OF KEY PLAYERS, BY COUNTRY, 2023
- 5.13.2 INDICATIVE PRICING ANALYSIS, BY KEY PLAYER, 2023
5.14 KEY CONFERENCES AND EVENTS, 2024-2025
5.15 PORTER'S FIVE FORCES ANALYSIS
- 5.15.1 THREAT OF NEW ENTRANTS
- 5.15.2 THREAT OF SUBSTITUTES
- 5.15.3 BARGAINING POWER OF SUPPLIERS
- 5.15.4 BARGAINING POWER OF BUYERS
- 5.15.5 INTENSITY OF COMPETITIVE RIVALRY
5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.17 KEY STAKEHOLDERS AND BUYING CRITERIA
- 5.17.1 KEY STAKEHOLDERS IN BUYING PROCESS
- 5.17.2 BUYING CRITERIA

6 GRAPH DATABASE MARKET, BY OFFERING

6.1 INTRODUCTION
- 6.1.1 OFFERING: GRAPH DATABASE MARKET DRIVERS
6.2 SOLUTIONS
- 6.2.1 INCREASING NEED FOR ENHANCING PRODUCTIVITY AND MAINTAINING BUSINESS CONTINUITY TO DRIVE MARKET
- 6.2.2 BY SOLUTION TYPE
  - 6.2.2.1 Graph extensions
  - 6.2.2.2 Graph processing engines
  - 6.2.2.3 Native graph database
  - 6.2.2.4 Knowledge graph engines
- 6.2.3 BY DEPLOYMENT MODE
  - 6.2.3.1 Cloud
  - 6.2.3.2 On-premises
6.3 SERVICES
- 6.3.1 MANAGED SERVICES
  - 6.3.1.1 Specialized skills for maintaining and updating graph database solutions to drive market
- 6.3.2 PROFESSIONAL SERVICES
  - 6.3.2.1 Consulting services
    - 6.3.2.1.1 Integration of graph databases with analytics and virtualization frameworks to boost market
  - 6.3.2.2 Deployment & integration services
    - 6.3.2.2.1 Growing need to overcome system-related issues effectively to drive market
  - 6.3.2.3 Support & maintenance services
    - 6.3.2.3.1 Services provided for upgradation and maintenance of operating ecosystem post-implementation to fuel market growth

7 GRAPH DATABASE MARKET, BY MODEL TYPE

7.1 INTRODUCTION
- 7.1.1 MODEL TYPE: GRAPH DATABASE MARKET DRIVERS
7.2 RESOURCE DESCRIPTION FRAMEWORK
- 7.2.1 NEED FOR INTELLIGENT DATA MANAGEMENT SOLUTIONS TO DRIVE DEMAND FOR GRAPH DATABASE
7.3 PROPERTY GRAPH
- 7.3.1 INCREASING URGE TO FIND RELATIONSHIPS AMONG NUMEROUS ENTITIES TO BOOST MARKET
  - 7.3.1.1 Labeled property graph
  - 7.3.1.2 Typed property graph

8 GRAPH DATABASE MARKET, BY APPLICATION

8.1 INTRODUCTION
- 8.1.1 APPLICATION: GRAPH DATABASE MARKET DRIVERS
8.2 DATA GOVERNANCE & MASTER DATA MANAGEMENT
- 8.2.1 NEED FOR MANAGING, INTEGRATING, AND SECURING COMPLEX DATA RELATIONSHIPS TO DRIVE MARKET
8.3 DATA ANALYTICS & BUSINESS INTELLIGENCE
- 8.3.1 SUPERIOR QUERY PERFORMANCE FOR COMPLEX OPERATIONS TO BOOST MARKET
8.4 KNOWLEDGE & CONTENT MANAGEMENT
- 8.4.1 INTUITIVE AND DYNAMIC WAY OF ORGANIZING, CONNECTING, AND RETRIEVING INFORMATION TO FUEL MARKET GROWTH
8.5 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY
- 8.5.1 PERSONALIZED, INTELLIGENT, AND CONTEXT-AWARE INTERACTIONS TO SUPPORT MARKET GROWTH
8.6 PRODUCT & CONFIGURATION MANAGEMENT
- 8.6.1 VISIBILITY INTO INTERDEPENDENCIES ACROSS TEAMS TO ENSURE TRACEABILITY AND BETTER DECISION-MAKING
8.7 INFRASTRUCTURE & ASSET MANAGEMENT
- 8.7.1 MODELING AND ANALYSIS OF INTRICATE RELATIONSHIPS BETWEEN ASSETS TO DRIVE MARKET
8.8 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT
- 8.8.1 OPTIMIZE PROCESS BY ANALYZING COMPLEX, INTERCONNECTED DATA THROUGH GRAPH DATA SCIENCE
8.9 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING
- 8.9.1 IDENTIFICATION AND ASSESSMENT OF RISKS BY VISUALIZING CONNECTIONS TO BOOST MARKET
8.10 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION
- 8.10.1 GRAPH DATABASES TO IMPROVE SALES EFFECTIVENESS AND CUSTOMER ENGAGEMENT
8.11 OTHER APPLICATIONS

9 GRAPH DATABASE MARKET, BY VERTICAL

9.1 INTRODUCTION
- 9.1.1 VERTICAL: GRAPH DATABASE MARKET DRIVERS
9.2 BANKING, FINANCIAL SERVICES, AND INSURANCE
- 9.2.1 GROWING ADOPTION OF FINANCIAL STANDARDS AND COMPLIANCE WITH REGULATIONS TO DRIVE MARKET
- 9.2.2 CASE STUDY
  - 9.2.2.1 Fraud detection & risk management
    - 9.2.2.1.1 Neo4j-powered system helped BNP Paribas Personal Finance achieve a 20% reduction in fraud
    - 9.2.2.1.2 Zurich Switzerland enhanced fraud investigations with Neo4j
  - 9.2.2.2 Anti-money laundering
    - 9.2.2.2.1 US bank leveraged TigerGraph's graph analytics capabilities to detect intricate money laundering network
    - 9.2.2.2.2 KERBEROS enhanced money laundering capabilities with Neo4j's graph database and Structr application platform
  - 9.2.2.3 Identity & access management
    - 9.2.2.3.1 Ability for mapping and querying intricate relationships to drive market
  - 9.2.2.4 Risk management
    - 9.2.2.4.1 Rising usage of graph database tools and services for enhancing risk intelligence capabilities to aid market growth
    - 9.2.2.4.2 UBS implemented Neo4j's graph database to improve its data lineage and governance
    - 9.2.2.4.3 Marionete integrated its various databases with the Neo4j graph database, enabling it to reduce credit risk and influence charges
  - 9.2.2.5 Data integration & governance
    - 9.2.2.5.1 Optimizing data security and privacy
    - 9.2.2.5.2 Real-time monitoring and audit
  - 9.2.2.6 Know Your Customer (KYC) process
    - 9.2.2.6.1 Neo4j's graph technology helped institutions save time in compliance workflows
  - 9.2.2.7 Operational resilience for bank IT systems
    - 9.2.2.7.1 Stardog's platform allowed for easy navigation through interconnected data, helping organizations identify dependencies and analyze systemic risks
  - 9.2.2.8 Regulatory compliance
    - 9.2.2.8.1 Streamlining regulatory compliance with RDFoc
  - 9.2.2.9 Customer 360° view
    - 9.2.2.9.1 Unified, holistic perspective of each customer by integrating data from multiple sources
  - 9.2.2.10 Market analysis & trend detection
    - 9.2.2.10.1 Graph databases to help gain deeper insights into organizations' complex relationships and enhance customer experiences
  - 9.2.2.11 Policy impact analysis
    - 9.2.2.11.1 Real-time updates to ensure quick adaptability to changing regulations, minimizing disruptions, and maintaining operational efficiency
  - 9.2.2.12 Self-service data and digital asset discovery
    - 9.2.2.12.1 Empowerment of users without technical expertise to independently find, explore, and handle data fosters market growth
  - 9.2.2.13 Customer support
    - 9.2.2.13.1 Quick issue resolution, personalized responses, and customized recommendations to boost market
9.3 RETAIL & ECOMMERCE
- 9.3.1 INCREASING NEED FOR IDENTIFYING CUSTOMER BEHAVIOR IN REAL-TIME TO DRIVE MARKET
- 9.3.2 CASE STUDY
  - 9.3.2.1 Fraud detection in eCommerce
    - 9.3.2.1.1 PayPal leveraged real-time graph databases and graph analysis to combat fraud effectively
  - 9.3.2.2 Dynamic pricing optimization
    - 9.3.2.2.1 Deployment of Neo4j-based system significantly improved efficiency and scalability in Marriott's pricing operations
  - 9.3.2.3 Personalized product recommendations
    - 9.3.2.3.1 Neo4j's graph-based approach allowed Walmart to enhance online shopping experience and maintain competitive edge
    - 9.3.2.3.2 AboutYou transformed personalized shopping with ArangoDB, boosting engagement and efficiency
  - 9.3.2.4 Market basket analysis
    - 9.3.2.4.1 Analyzing relationship between product pricing and consumer behavior to support development of optimized pricing strategies
  - 9.3.2.5 Customer experience enhancement
    - 9.3.2.5.1 Retailer achieved enhanced store operations and improved customer satisfaction with TigerGraph's platform
  - 9.3.2.6 Churn Prediction & Prevention
    - 9.3.2.6.1 Predicting churn helps companies identify customers at risk of leaving
  - 9.3.2.7 Social media influence on buying behavior
    - 9.3.2.7.1 Increasing need for understanding and leveraging dynamics of social media influencing consumer-buying decisions to fuel market growth
  - 9.3.2.8 Product Configuration & Recommendation
    - 9.3.2.8.1 Neo4j's graph database enabled eBay achieve seamless and intelligent product discovery experience
  - 9.3.2.9 Customer Segmentation & Targeting
    - 9.3.2.9.1 Targeted advertising and personalized shopping experiences to help drive sales
  - 9.3.2.10 Customer 360° View
    - 9.3.2.10.1 Tracking of customer's purchase behavior to aid market growth
    - 9.3.2.10.2 Neo4j empowered Hastens to build comprehensive 360-degree view of its data, operations, customers, and partners
  - 9.3.2.11 Review & reputation management
    - 9.3.2.11.1 To enhance and manage customer review to protect reputation
  - 9.3.2.12 Customer Support
    - 9.3.2.12.1 To improved customer satisfaction, faster response times, and stronger customer loyalty
9.4 TELECOM & TECHNOLOGY
- 9.4.1 SURGING DEMAND FOR IMPROVED SERVICES TO DRIVE MARKET
- 9.4.2 CASE STUDY
  - 9.4.2.1 Network optimization & management
    - 9.4.2.1.1 Australia's leading carrier enhanced network monitoring and security with ArangoDB
  - 9.4.2.2 Data integration & governance
    - 9.4.2.2.1 D&B achieved significant revenue growth and expanded its customer base using Neo4j's graph technology
  - 9.4.2.3 IT asset management
    - 9.4.2.3.1 Orange leveraged ArangoDB to build digital twin platform for enhanced process optimization
  - 9.4.2.4 Network security analysis
    - 9.4.2.4.1 Zeta Global chose Amazon Neptune for its scalability, elasticity, and cost-effectiveness
  - 9.4.2.5 IoT device management & connectivity
    - 9.4.2.5.1 BT Group leveraged Neo4j to deliver lightning-fast inventory management and streamline operations
    - 9.4.2.5.2 Amazon Neptune's capabilities empowered telecom & IT sectors to achieve enhanced device orchestration and seamless integration of IoT data
  - 9.4.2.6 Self-service data & digital asset discovery
    - 9.4.2.6.1 Optimizing telecom operations with self-service data and digital asset discovery
  - 9.4.2.7 Identity & access management
    - 9.4.2.7.1 Interconnected data model helped Telenor Norway eliminate performance bottlenecks and deliver faster insights
    - 9.4.2.7.2 Enhanced identity management and recommendations with TigerGraph
  - 9.4.2.8 Metadata enrichment
    - 9.4.2.8.1 Enhancing document findability with metadata enrichment at Cisco
  - 9.4.2.9 Service incident management
    - 9.4.2.9.1 Proactive incident management with Neo4j-powered intelligent network analysis tool
9.5 HEALTHCARE, LIFE SCIENCES, AND PHARMACEUTICALS
- 9.5.1 NEED FOR IMPROVED PATIENT-CENTRIC EXPERIENCE AND REAL-TIME TREATMENT TO DRIVE MARKET
- 9.5.2 CASE STUDY
  - 9.5.2.1 Drug discovery & development
    - 9.5.2.1.1 Novartis harnessed cutting-edge biological insights for drug discovery
    - 9.5.2.1.2 Revolutionizing biodiversity insights with graph-powered knowledge mapping
  - 9.5.2.2 Clinical trial management
    - 9.5.2.2.1 Neo4j's knowledge graph-based application helped Novo Nordisk achieve end-to-end consistency and increased automation
  - 9.5.2.3 Medical claims processing
    - 9.5.2.3.1 UnitedHealth improved medical claim processing with graph databases
  - 9.5.2.4 Clinical intelligence
    - 9.5.2.4.1 UnitedHealth Group deployed graph database to enhance patient care
    - 9.5.2.4.2 Dooloo turned to Neo4j's Graph Data Platform for delivering personalized, data-driven insights
  - 9.5.2.5 Healthcare network provider analysis
    - 9.5.2.5.1 Boston Scientific utilized Neo4j's Graph Data Science Library to simplify complex medical supply chain analysis
    - 9.5.2.5.2 Amgen enhanced data analysis and scalability with TigerGraph for healthcare insights
  - 9.5.2.6 Customer support
    - 9.5.2.6.1 Exact Sciences enhanced customer engagement with implementation of Doctor-and-Product 360 solution powered by TigerGraph
    - 9.5.2.6.2 Optimizing healthcare customer support with Graph RAG-powered chatbots
  - 9.5.2.7 Patient journey & care pathway analysis
    - 9.5.2.7.1 Neo4j's scalable and interconnected data model empowered Care-for-Rare to transform vast, siloed datasets into actionable medical insights
  - 9.5.2.8 Self-service data & digital asset discovery
    - 9.5.2.8.1 Stardog-powered enterprise knowledge graph enabled Boehringer Ingelheim to address its challenge of siloed research data
9.6 GOVERNMENT & PUBLIC SECTOR
- 9.6.1 RISING NEED FOR ENHANCED DATA SECURITY AND ADVANCED INTELLIGENCE TO DRIVE MARKET
- 9.6.2 CASE STUDY
  - 9.6.2.1 Government service optimization
    - 9.6.2.1.1 Empowering government agencies with Stardog Voicebox for seamless data insights and enhanced decision-making
  - 9.6.2.2 Legislative & regulatory analysis
    - 9.6.2.2.1 Streamlining legislative and regulatory analysis with graph databases for enhanced compliance and decision-making
  - 9.6.2.3 Crisis management& disaster response planning
    - 9.6.2.3.1 Strengthening cybersecurity with graph databases for proactive threat detection and risk management
  - 9.6.2.4 Environmental impact analysis & ESG
    - 9.6.2.4.1 NASA leveraged Stardog's Enterprise Knowledge Platform, enabling seamless integration and analysis
  - 9.6.2.5 Social network analysis for security and law enforcement
    - 9.6.2.5.1 Global financial institution leveraged Neo4j and Linkurious Enterprise (LE) to enhance fraud detection
  - 9.6.2.6 Policy impact analysis
    - 9.6.2.6.1 Transforming information access at IDB with knowledge graphs
  - 9.6.2.7 Knowledge management
    - 9.6.2.7.1 Neo4j's graph database helped NASA leverage historical insights to reduce project timelines and prevent disasters
  - 9.6.2.8 Data integration & governance
    - 9.6.2.8.1 Transforming product lifecycle management with graph technology
9.7 MANUFACTURING & AUTOMOTIVE
- 9.7.1 GROWING NEED FOR EXTENDING FACTORY EQUIPMENT LIFESPAN AND REDUCING PRODUCTION RISK DELAYS TO BOOST GROWTH
- 9.7.2 CASE STUDY
  - 9.7.2.1 Equipment management & predictive maintenance
    - 9.7.2.1.1 Leveraging graph databases for flexible and robust operations
  - 9.7.2.2 Product lifecycle management
    - 9.7.2.2.1 Japanese automotive manufacturer optimized product life cycle and validation with Neo4j-powered knowledge graph
  - 9.7.2.3 Manufacturing process optimization
    - 9.7.2.3.1 Optimizing manufacturing processes with Stardog Voicebox and Databricks for enhanced quality and efficiency
    - 9.7.2.3.2 Ford enhanced manufacturing efficiency with TigerGraph
  - 9.7.2.4 Enhanced vehicle safety and reliability
    - 9.7.2.4.1 Increase vehicle safety with advanced technologies and graph databases
  - 9.7.2.5 Optimization of industrial processes
    - 9.7.2.5.1 Enhancing smart manufacturing with Siemens' knowledge graph and AI-driven automation
    - 9.7.2.5.2 Optimizing automotive pricing and processes with Neo4j and AWS
  - 9.7.2.6 Root cause analysis
    - 9.7.2.6.1 Leveraging knowledge graphs for transparent and effective root cause analysis
  - 9.7.2.7 Inventory management & demand forecasting
    - 9.7.2.7.1 Optimizing Inventory management with dynamic stock calculation and cost analysis
  - 9.7.2.8 Service incident management
    - 9.7.2.8.1 Improving service incident management with graph databases in manufacturing and automotive
  - 9.7.2.9 Staff & resource allocation
    - 9.7.2.9.1 Enhancing resource and staff allocation efficiency using graph databases
  - 9.7.2.10 Product configuration & recommendation
    - 9.7.2.10.1 Cox Automotive built identity graph using Amazon Neptune to connect and analyze large datasets of shopper information
9.8 MEDIA & ENTERTAINMENT
- 9.8.1 DEMAND FOR MODELING-USER PREFERENCES AND CONTENT INTERACTIONS TO FOSTER MARKET GROWTH
- 9.8.2 CASE STUDY
  - 9.8.2.1 Content recommendation & personalization
    - 9.8.2.1.1 Graph databases enable media companies to provide highly accurate content recommendations and personalized experiences
    - 9.8.2.1.2 Kickdynamic adopted TigerGraph on AWS Cloud to power its recommendation engine
    - 9.8.2.1.3 Musimap adopted Neo4j graph database to offer personalized music recommendations
  - 9.8.2.2 Social media influence analysis
    - 9.8.2.2.1 Myntelligence optimized social media campaigns with TigerGraph's real-time analytics
    - 9.8.2.2.2 TigerGraph's advanced analytics enable OpenCorporates to support complex investigative queries with real-time response times
  - 9.8.2.3 Content recommendation system
    - 9.8.2.3.1 IppenDigital's adoption of TigerGraph's graph database technology helped deliver hyper-personalized content recommendations
    - 9.8.2.3.2 Netflix leveraged graph databases for personalization and scalability
  - 9.8.2.4 User engagement analysis
    - 9.8.2.4.1 Enabling enterprises to capture and dissect intricate associations among users
    - 9.8.2.4.2 Graph technology powered personalized smart home automation for Xfinity
  - 9.8.2.5 Copyright and licensing management
    - 9.8.2.5.1 Enhancing license and copyright management in media & entertainment industry through graph database technology
  - 9.8.2.6 Knowledge management
    - 9.8.2.6.1 Graph technology to enhance collaboration and accelerate decision-making
  - 9.8.2.7 Audience segmentation and targeting
    - 9.8.2.7.1 Optimizing audience segmentation and targeting for maximum impact
  - 9.8.2.8 Self-service data and digital asset discovery
    - 9.8.2.8.1 Consistent metadata management, robust security, user training, and scalability required to handle growing volume of assets effectively
9.9 ENERGY & UTILITIES
- 9.9.1 SURGING DEMAND FOR DECREASING OPERATIONAL RISKS AND COSTS TO DRIVE MARKET
- 9.9.2 CASE STUDY
  - 9.9.2.1 Smart grid management
    - 9.9.2.1.1 Adoption of graph database to manage complex relationships and interconnected data
  - 9.9.2.2 Energy trading optimization
    - 9.9.2.2.1 Unlocking efficient energy trading with graph database technology
  - 9.9.2.3 Renewable energy integration & optimization
    - 9.9.2.3.1 Graph databases to enhance visibility into entire energy ecosystem
  - 9.9.2.4 Public Infrastructure Management
    - 9.9.2.4.1 Enhancing public infrastructure management with graph databases
  - 9.9.2.5 Customer Engagement And Billing
    - 9.9.2.5.1 Ease billing process to improve customer satisfaction
  - 9.9.2.6 Service incident management
    - 9.9.2.6.1 Enxchange transformed energy grid management with graph-based digital twins for real-time insights and cost savings
  - 9.9.2.7 Environmental impact analysis and ESG
    - 9.9.2.7.1 Optimizing energy sustainability and environmental impact with graph databases
    - 9.9.2.7.2 Integration of advanced technologies to enhance data management and insights
  - 9.9.2.8 Railway asset management
    - 9.9.2.8.1 Customized knowledge graphs enable smarter decision-making, predictive maintenance, and cost-effective operations
  - 9.9.2.9 Staff and resource allocation
    - 9.9.2.9.1 Optimizing staff and resource allocation for sustainable energy operations
9.10 TRAVEL & HOSPITALITY
- 9.10.1 FOCUS ON FOSTERING TRAVEL PLANS FOR BETTER CUSTOMER EXPERIENCES TO DRIVE MARKET EXPANSION
- 9.10.2 CASE STUDY
  - 9.10.2.1 Personalized travel recommendations
    - 9.10.2.1.1 Revolutionizing personalized travel recommendations with graph databases
  - 9.10.2.2 Dynamic pricing optimization
    - 9.10.2.2.1 Transforming dynamic price management with graph databases
  - 9.10.2.3 Customer journey mapping
    - 9.10.2.3.1 Customer journey mapping to give personalized recommendations
  - 9.10.2.4 Booking and reservation management
    - 9.10.2.4.1 Graph databases ensure seamless customer experiences and efficient operations
  - 9.10.2.5 Customer experience management
    - 9.10.2.5.1 Transforming customer experience with unified data and actionable insights
  - 9.10.2.6 Product configuration and recommendation
    - 9.10.2.6.1 Dynamic product configuration and personalized recommendations in travel and hospitality
9.11 TRANSPORTATION & LOGISTICS
- 9.11.1 RISING NEED FOR GAINING COMPLETE AND REAL-TIME VISIBILITY TO DRIVE MARKET
- 9.11.2 TRANSPORT FOR LONDON (TFL) REDUCED CONGESTION BY 10% USING DIGITAL TWIN POWERED BY NEO4J
- 9.11.3 USE CASES
  - 9.11.3.1 Route optimization and fleet management
    - 9.11.3.1.1 Careem achieved enhanced fraud detection with AWS
    - 9.11.3.1.2 Optimizing delivery routes and scaling logistics with precision data
  - 9.11.3.2 Supply chain management
    - 9.11.3.2.1 Transforming supply chains with Google Cloud and Neo4j
  - 9.11.3.3 Asset tracking and management
    - 9.11.3.3.1 Graph databases to model intricate relationships and dependencies between assets, locations, and stakeholders
  - 9.11.3.4 Equipment maintenance and predictive maintenance
    - 9.11.3.4.1 Optimizing equipment maintenance with predictive insights powered by graph databases
  - 9.11.3.5 Supply chain management
    - 9.11.3.5.1 Revolutionizing supply chain visibility through real-time digital twin solutions
  - 9.11.3.6 Vendor and supplier analysis
    - 9.11.3.6.1 Graph database to enable comprehensive view of supply chain
  - 9.11.3.7 Operational efficiency & decision-making
    - 9.11.3.7.1 Optimizing delivery routes and scaling logistics with precision data
9.12 OTHER VERTICALS

10 GRAPH DATABASE MARKET, BY REGION

10.1 INTRODUCTION
10.2 NORTH AMERICA
- 10.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK
- 10.2.2 US
  - 10.2.2.1 Increasing use of graph databases in medical science and political campaigns to foster market growth
- 10.2.3 CANADA
  - 10.2.3.1 Stringent data regulation and extensive applications of graph databases in research to drive growth
10.3 EUROPE
- 10.3.1 EUROPE: MACROECONOMIC OUTLOOK
- 10.3.2 UK
  - 10.3.2.1 Government initiatives and healthcare-focused projects to drive market growth
- 10.3.3 ITALY
  - 10.3.3.1 Increasing use of graph databases in financial sector to accelerate market growth
- 10.3.4 GERMANY
  - 10.3.4.1 Increasing focus on enhancing interoperability to boost market
- 10.3.5 FRANCE
  - 10.3.5.1 Graph databases to drive innovation, enabling data-driven decision-making across key industries
- 10.3.6 SPAIN
  - 10.3.6.1 Government initiatives and geographical research to bolster market growth
- 10.3.7 REST OF EUROPE
10.4 ASIA PACIFIC
- 10.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK
- 10.4.2 CHINA
  - 10.4.2.1 Major players and use of graph databases in telecom fueling market growth
- 10.4.3 INDIA
  - 10.4.3.1 Increasing focus on digital transformation to support market growth
- 10.4.4 JAPAN
  - 10.4.4.1 Integration of knowledge graphs with generative AI to fuel market growth
- 10.4.5 AUSTRALIA & NEW ZEALAND
  - 10.4.5.1 Strategic initiatives and presence of major players to drive adoption of graph databases
- 10.4.6 SOUTH KOREA
  - 10.4.6.1 Increasing applications of graph databases in fraud detection, network analysis, and AI-powered innovations to aid market growth
- 10.4.7 REST OF ASIA PACIFIC
10.5 MIDDLE EAST & AFRICA
- 10.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
- 10.5.2 MIDDLE EAST
  - 10.5.2.1 KSA
    - 10.5.2.1.1 Digitalization initiatives to drive market growth
  - 10.5.2.2 UAE
    - 10.5.2.2.1 Increasing applications of graph databases for environmental insights and research collaboration to drive market growth
  - 10.5.2.3 Qatar
    - 10.5.2.3.1 Rising demand for advanced data analytics and interconnected data management solutions to drive market growth
  - 10.5.2.4 Turkey
    - 10.5.2.4.1 Increasing adoption of graph technologies to address challenges in data analytics, decision-making, and innovation
  - 10.5.2.5 Rest of Middle East
- 10.5.3 AFRICA
  - 10.5.3.1 Strategic investments in cloud and AI technologies to drive adoption of graph databases
10.6 LATIN AMERICA
- 10.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK
- 10.6.2 BRAZIL
  - 10.6.2.1 Growing adoption of graph databases across industries and key collaborative initiatives to drive market
- 10.6.3 ARGENTINA
  - 10.6.3.1 Advancements in cloud infrastructure and AI to further enable scalable deployment of graph databases
- 10.6.4 MEXICO
  - 10.6.4.1 Increasing investments in cloud infrastructure to accelerate adoption of graph databases
- 10.6.5 REST OF LATIN AMERICA

11 COMPETITIVE LANDSCAPE

11.1 INTRODUCTION
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
11.3 MARKET SHARE ANALYSIS, 2024
- 11.3.1 MARKET RANKING ANALYSIS
11.4 REVENUE ANALYSIS, 2019-2023
11.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
- 11.5.1 STARS
- 11.5.2 EMERGING LEADERS
- 11.5.3 PERVASIVE PLAYERS
- 11.5.4 PARTICIPANTS
- 11.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
  - 11.5.5.1 Company footprint
  - 11.5.5.2 Offering footprint
  - 11.5.5.3 Model type footprint
  - 11.5.5.4 Application footprint
  - 11.5.5.5 Vertical footprint
  - 11.5.5.6 Region footprint
11.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
- 11.6.1 PROGRESSIVE COMPANIES
- 11.6.2 RESPONSIVE COMPANIES
- 11.6.3 DYNAMIC COMPANIES
- 11.6.4 STARTING BLOCKS
- 11.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
  - 11.6.5.1 Detailed list of key startups/SMEs
  - 11.6.5.2 Competitive benchmarking of key startups/SMEs
11.7 COMPETITIVE SCENARIO
- 11.7.1 PRODUCT LAUNCHES AND ENHANCEMENTS
- 11.7.2 DEALS
11.8 BRAND COMPARISON
11.9 COMPANY VALUATION AND FINANCIAL METRICS

12 COMPANY PROFILES

12.1 KEY PLAYERS
- 12.1.1 NEO4J
  - 12.1.1.1 Business overview
  - 12.1.1.2 Products/Solutions/Services offered
  - 12.1.1.3 Recent developments
    - 12.1.1.3.1 Product launches and enhancements
    - 12.1.1.3.2 Deals
  - 12.1.1.4 MnM view
    - 12.1.1.4.1 Key strengths
    - 12.1.1.4.2 Strategic choices
    - 12.1.1.4.3 Weaknesses and competitive threats
- 12.1.2 AMAZON WEB SERVICES, INC
  - 12.1.2.1 Business overview
  - 12.1.2.2 Products/Solutions/Services offered
  - 12.1.2.3 Recent developments
    - 12.1.2.3.1 Product launches and enhancements
    - 12.1.2.3.2 Deals
  - 12.1.2.4 MnM view
    - 12.1.2.4.1 Key strengths
    - 12.1.2.4.2 Strategic choices
    - 12.1.2.4.3 Weaknesses and competitive threats
- 12.1.3 TIGERGRAPH
  - 12.1.3.1 Business overview
  - 12.1.3.2 Products/Solutions/Services offered
  - 12.1.3.3 Recent developments
    - 12.1.3.3.1 Product launches and enhancements
    - 12.1.3.3.2 Deals
  - 12.1.3.4 MnM view
    - 12.1.3.4.1 Key strengths
    - 12.1.3.4.2 Strategic choices
    - 12.1.3.4.3 Weaknesses and competitive threats
- 12.1.4 RELATIONALAI
  - 12.1.4.1 Business overview
  - 12.1.4.2 Products/Solutions/Services offered
  - 12.1.4.3 Recent developments
    - 12.1.4.3.1 Product launches and enhancements
  - 12.1.4.4 MnM view
    - 12.1.4.4.1 Key strengths
    - 12.1.4.4.2 Strategic choices
    - 12.1.4.4.3 Weaknesses and competitive threats
- 12.1.5 GRAPHWISE
  - 12.1.5.1 Business overview
  - 12.1.5.2 Products/Solutions/Services offered
  - 12.1.5.3 Recent developments
    - 12.1.5.3.1 Product launches and enhancements
  - 12.1.5.4 MnM view
    - 12.1.5.4.1 Key strengths
    - 12.1.5.4.2 Strategic choices
    - 12.1.5.4.3 Weaknesses and competitive threats
- 12.1.6 IBM CORPORATION
  - 12.1.6.1 Business overview
  - 12.1.6.2 Products/Solutions/Services offered
  - 12.1.6.3 Recent developments
    - 12.1.6.3.1 Deals
- 12.1.7 MICROSOFT CORPORATION, INC.
  - 12.1.7.1 Business overview
  - 12.1.7.2 Products/Solutions/Services offered
  - 12.1.7.3 Recent developments
    - 12.1.7.3.1 Deals
- 12.1.8 ONTOTEXT
  - 12.1.8.1 Business overview
  - 12.1.8.2 Products/Solutions/Services offered
  - 12.1.8.3 Recent developments
    - 12.1.8.3.1 Product launches and enhancements
    - 12.1.8.3.2 Deals
- 12.1.9 STAR DOG
  - 12.1.9.1 Business overview
  - 12.1.9.2 Products/Solutions/Services offered
  - 12.1.9.3 Recent developments
    - 12.1.9.3.1 Product launches and enhancements
    - 12.1.9.3.2 Deals
- 12.1.10 ALTAIR
  - 12.1.10.1 Business overview
  - 12.1.10.2 Products/Solutions/Services offered
  - 12.1.10.3 Recent developments
    - 12.1.10.3.1 Product launches and enhancements
    - 12.1.10.3.2 Deals
- 12.1.11 ORACLE CORPORATION
  - 12.1.11.1 Business overview
  - 12.1.11.2 Products/Solutions/Services offered
  - 12.1.11.3 Recent developments
    - 12.1.11.3.1 Product launches and enhancements
- 12.1.12 PROGRESS SOFTWARE
  - 12.1.12.1 Business overview
  - 12.1.12.2 Products/Solutions/Services offered
  - 12.1.12.3 Recent developments
    - 12.1.12.3.1 Deals
- 12.1.13 FRANZ INC.
  - 12.1.13.1 Business overview
  - 12.1.13.2 Products/Solutions/Services offered
  - 12.1.13.3 Recent developments
    - 12.1.13.3.1 Product launches and enhancements
- 12.1.14 DATASTAX
  - 12.1.14.1 Business overview
  - 12.1.14.2 Products/Solutions/Services offered
  - 12.1.14.3 Recent developments
    - 12.1.14.3.1 Product launches and enhancements
    - 12.1.14.3.2 Deals
- 12.1.15 DGRAPH LABS
- 12.1.16 OPENLINK SOFTWARE
12.2 STARTUPS/SMES
- 12.2.1 OXFORD SEMANTIC TECHNOLOGIES
- 12.2.2 BITNINE
- 12.2.3 ARANGODB
- 12.2.4 FLUREE
- 12.2.5 BLAZEGRAPH
- 12.2.6 MEMGRAPH
- 12.2.7 OBJECTIVITY INC
- 12.2.8 GRAPHBASE
- 12.2.9 GRAPH STORY
- 12.2.10 FALKORDB

13 ADJACENT AND RELATED MARKETS

13.1 INTRODUCTION
13.2 MARKET DEFINITION
13.3 CLOUD DATABASE AND DBAAS MARKET
- 13.3.1 MARKET DEFINITION
- 13.3.2 MARKET OVERVIEW
  - 13.3.2.1 Cloud database and DBaaS market, by component
  - 13.3.2.2 Cloud database and DBaaS market, by deployment model
  - 13.3.2.3 Cloud database and DBaaS market, by organization size
  - 13.3.2.4 Cloud database and DBaaS market, by vertical
  - 13.3.2.5 Cloud database and DBaaS market, by region
13.4 VECTOR DATABASE MARKET
- 13.4.1 MARKET DEFINITION
- 13.4.2 VECTOR DATABASE MARKET, BY OFFERING
- 13.4.3 VECTOR DATABASE MARKET, BY TECHNOLOGY
- 13.4.4 VECTOR DATABASE MARKET, BY VERTICAL
- 13.4.5 VECTOR DATABASE MARKET, BY REGION

14 APPENDIX

14.1 DISCUSSION GUIDE
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
14.3 CUSTOMIZATION OPTIONS
14.4 RELATED REPORTS
14.5 AUTHOR DETAILS

简介目录图表

List of Tables

TABLE 1 USD EXCHANGE RATE, 2021-2023
TABLE 2 PRIMARY INTERVIEWS WITH EXPERTS
TABLE 3 RISK ASSESSMENT
TABLE 4 GRAPH DATABASE MARKET: ECOSYSTEM
TABLE 5 TECHNOLOGY ROADMAP OF GRAPH DATABASE MARKET, 2024-2030
TABLE 6 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 7 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 8 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 9 REST OF THE WORLD: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 10 GRAPH DATABASE MARKET: KEY PATENTS, 2014-2022
TABLE 11 AVERAGE SELLING PRICES OF GRAPH DATABASE SOLUTIONS, BY REGION, 2023
TABLE 12 INDICATIVE PRICING ANALYSIS OF KEY PLAYERS, 2023 (USD)
TABLE 13 GRAPH DATABASE MARKET: CONFERENCES AND EVENTS, 2024-2025
TABLE 14 IMPACT OF PORTER'S FIVE FORCES ON GRAPH DATABASE MARKET
TABLE 15 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS
TABLE 16 KEY BUYING CRITERIA FOR TOP THREE INDUSTRIES
TABLE 17 GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 18 GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 19 GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 20 GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 21 SOLUTIONS: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 22 SOLUTIONS: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 23 GRAPH EXTENSIONS: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 24 GRAPH EXTENSIONS: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 25 GRAPH PROCESSING ENGINES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 26 GRAPH PROCESSING ENGINES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 27 NATIVE GRAPH DATABASE: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 28 NATIVE GRAPH DATABASE: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 29 KNOWLEDGE GRAPH ENGINES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 30 KNOWLEDGE GRAPH ENGINES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 31 GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 32 GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 33 CLOUD: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 34 CLOUD: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 35 ON-PREMISES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 36 ON-PREMISES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 37 GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 38 GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 39 SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 40 SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 41 MANAGED SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 42 MANAGED SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 43 PROFESSIONAL SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 44 PROFESSIONAL SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 45 GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 46 GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 47 CONSULTING SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 48 CONSULTING SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 49 DEPLOYMENT & INTEGRATION SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 50 DEPLOYMENT & INTEGRATION SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 51 SUPPORT & MAINTENANCE SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 52 SUPPORT & MAINTENANCE SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 53 GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 54 GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 55 RESOURCE DESCRIPTION FRAMEWORK: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 56 RESOURCE DESCRIPTION FRAMEWORK: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 57 PROPERTY GRAPH: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 58 PROPERTY GRAPH: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 59 GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 60 GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 61 DATA GOVERNANCE & MASTER DATA MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 62 DATA GOVERNANCE & MASTER DATA MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 63 DATA ANALYTICS & BUSINESS INTELLIGENCE: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 64 DATA ANALYTICS & BUSINESS INTELLIGENCE: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 65 KNOWLEDGE & CONTENT MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 66 KNOWLEDGE & CONTENT MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 67 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 68 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 69 PRODUCT & CONFIGURATION MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 70 PRODUCT & CONFIGURATION MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 71 INFRASTRUCTURE & ASSET MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 72 INFRASTRUCTURE & ASSET MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 73 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 74 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 75 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 76 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 77 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 78 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 79 OTHER APPLICATIONS: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 80 OTHER APPLICATIONS: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 81 GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 82 GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 83 BANKING, FINANCIAL SERVICES, AND INSURANCE: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 84 BANKING, FINANCIAL SERVICES, AND INSURANCE: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 85 RETAIL & ECOMMERCE: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 86 RETAIL & ECOMMERCE: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 87 TELECOM & TECHNOLOGY: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 88 TELECOM & IT: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 89 HEALTHCARE, LIFESCIENCES, AND PHARMACEUTICALS: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 90 HEALTHCARE, LIFESCIENCES, AND PHARMACEUTICALS: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 91 GOVERNMENT & PUBLIC SECTOR: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 92 GOVERNMENT & PUBLIC SECTOR: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 93 MANUFACTURING & AUTOMOTIVE: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 94 MANUFACTURING & AUTOMOTIVE: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 95 MEDIA & ENTERTAINMENT: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 96 MEDIA & ENTERTAINMENT: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 97 ENERGY & UTILITIES: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 98 ENERGY & UTILITIES: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 99 TRAVEL & HOSPITALITY: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 100 TRAVEL & HOSPITALITY: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 101 TRANSPORTATION & LOGISTICS: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 102 TRANSPORTATION & LOGISTICS: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 103 OTHER VERTICALS: GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 104 OTHER VERTICALS: GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 105 GRAPH DATABASE MARKET, BY REGION, 2019-2023 (USD MILLION)
TABLE 106 GRAPH DATABASE MARKET, BY REGION, 2024-2030 (USD MILLION)
TABLE 107 NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 108 NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 109 NORTH AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 110 NORTH AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 111 NORTH AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 112 NORTH AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 113 NORTH AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 114 NORTH AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 115 NORTH AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 116 NORTH AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 117 NORTH AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 118 NORTH AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 119 NORTH AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 120 NORTH AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 121 NORTH AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 122 NORTH AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 123 NORTH AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2019-2023 (USD MILLION)
TABLE 124 NORTH AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2024-2030 (USD MILLION)
TABLE 125 US: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 126 US: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 127 US: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 128 US: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 129 US: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 130 US: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 131 US: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 132 US: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 133 US: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 134 US: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 135 US: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 136 US: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 137 US: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 138 US: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 139 US: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 140 US: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 141 EUROPE: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 142 EUROPE: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 143 EUROPE: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 144 EUROPE: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 145 EUROPE: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 146 EUROPE: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 147 EUROPE: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 148 EUROPE: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 149 EUROPE: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 150 EUROPE: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 151 EUROPE: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 152 EUROPE: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 153 EUROPE: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 154 EUROPE: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 155 EUROPE: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 156 EUROPE: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 157 EUROPE: GRAPH DATABASE MARKET, BY COUNTRY, 2019-2023 (USD MILLION)
TABLE 158 EUROPE: GRAPH DATABASE MARKET, BY COUNTRY, 2024-2030 (USD MILLION)
TABLE 159 UK: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 160 UK: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 161 UK: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 162 UK: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 163 UK: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 164 UK: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 165 UK: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 166 UK: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 167 UK: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 168 UK: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 169 UK: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 170 UK: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 171 UK: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 172 UK: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 173 UK: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 174 UK: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 175 ITALY: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 176 ITALY: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 177 ITALY: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 178 ITALY: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 179 ITALY: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 180 ITALY: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 181 ITALY: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 182 ITALY: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 183 ITALY: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 184 ITALY: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 185 ITALY: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 186 ITALY: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 187 ITALY: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 188 ITALY: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 189 ITALY: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 190 ITALY: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 191 ASIA PACIFIC: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 192 ASIA PACIFIC: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 193 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 194 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 195 ASIA PACIFIC: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 196 ASIA PACIFIC: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 197 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 198 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 199 ASIA PACIFIC: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 200 ASIA PACIFIC: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 201 ASIA PACIFIC: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 202 ASIA PACIFIC: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 203 ASIA PACIFIC: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 204 ASIA PACIFIC: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 205 ASIA PACIFIC: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 206 ASIA PACIFIC: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 207 ASIA PACIFIC: GRAPH DATABASE MARKET, BY COUNTRY, 2019-2023 (USD MILLION)
TABLE 208 ASIA PACIFIC: GRAPH DATABASE MARKET, BY COUNTRY, 2024-2030 (USD MILLION)
TABLE 209 CHINA: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 210 CHINA: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 211 CHINA: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 212 CHINA: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 213 CHINA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 214 CHINA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 215 CHINA: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 216 CHINA: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 217 CHINA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 218 CHINA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 219 CHINA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 220 CHINA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 221 CHINA: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 222 CHINA: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 223 CHINA: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 224 CHINA: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 225 INDIA: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 226 INDIA: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 227 INDIA: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 228 INDIA: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 229 INDIA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 230 INDIA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 231 INDIA: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 232 INDIA: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 233 INDIA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 234 INDIA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 235 INDIA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 236 INDIA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 237 INDIA: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 238 INDIA: GRAPH DATABASE MARKET, BY APPLICATIONS, 2024-2030 (USD MILLION)
TABLE 239 INDIA: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 240 INDIA: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 241 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 242 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 243 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 244 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 245 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 246 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 247 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 248 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 249 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 250 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 251 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 252 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 253 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 254 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 255 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 256 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 257 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY COUNTRY, 2019-2023 (USD MILLION)
TABLE 258 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY COUNTRY, 2024-2030 (USD MILLION)
TABLE 259 MIDDLE EAST: GRAPH DATABASE MARKET, BY COUNTRY, 2019-2023 (USD MILLION)
TABLE 260 MIDDLE EAST: GRAPH DATABASE MARKET, BY COUNTRY, 2024-2030 (USD MILLION)
TABLE 261 KSA: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 262 KSA: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 263 KSA: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 264 KSA: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 265 KSA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 266 KSA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 267 KSA: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 268 KSA: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 269 KSA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 270 KSA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 271 KSA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 272 KSA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 273 KSA: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 274 KSA: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 275 KSA: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 276 KSA: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 277 LATIN AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 278 LATIN AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 279 LATIN AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 280 LATIN AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 281 LATIN AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 282 LATIN AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 283 LATIN AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 284 LATIN AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 285 LATIN AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 286 LATIN AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 287 LATIN AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 288 LATIN AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 289 LATIN AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 290 LATIN AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 291 LATIN AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 292 LATIN AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 293 LATIN AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2019-2023 (USD MILLION)
TABLE 294 LATIN AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2024-2030 (USD MILLION)
TABLE 295 BRAZIL: GRAPH DATABASE MARKET, BY OFFERING, 2019-2023 (USD MILLION)
TABLE 296 BRAZIL: GRAPH DATABASE MARKET, BY OFFERING, 2024-2030 (USD MILLION)
TABLE 297 BRAZIL: GRAPH DATABASE MARKET, BY SOLUTION, 2019-2023 (USD MILLION)
TABLE 298 BRAZIL: GRAPH DATABASE MARKET, BY SOLUTION, 2024-2030 (USD MILLION)
TABLE 299 BRAZIL: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019-2023 (USD MILLION)
TABLE 300 BRAZIL: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024-2030 (USD MILLION)
TABLE 301 BRAZIL: GRAPH DATABASE MARKET, BY SERVICE, 2019-2023 (USD MILLION)
TABLE 302 BRAZIL: GRAPH DATABASE MARKET, BY SERVICE, 2024-2030 (USD MILLION)
TABLE 303 BRAZIL: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019-2023 (USD MILLION)
TABLE 304 BRAZIL: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024-2030 (USD MILLION)
TABLE 305 BRAZIL: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019-2023 (USD MILLION)
TABLE 306 BRAZIL: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024-2030 (USD MILLION)
TABLE 307 BRAZIL: GRAPH DATABASE MARKET, BY APPLICATION, 2019-2023 (USD MILLION)
TABLE 308 BRAZIL: GRAPH DATABASE MARKET, BY APPLICATION, 2024-2030 (USD MILLION)
TABLE 309 BRAZIL: GRAPH DATABASE MARKET, BY VERTICAL, 2019-2023 (USD MILLION)
TABLE 310 BRAZIL: GRAPH DATABASE MARKET, BY VERTICAL, 2024-2030 (USD MILLION)
TABLE 311 OVERVIEW OF STRATEGIES DEPLOYED BY KEY GRAPH DATABASE MARKET PLAYERS, 2021-2024
TABLE 312 GRAPH DATABASE MARKET: DEGREE OF COMPETITION
TABLE 313 GRAPH DATABASE MARKET: OFFERING FOOTPRINT
TABLE 314 GRAPH DATABASE MARKET: MODEL TYPE FOOTPRINT
TABLE 315 GRAPH DATABASE MARKET: APPLICATION FOOTPRINT
TABLE 316 GRAPH DATABASE MARKET: VERTICAL FOOTPRINT
TABLE 317 GRAPH DATABASE MARKET: REGION FOOTPRINT
TABLE 318 GRAPH DATABASE MARKET: LIST OF KEY STARTUPS/SMES
TABLE 319 COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
TABLE 320 GRAPH DATABASE: PRODUCT LAUNCHES AND ENHANCEMENTS, SEPTEMBER 2022-OCTOBER 2024
TABLE 321 GRAPH DATABASE MARKET: DEALS, JANUARY 2023-NOVEMBER 2024
TABLE 322 NEO4J: COMPANY OVERVIEW
TABLE 323 NEO4J: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 324 NEO4J: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 325 NEO4J: DEALS
TABLE 326 AMAZON WEB SERVICES: COMPANY OVERVIEW
TABLE 327 AMAZON WEB SERVICES: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 328 AMAZON WEB SERVICES: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 329 AMAZON WEB SERVICES: DEALS
TABLE 330 TIGERGRAPH: COMPANY OVERVIEW
TABLE 331 TIGERGRAPH: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 332 TIGERGRAPH: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 333 TIGERGRAPH: DEALS
TABLE 334 RELATIONALAI: COMPANY OVERVIEW
TABLE 335 RELATIONALAI: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 336 RELATIONALAI: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 337 GRAPHWISE: COMPANY OVERVIEW
TABLE 338 GRAPHWISE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 339 GRAPHWISE: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 340 IBM: COMPANY OVERVIEW
TABLE 341 IBM: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 342 IBM: DEALS
TABLE 343 MICROSOFT: COMPANY OVERVIEW
TABLE 344 MICROSOFT: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 345 MICROSOFT: DEALS
TABLE 346 ONTOTEXT: COMPANY OVERVIEW
TABLE 347 ONTOTEXT: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 348 ONTOTEXT: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 349 ONTOTEXT: DEALS
TABLE 350 STARDOG: COMPANY OVERVIEW
TABLE 351 STARDOG: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 352 STARDOG: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 353 STARDOG: DEALS
TABLE 354 ALTAIR: COMPANY OVERVIEW
TABLE 355 ALTAIR: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 356 ALTAIR: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 357 ALTAIR: DEALS
TABLE 358 ORACLE: COMPANY OVERVIEW
TABLE 359 ORACLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 360 ORACLE: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 361 PROGRESS SOFTWARE: COMPANY OVERVIEW
TABLE 362 PROGRESS SOFTWARE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 363 PROGRESS SOFTWARE: DEALS
TABLE 364 FRANZ INC: COMPANY OVERVIEW
TABLE 365 FRANZ INC: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 366 FRANZ INC.: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 367 DATASTAX: COMPANY OVERVIEW
TABLE 368 DATASTAX: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 369 DATASTAX: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 370 DATASTAX: DEALS
TABLE 371 CLOUD DATABASE AND DBAAS MARKET, BY COMPONENT, 2018-2022 (USD MILLION)
TABLE 372 CLOUD DATABASE AND DBAAS MARKET, BY COMPONENT, 2023-2028 (USD MILLION)
TABLE 373 CLOUD DATABASE AND DBAAS MARKET, BY SERVICE, 2018-2022 (USD MILLION)
TABLE 374 CLOUD DATABASE AND DBAAS MARKET, BY SERVICE, 2023-2028 (USD MILLION)
TABLE 375 CLOUD DATABASE AND DBAAS MARKET, BY DEPLOYMENT MODEL, 2018-2022 (USD MILLION)
TABLE 376 CLOUD DATABASE AND DBAAS MARKET, BY DEPLOYMENT MODEL, 2023-2028 (USD MILLION)
TABLE 377 CLOUD DATABASE AND DBAAS MARKET, BY ORGANIZATION SIZE, 2018-2022 (USD MILLION)
TABLE 378 CLOUD DATABASE AND DBAAS MARKET, BY ORGANIZATION SIZE, 2023-2028 (USD MILLION)
TABLE 379 CLOUD DATABASE AND DBAAS MARKET, BY VERTICAL, 2018-2022 (USD MILLION)
TABLE 380 CLOUD DATABASE AND DBAAS MARKET, BY VERTICAL, 2023-2028 (USD MILLION)
TABLE 381 CLOUD DATABASE AND DBAAS MARKET, BY REGION, 2018-2022 (USD MILLION)
TABLE 382 VECTOR DATABASE MARKET, BY OFFERING, 2019-2022 (USD MILLION)
TABLE 383 VECTOR DATABASE MARKET, BY OFFERING, 2023-2028 (USD MILLION)
TABLE 384 VECTOR DATABASE MARKET, BY TECHNOLOGY, 2019-2022 (USD MILLION)
TABLE 385 VECTOR DATABASE MARKET, BY TECHNOLOGY, 2023-2028 (USD MILLION)
TABLE 386 VECTOR DATABASE MARKET, BY VERTICAL, 2019-2022 (USD MILLION)
TABLE 387 VECTOR DATABASE MARKET, BY REGION, 2019-2022 (USD MILLION)
TABLE 388 VECTOR DATABASE MARKET, BY REGION, 2023-2028 (USD MILLION)
TABLE 389 VECTOR DATABASE MARKET, BY VERTICAL, 2023-2028 (USD MILLION)

List of Figures

FIGURE 1 GRAPH DATABASE MARKET: RESEARCH DESIGN
FIGURE 2 KEY DATA FROM SECONDARY SOURCES
FIGURE 3 TOP-DOWN APPROACH
FIGURE 4 APPROACH 1 (SUPPLY SIDE): REVENUE OF VENDORS IN GRAPH DATABASE MARKET
FIGURE 5 BOTTOM-UP APPROACH
FIGURE 6 DEMAND-SIDE ANALYSIS
FIGURE 7 BOTTOM-UP (SUPPLY SIDE) ANALYSIS: COLLECTIVE REVENUE FROM SOLUTIONS/SERVICES OF EMOTION AI MARKET
FIGURE 8 DATA TRIANGULATION
FIGURE 9 GRAPH DATABASE MARKET, 2024-2030 (USD MILLION)
FIGURE 10 GRAPH DATABASE MARKET, BY REGION (2024)
FIGURE 11 INCREASING RELIANCE ON REAL-TIME ANALYTICS FOR CRITICAL DECISION-MAKING ACROSS INDUSTRIES TO DRIVE MARKET
FIGURE 12 SOLUTIONS SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD
FIGURE 13 MANAGED SERVICES SEGMENT TO ACCOUNT FOR HIGHER CAGR DURING FORECAST PERIOD
FIGURE 14 DEPLOYMENT & INTEGRATION SERVICES SEGMENT TO ACCOUNT FOR LARGEST MARKET DURING FORECAST PERIOD
FIGURE 15 DATA GOVERNANCE & MASTER DATA MANAGEMENT SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE DURING FORECAST PERIOD
FIGURE 16 PROPERTY GRAPH TO ACCOUNT FOR LARGER MARKET SHARE DURING FORECAST PERIOD
FIGURE 17 BFSI SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE DURING FORECAST PERIOD
FIGURE 18 SOLUTIONS & PROPERTY GRAPH SEGMENTS TO ACCOUNT FOR SIGNIFICANT MARKET SHARES IN 2024
FIGURE 19 GRAPH DATABASE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
FIGURE 20 EVOLUTION OF GRAPH DATABASE MARKET
FIGURE 21 GRAPH DATABASE MARKET: ECOSYSTEM ANALYSIS
FIGURE 22 GRAPH DATABASE MARKET: SUPPLY CHAIN ANALYSIS
FIGURE 23 GRAPH DATABASE MARKET: INVESTMENT AND FUNDING SCENARIO, 2020-2024 (USD MILLION)
FIGURE 24 USE CASES OF GENERATIVE AI IN GRAPH DATABASE MARKET
FIGURE 25 LIST OF MAJOR PATENTS FOR GRAPH DATABASE MARKET (2014-2024)
FIGURE 26 AVERAGE SELLING PRICE OF KEY PLAYERS, BY COUNTRY, 2023
FIGURE 27 GRAPH DATABASE MARKET: PORTER'S FIVE FORCES ANALYSIS
FIGURE 28 GRAPH DATABASE MARKET: TRENDS/DISRUPTIONS INFLUENCING CUSTOMER BUSINESS
FIGURE 29 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS
FIGURE 30 KEY BUYING CRITERIA FOR TOP THREE INDUSTRIES
FIGURE 31 SERVICES SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD
FIGURE 32 KNOWLEDGE GRAPH ENGINES SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 33 MANAGED SERVICES TO GROW AT HIGHER CAGR DURING FORECAST PERIOD
FIGURE 34 SUPPORT & MAINTENANCE SERVICES TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 35 RESOURCE DESCRIPTION FRAMEWORK SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD
FIGURE 36 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 37 BFSI TO ACCOUNT FOR LARGEST MARKET DURING FORECAST PERIOD
FIGURE 38 NORTH AMERICA: GRAPH DATABASE MARKET SNAPSHOT
FIGURE 39 ASIA PACIFIC: GRAPH DATABASE MARKET SNAPSHOT
FIGURE 40 SHARE ANALYSIS OF LEADING COMPANIES IN GRAPH DATABASE MARKET, 2024
FIGURE 41 MARKET RANKING ANALYSIS OF TOP FIVE PLAYERS
FIGURE 42 REVENUE ANALYSIS OF KEY PLAYERS IN GRAPH DATABASE MARKET, 2019-2023 (USD MILLION)
FIGURE 43 GRAPH DATABASE MARKET: COMPANY EVALUATION MATRIX (KEY PLAYERS), 2024
FIGURE 44 GRAPH DATABASE MARKET: COMPANY FOOTPRINT
FIGURE 45 GRAPH DATABASE MARKET: COMPANY EVALUATION MATRIX (STARTUPS/SMES), 2024
FIGURE 46 BRAND COMPARISON
FIGURE 47 COMPANY VALUATION
FIGURE 48 FINANCIAL METRICS
FIGURE 49 AMAZON WEB SERVICES: COMPANY SNAPSHOT
FIGURE 50 IBM: COMPANY SNAPSHOT
FIGURE 51 MICROSOFT: COMPANY SNAPSHOT
FIGURE 52 ALTAIR: COMPANY SNAPSHOT
FIGURE 53 ORACLE: COMPANY SNAPSHOT
FIGURE 54 PROGRESS SOFTWARE: COMPANY SNAPSHOT

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License/价格

全球图形资料库市场（按解决方案和应用）- 2030 年预测

Graph Database Market by Solutions (Graph Extension, Graph Processing Engines, Native Graph Database, Knowledge Graph Engines), Application (Data Governance and Master Data Management, Infrastructure and Asset Management) - Global Forecast to 2030