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

1 INTRODUCTION 42 1.1 STUDY OBJECTIVES 42 1.2 MARKET DEFINITION 42 1.3 STUDY SCOPE 43 1.3.1 MARKET SEGMENTATION 43 1.3.2 INCLUSIONS AND EXCLUSIONS 44 1.3.3 YEARS CONSIDERED 44 1.4 CURRENCY CONSIDERED 45 1.5 STAKEHOLDERS 45 1.6 SUMMARY OF CHANGES 46 2 RESEARCH METHODOLOGY 47 2.1 RESEARCH DATA 47 2.1.1 SECONDARY DATA 48 2.1.1.1 Key data from secondary sources 48 2.1.2 PRIMARY DATA 49 2.1.2.1 Primary interviews with experts 49 2.1.2.2 Breakdown of primary interviews 49 2.1.2.3 Key industry insights 50 2.2 MARKET SIZE ESTIMATION 50 2.2.1 TOP-DOWN APPROACH 50 2.2.1.1 Supply-side analysis 51 2.2.2 BOTTOM-UP APPROACH 51 2.2.2.1 Demand-side analysis 52 2.3 DATA TRIANGULATION 54 2.4 RESEARCH ASSUMPTIONS 55 2.5 RESEARCH LIMITATIONS 56 2.6 RISK ASSESSMENT 56 3 EXECUTIVE SUMMARY 57 4 PREMIUM INSIGHTS 59 4.1 OPPORTUNITIES FOR KEY PLAYERS IN GRAPH DATABASE MARKET 59 4.2 GRAPH DATABASE MARKET, BY OFFERING 59 4.3 GRAPH DATABASE MARKET, BY SERVICE 60 4.4 GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE 60 4.5 GRAPH DATABASE MARKET, BY APPLICATION 60 4.6 GRAPH DATABASE MARKET, BY MODEL TYPE 61 4.7 GRAPH DATABASE MARKET, BY VERTICAL 61 4.8 NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING AND MODEL TYPE 62 5 MARKET OVERVIEW AND INDUSTRY TRENDS 63 5.1 MARKET DYNAMICS 63 5.1.1 DRIVERS 63 5.1.1.1 Increasing Gen AI applications 63 5.1.1.2 Surging need for incorporating real-time big data mining with result visualization 64 5.1.1.3 Rising demand for solutions that can process low-latency queries 64 5.1.1.4 Rapid use of virtualization for big data analytics 65 5.1.1.5 Growing demand for semantic search across unstructured content 65 5.1.2 RESTRAINTS 65 5.1.2.1 Lack of standardization and programming ease 65 5.1.2.2 Rapid proliferation of data management technologies 65 5.1.2.3 High implementation costs 66 5.1.3 OPPORTUNITIES 66 5.1.3.1 Data unification and rapid proliferation of knowledge graphs 66 5.1.3.2 Provision of semantic knowledgeable graphs to address complex-scientific research 66 5.1.3.3 Emphasis on emergence of open knowledge networks 67 5.1.4 CHALLENGES 67 5.1.4.1 Lack of technical expertise 67 5.1.4.2 Difficulty in demonstrating benefits of knowledge graphs in single application or use case 68 5.2 BEST PRACTICES IN GRAPH DATABASE MARKET 68 5.2.1 VALIDATION OF USE CASES 68 5.2.2 AVOIDANCE OF INEFFICIENT TRAVERSAL PATTERNS 68 5.2.3 USAGE OF DATA MODELING 69 5.2.4 ENSURING DATA CONSISTENCY 69 5.2.5 PARTITIONING OF COSMOS DB 69 5.2.6 FOSTERING TEAM EXPERTISE IN GRAPH DATABASE 69 5.3 EVOLUTION OF GRAPH DATABASE MARKET 70 5.4 ECOSYSTEM ANALYSIS 72 5.5 CASE STUDY ANALYSIS 73 5.5.1 NEO4J-POWERED KNOWLEDGE GRAPH HELPED INTUIT PROVIDE REAL-TIME INSIGHTS AND FACILITATE SWIFT RESPONSES TO SECURITY THREATS 73 5.5.2 WESTJET IMPROVED ITS CUSTOMER BOOKING EXPERIENCE BY INTEGRATING NEO4J'S GRAPH TECHNOLOGY 74 5.5.3 NEWDAY IMPROVED FRAUD DETECTION CAPABILITIES WITH TIGERGRAPH CLOUD 74 5.5.4 CYBER RESILIENCE LEADER LEVERAGED TIGERGRAPH TO ELEVATE ITS NEXT-GENERATION CLOUD-BASED CYBERSECURITY SERVICES 75 5.5.5 XBOX CHOSE TIGERGRAPH TO EMPOWER ITS GRAPH ANALYTICS CAPABILITIES 76 5.5.6 DGRAPH'S CUTTING-EDGE DATABASE SOLUTION ENABLED MOONCAMP TO STREAMLINE ITS BACKEND OPERATIONS 76 5.5.7 NEO4J’S GRAPH DATABASE AND APPLICATION PLATFORM HELPED KERBEROS CONTROL COMPLEX LEGAL OBLIGATIONS 77 5.5.8 BLAZEGRAPH HELPED YAHOO7 DRIVE NATIVE REAL-TIME ADVERTISING USING GRAPH QUERIES 78 5.5.9 NEO4J ENABLED ICU’S TEAM TO VISUALIZE AND ANALYZE CONNECTIONS BETWEEN ELEMENTS OF PANAMA PAPERS LEAKS 78 5.5.10 NEO4J’S GRAPH TECHNOLOGY HELPED U.S. ARMY BY TRACKING AND ANALYZING EQUIPMENT MAINTENANCE 79 5.5.11 JAGUAR LAND ROVER ACHIEVED REDUCED INVENTORY COSTS AND HIGHER PROFITABILITY USING TIGERGRAPH’S SOLUTION 79 5.5.12 MACY'S REDUCED CATALOG DATA REFRESH TIME BY SIX-FOLD 80 5.5.13 METAPHACTS AND ONTOTEXT ENABLED GLOBAL PHARMA COMPANY TO BOOST R&D KNOWLEDGE DISCOVERY 80 5.6 SUPPLY CHAIN ANALYSIS 81 5.7 INVESTMENT AND FUNDING SCENARIO 82 5.8 IMPACT OF GENERATIVE AI ON GRAPH DATABASE MARKET 82 5.8.1 USE CASES OF GENERATIVE AI IN GRAPH DATABASE 83 5.8.1.1 Neo4j LLM Knowledge Graph Builder enabled users to extract nodes and relationships from unstructured text 83 5.8.1.2 Data²’s flagship analytics platform, reView, delivered powerful insights by integrating customer data into Neo4j-backed knowledge graph 83 5.8.1.3 JPMorgan leveraged LLMs to detect fraudulent activities 83 5.8.1.4 Mastercard leveraged GenAI capabilities to strengthen its fraud detection system 84 5.9 TECHNOLOGY ROADMAP OF GRAPH DATABASE MARKET 85 5.10 REGULATORY LANDSCAPE 86 5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 86 5.10.2 KEY REGULATIONS 89 5.10.2.1 North America 89 5.10.2.1.1 SCR 17: Artificial Intelligence Bill (California) 89 5.10.2.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut) 90 5.10.2.1.3 National Artificial Intelligence Initiative Act (NAIIA) 90 5.10.2.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada 90 5.10.2.1.5 Cybersecurity Maturity Model Certification (CMMC) (USA) 91 5.10.2.2 Europe 91 5.10.2.2.1 The European Union (EU) - Artificial Intelligence Act (AIA) 91 5.10.2.2.2 General Data Protection Regulation (Europe) 91 5.10.2.3 Asia Pacific 92 5.10.2.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China) 92 5.10.2.3.2 National AI Strategy (Singapore) 92 5.10.2.3.3 Hiroshima AI Process Comprehensive Policy Framework (Japan) 93 5.10.2.4 Middle East & Africa 94 5.10.2.4.1 National Strategy for Artificial Intelligence (UAE) 94 5.10.2.4.2 National Artificial Intelligence Strategy (Qatar) 94 5.10.2.4.3 AI Ethics Principles and Guidelines (Dubai) 94 5.10.2.5 Latin America 95 5.10.2.5.1 The Santiago Declaration (Chile) 95 5.10.2.5.2 Brazilian Artificial Intelligence Strategy-EBIA 95 5.11 PATENT ANALYSIS 96 5.11.1 METHODOLOGY 96 5.11.2 LIST OF MAJOR PATENTS 97 5.12 TECHNOLOGY ANALYSIS 98 5.12.1 KEY TECHNOLOGIES 99 5.12.1.1 Semantic Web 99 5.12.1.2 Generative AI and natural language processing 99 5.12.1.3 Graph RAG 99 5.12.2 COMPLEMENTARY TECHNOLOGIES 100 5.12.2.1 Cloud computing 100 5.12.2.2 AI and ML 100 5.12.2.3 Big data & analytics 101 5.12.2.4 Graph neural networks 101 5.12.2.5 Vector databases and full-text search engines 101 5.12.2.6 Multimodal databases 101 5.12.3 ADJACENT TECHNOLOGIES 102 5.12.3.1 Digital twin 102 5.12.3.2 IoT 102 5.12.3.3 Blockchain 102 5.12.3.4 Edge computing 102 5.13 PRICING ANALYSIS 103 5.13.1 AVERAGE SELLING PRICE OF KEY PLAYERS, BY COUNTRY, 2023 103 5.13.2 INDICATIVE PRICING ANALYSIS, BY KEY PLAYER, 2023 104 5.14 KEY CONFERENCES AND EVENTS, 2024–2025 106 5.15 PORTER’S FIVE FORCES ANALYSIS 108 5.15.1 THREAT OF NEW ENTRANTS 109 5.15.2 THREAT OF SUBSTITUTES 109 5.15.3 BARGAINING POWER OF SUPPLIERS 109 5.15.4 BARGAINING POWER OF BUYERS 109 5.15.5 INTENSITY OF COMPETITIVE RIVALRY 109   5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 110 5.17 KEY STAKEHOLDERS AND BUYING CRITERIA 111 5.17.1 KEY STAKEHOLDERS IN BUYING PROCESS 111 5.17.2 BUYING CRITERIA 112 6 GRAPH DATABASE MARKET, BY OFFERING 113 6.1 INTRODUCTION 114 6.1.1 OFFERING: GRAPH DATABASE MARKET DRIVERS 114 6.2 SOLUTIONS 115 6.2.1 INCREASING NEED FOR ENHANCING PRODUCTIVITY AND MAINTAINING BUSINESS CONTINUITY TO DRIVE MARKET 115 6.2.2 BY SOLUTION TYPE 117 6.2.2.1 Graph extensions 117 6.2.2.2 Graph processing engines 118 6.2.2.3 Native graph database 119 6.2.2.4 Knowledge graph engines 119 6.2.3 BY DEPLOYMENT MODE 120 6.2.3.1 Cloud 121 6.2.3.2 On-premises 121 6.3 SERVICES 122 6.3.1 MANAGED SERVICES 124 6.3.1.1 Specialized skills for maintaining and updating graph database solutions to drive market 124 6.3.2 PROFESSIONAL SERVICES 125 6.3.2.1 Consulting services 126 6.3.2.1.1 Integration of graph databases with analytics and virtualization frameworks to boost market 126 6.3.2.2 Deployment & integration services 127 6.3.2.2.1 Growing need to overcome system-related issues effectively to drive market 127 6.3.2.3 Support & maintenance services 128 6.3.2.3.1 Services provided for upgradation and maintenance of operating ecosystem post-implementation to fuel market growth 128 7 GRAPH DATABASE MARKET, BY MODEL TYPE 130 7.1 INTRODUCTION 131 7.1.1 MODEL TYPE: GRAPH DATABASE MARKET DRIVERS 131 7.2 RESOURCE DESCRIPTION FRAMEWORK 132 7.2.1 NEED FOR INTELLIGENT DATA MANAGEMENT SOLUTIONS TO DRIVE DEMAND FOR GRAPH DATABASE 132 7.3 PROPERTY GRAPH 133 7.3.1 INCREASING URGE TO FIND RELATIONSHIPS AMONG NUMEROUS ENTITIES TO BOOST MARKET 133 7.3.1.1 Labeled property graph 134 7.3.1.2 Typed property graph 134 8 GRAPH DATABASE MARKET, BY APPLICATION 135 8.1 INTRODUCTION 136 8.1.1 APPLICATION: GRAPH DATABASE MARKET DRIVERS 136 8.2 DATA GOVERNANCE & MASTER DATA MANAGEMENT 138 8.2.1 NEED FOR MANAGING, INTEGRATING, AND SECURING COMPLEX DATA RELATIONSHIPS TO DRIVE MARKET 138 8.3 DATA ANALYTICS & BUSINESS INTELLIGENCE 139 8.3.1 SUPERIOR QUERY PERFORMANCE FOR COMPLEX OPERATIONS TO BOOST MARKET 139 8.4 KNOWLEDGE & CONTENT MANAGEMENT 140 8.4.1 INTUITIVE AND DYNAMIC WAY OF ORGANIZING, CONNECTING, AND RETRIEVING INFORMATION TO FUEL MARKET GROWTH 140 8.5 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY 141 8.5.1 PERSONALIZED, INTELLIGENT, AND CONTEXT-AWARE INTERACTIONS TO SUPPORT MARKET GROWTH 141 8.6 PRODUCT & CONFIGURATION MANAGEMENT 142 8.6.1 VISIBILITY INTO INTERDEPENDENCIES ACROSS TEAMS TO ENSURE TRACEABILITY AND BETTER DECISION-MAKING 142 8.7 INFRASTRUCTURE & ASSET MANAGEMENT 143 8.7.1 MODELING AND ANALYSIS OF INTRICATE RELATIONSHIPS BETWEEN ASSETS TO DRIVE MARKET 143 8.8 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT 144 8.8.1 OPTIMIZE PROCESS BY ANALYZING COMPLEX, INTERCONNECTED DATA THROUGH GRAPH DATA SCIENCE 144 8.9 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING 145 8.9.1 IDENTIFICATION AND ASSESSMENT OF RISKS BY VISUALIZING CONNECTIONS TO BOOST MARKET 145 8.10 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION 146 8.10.1 GRAPH DATABASES TO IMPROVE SALES EFFECTIVENESS AND CUSTOMER ENGAGEMENT 146 8.11 OTHER APPLICATIONS 147 9 GRAPH DATABASE MARKET, BY VERTICAL 149 9.1 INTRODUCTION 150 9.1.1 VERTICAL: GRAPH DATABASE MARKET DRIVERS 150 9.2 BANKING, FINANCIAL SERVICES, AND INSURANCE 152 9.2.1 GROWING ADOPTION OF FINANCIAL STANDARDS AND COMPLIANCE WITH REGULATIONS TO DRIVE MARKET 152 9.2.2 CASE STUDY 153 9.2.2.1 Fraud detection & risk management 153 9.2.2.1.1 Neo4j-powered system helped BNP Paribas Personal Finance achieve a 20% reduction in fraud 153 9.2.2.1.2 Zurich Switzerland enhanced fraud investigations with Neo4j 154 9.2.2.2 Anti-money laundering 154 9.2.2.2.1 US bank leveraged TigerGraph's graph analytics capabilities to detect intricate money laundering network 154 9.2.2.2.2 KERBEROS enhanced money laundering capabilities with Neo4j's graph database and Structr application platform 155 9.2.2.3 Identity & access management 155 9.2.2.3.1 Ability for mapping and querying intricate relationships to drive market 155 9.2.2.4 Risk management 155 9.2.2.4.1 Rising usage of graph database tools and services for enhancing risk intelligence capabilities to aid market growth 155 9.2.2.4.2 UBS implemented Neo4j's graph database to improve its data lineage and governance 156 9.2.2.4.3 Marionete integrated its various databases with the Neo4j graph database, enabling it to reduce credit risk and influence charges 156 9.2.2.5 Data integration & governance 156 9.2.2.5.1 Optimizing data security and privacy 156 9.2.2.5.2 Real-time monitoring and audit 157 9.2.2.6 Know Your Customer (KYC) process 157 9.2.2.6.1 Neo4j’s graph technology helped institutions save time in compliance workflows 157 9.2.2.7 Operational resilience for bank IT systems 158 9.2.2.7.1 Stardog’s platform allowed for easy navigation through interconnected data, helping organizations identify dependencies and analyze systemic risks 158 9.2.2.8 Regulatory compliance 158 9.2.2.8.1 Streamlining regulatory compliance with RDFoc 158 9.2.2.9 Customer 360° view 159 9.2.2.9.1 Unified, holistic perspective of each customer by integrating data from multiple sources 159 9.2.2.10 Market analysis & trend detection 159 9.2.2.10.1 Graph databases to help gain deeper insights into organizations’ complex relationships and enhance customer experiences 159 9.2.2.11 Policy impact analysis 160 9.2.2.11.1 Real-time updates to ensure quick adaptability to changing regulations, minimizing disruptions, and maintaining operational efficiency 160 9.2.2.12 Self-service data and digital asset discovery 160 9.2.2.12.1 Empowerment of users without technical expertise to independently find, explore, and handle data fosters market growth 160 9.2.2.13 Customer support 160 9.2.2.13.1 Quick issue resolution, personalized responses, and customized recommendations to boost market 160   9.3 RETAIL & ECOMMERCE 160 9.3.1 INCREASING NEED FOR IDENTIFYING CUSTOMER BEHAVIOR IN REAL-TIME TO DRIVE MARKET 160 9.3.2 CASE STUDY 162 9.3.2.1 Fraud detection in eCommerce 162 9.3.2.1.1 PayPal leveraged real-time graph databases and graph analysis to combat fraud effectively 162 9.3.2.2 Dynamic pricing optimization 162 9.3.2.2.1 Deployment of Neo4j-based system significantly improved efficiency and scalability in Marriott’s pricing operations 162 9.3.2.3 Personalized product recommendations 162 9.3.2.3.1 Neo4j’s graph-based approach allowed Walmart to enhance online shopping experience and maintain competitive edge 163 9.3.2.3.2 AboutYou transformed personalized shopping with ArangoDB, boosting engagement and efficiency 163 9.3.2.4 Market basket analysis 163 9.3.2.4.1 Analyzing relationship between product pricing and consumer behavior to support development of optimized pricing strategies 163 9.3.2.5 Customer experience enhancement 163 9.3.2.5.1 Retailer achieved enhanced store operations and improved customer satisfaction with TigerGraph’s platform 164 9.3.2.6 Churn Prediction & Prevention 164 9.3.2.6.1 Predicting churn helps companies identify customers at risk of leaving 164 9.3.2.7 Social media influence on buying behavior 164 9.3.2.7.1 Increasing need for understanding and leveraging dynamics of social media influencing consumer-buying decisions to fuel market growth 164 9.3.2.8 Product Configuration & Recommendation 165 9.3.2.8.1 Neo4j's graph database enabled eBay achieve seamless and intelligent product discovery experience 165 9.3.2.9 Customer Segmentation & Targeting 165 9.3.2.9.1 Targeted advertising and personalized shopping experiences to help drive sales 165 9.3.2.10 Customer 360° View 165 9.3.2.10.1 Tracking of customer’s purchase behavior to aid market growth 165 9.3.2.10.2 Neo4j empowered Hästens to build comprehensive 360-degree view of its data, operations, customers, and partners 166 9.3.2.11 Review & reputation management 166 9.3.2.11.1 To enhance and manage customer review to protect reputation 166 9.3.2.12 Customer Support 166 9.3.2.12.1 To improved customer satisfaction, faster response times, and stronger customer loyalty 166 9.4 TELECOM & TECHNOLOGY 166 9.4.1 SURGING DEMAND FOR IMPROVED SERVICES TO DRIVE MARKET 166 9.4.2 CASE STUDY 168 9.4.2.1 Network optimization & management 168 9.4.2.1.1 Australia's leading carrier enhanced network monitoring and security with ArangoDB 168 9.4.2.2 Data integration & governance 168 9.4.2.2.1 D&B achieved significant revenue growth and expanded its customer base using Neo4j’s graph technology 168 9.4.2.3 IT asset management 168 9.4.2.3.1 Orange leveraged ArangoDB to build digital twin platform for enhanced process optimization 168 9.4.2.4 Network security analysis 169 9.4.2.4.1 Zeta Global chose Amazon Neptune for its scalability, elasticity, and cost-effectiveness 169 9.4.2.5 IoT device management & connectivity 169 9.4.2.5.1 BT Group leveraged Neo4j to deliver lightning-fast inventory management and streamline operations 169 9.4.2.5.2 Amazon Neptune's capabilities empowered telecom & IT sectors to achieve enhanced device orchestration and seamless integration of IoT data 169 9.4.2.6 Self-service data & digital asset discovery 170 9.4.2.6.1 Optimizing telecom operations with self-service data and digital asset discovery 170 9.4.2.7 Identity & access management 170 9.4.2.7.1 Interconnected data model helped Telenor Norway eliminate performance bottlenecks and deliver faster insights 170 9.4.2.7.2 Enhanced identity management and recommendations with TigerGraph 170 9.4.2.8 Metadata enrichment 170 9.4.2.8.1 Enhancing document findability with metadata enrichment at Cisco 170 9.4.2.9 Service incident management 171 9.4.2.9.1 Proactive incident management with Neo4j-powered intelligent network analysis tool 171 9.5 HEALTHCARE, LIFE SCIENCES, AND PHARMACEUTICALS 171 9.5.1 NEED FOR IMPROVED PATIENT-CENTRIC EXPERIENCE AND REAL-TIME TREATMENT TO DRIVE MARKET 171 9.5.2 CASE STUDY 173 9.5.2.1 Drug discovery & development 173 9.5.2.1.1 Novartis harnessed cutting-edge biological insights for drug discovery 173 9.5.2.1.2 Revolutionizing biodiversity insights with graph-powered knowledge mapping 173   9.5.2.2 Clinical trial management 173 9.5.2.2.1 Neo4j’s knowledge graph-based application helped Novo Nordisk achieve end-to-end consistency and increased automation 173 9.5.2.3 Medical claims processing 174 9.5.2.3.1 UnitedHealth improved medical claim processing with graph databases 174 9.5.2.4 Clinical intelligence 174 9.5.2.4.1 UnitedHealth Group deployed graph database to enhance patient care 174 9.5.2.4.2 Dooloo turned to Neo4j’s Graph Data Platform for delivering personalized, data-driven insights 174 9.5.2.5 Healthcare network provider analysis 174 9.5.2.5.1 Boston Scientific utilized Neo4j’s Graph Data Science Library to simplify complex medical supply chain analysis 175 9.5.2.5.2 Amgen enhanced data analysis and scalability with TigerGraph for healthcare insights 175 9.5.2.6 Customer support 175 9.5.2.6.1 Exact Sciences enhanced customer engagement with implementation of Doctor-and-Product 360 solution powered by TigerGraph 175 9.5.2.6.2 Optimizing healthcare customer support with Graph RAG-powered chatbots 176 9.5.2.7 Patient journey & care pathway analysis 176 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 176 9.5.2.8 Self-service data & digital asset discovery 176 9.5.2.8.1 Stardog-powered enterprise knowledge graph enabled Boehringer Ingelheim to address its challenge of siloed research data 176 9.6 GOVERNMENT & PUBLIC SECTOR 177 9.6.1 RISING NEED FOR ENHANCED DATA SECURITY AND ADVANCED INTELLIGENCE TO DRIVE MARKET 177 9.6.2 CASE STUDY 178 9.6.2.1 Government service optimization 178 9.6.2.1.1 Empowering government agencies with Stardog Voicebox for seamless data insights and enhanced decision-making 178 9.6.2.2 Legislative & regulatory analysis 178 9.6.2.2.1 Streamlining legislative and regulatory analysis with graph databases for enhanced compliance and decision-making 178 9.6.2.3 Crisis management& disaster response planning 179 9.6.2.3.1 Strengthening cybersecurity with graph databases for proactive threat detection and risk management 179 9.6.2.4 Environmental impact analysis & ESG 179 9.6.2.4.1 NASA leveraged Stardog’s Enterprise Knowledge Platform, enabling seamless integration and analysis 179   9.6.2.5 Social network analysis for security and law enforcement 179 9.6.2.5.1 Global financial institution leveraged Neo4j and Linkurious Enterprise (LE) to enhance fraud detection 179 9.6.2.6 Policy impact analysis 180 9.6.2.6.1 Transforming information access at IDB with knowledge graphs 180 9.6.2.7 Knowledge management 180 9.6.2.7.1 Neo4j’s graph database helped NASA leverage historical insights to reduce project timelines and prevent disasters 180 9.6.2.8 Data integration & governance 180 9.6.2.8.1 Transforming product lifecycle management with graph technology 180 9.7 MANUFACTURING & AUTOMOTIVE 181 9.7.1 GROWING NEED FOR EXTENDING FACTORY EQUIPMENT LIFESPAN AND REDUCING PRODUCTION RISK DELAYS TO BOOST GROWTH 181 9.7.2 CASE STUDY 182 9.7.2.1 Equipment management & predictive maintenance 182 9.7.2.1.1 Leveraging graph databases for flexible and robust operations 182 9.7.2.2 Product lifecycle management 182 9.7.2.2.1 Japanese automotive manufacturer optimized product life cycle and validation with Neo4j-powered knowledge graph 182 9.7.2.3 Manufacturing process optimization 183 9.7.2.3.1 Optimizing manufacturing processes with Stardog Voicebox and Databricks for enhanced quality and efficiency 183 9.7.2.3.2 Ford enhanced manufacturing efficiency with TigerGraph 183 9.7.2.4 Enhanced vehicle safety and reliability 183 9.7.2.4.1 Increase vehicle safety with advanced technologies and graph databases 183 9.7.2.5 Optimization of industrial processes 184 9.7.2.5.1 Enhancing smart manufacturing with Siemens' knowledge graph and AI-driven automation 184 9.7.2.5.2 Optimizing automotive pricing and processes with Neo4j and AWS 184 9.7.2.6 Root cause analysis 184 9.7.2.6.1 Leveraging knowledge graphs for transparent and effective root cause analysis 184 9.7.2.7 Inventory management & demand forecasting 185 9.7.2.7.1 Optimizing Inventory management with dynamic stock calculation and cost analysis 185 9.7.2.8 Service incident management 185 9.7.2.8.1 Improving service incident management with graph databases in manufacturing and automotive 185 9.7.2.9 Staff & resource allocation 185 9.7.2.9.1 Enhancing resource and staff allocation efficiency using graph databases 185   9.7.2.10 Product configuration & recommendation 186 9.7.2.10.1 Cox Automotive built identity graph using Amazon Neptune to connect and analyze large datasets of shopper information 186 9.8 MEDIA & ENTERTAINMENT 186 9.8.1 DEMAND FOR MODELING-USER PREFERENCES AND CONTENT INTERACTIONS TO FOSTER MARKET GROWTH 186 9.8.2 CASE STUDY 187 9.8.2.1 Content recommendation & personalization 187 9.8.2.1.1 Graph databases enable media companies to provide highly accurate content recommendations and personalized experiences 187 9.8.2.1.2 Kickdynamic adopted TigerGraph on AWS Cloud to power its recommendation engine 187 9.8.2.1.3 Musimap adopted Neo4j graph database to offer personalized music recommendations 188 9.8.2.2 Social media influence analysis 188 9.8.2.2.1 Myntelligence optimized social media campaigns with TigerGraph's real-time analytics 188 9.8.2.2.2 TigerGraph’s advanced analytics enable OpenCorporates to support complex investigative queries with real-time response times 188 9.8.2.3 Content recommendation system 189 9.8.2.3.1 IppenDigital’s adoption of TigerGraph’s graph database technology helped deliver hyper-personalized content recommendations 189 9.8.2.3.2 Netflix leveraged graph databases for personalization and scalability 189 9.8.2.4 User engagement analysis 189 9.8.2.4.1 Enabling enterprises to capture and dissect intricate associations among users 189 9.8.2.4.2 Graph technology powered personalized smart home automation for Xfinity 190 9.8.2.5 Copyright and licensing management 190 9.8.2.5.1 Enhancing license and copyright management in media & entertainment industry through graph database technology 190 9.8.2.6 Knowledge management 190 9.8.2.6.1 Graph technology to enhance collaboration and accelerate decision-making 190 9.8.2.7 Audience segmentation and targeting 191 9.8.2.7.1 Optimizing audience segmentation and targeting for maximum impact 191 9.8.2.8 Self-service data and digital asset discovery 191 9.8.2.8.1 Consistent metadata management, robust security, user training, and scalability required to handle growing volume of assets effectively 191   9.9 ENERGY & UTILITIES 191 9.9.1 SURGING DEMAND FOR DECREASING OPERATIONAL RISKS AND COSTS TO DRIVE MARKET 191 9.9.2 CASE STUDY 192 9.9.2.1 Smart grid management 192 9.9.2.1.1 Adoption of graph database to manage complex relationships and interconnected data 192 9.9.2.2 Energy trading optimization 193 9.9.2.2.1 Unlocking efficient energy trading with graph database technology 193 9.9.2.3 Renewable energy integration & optimization 193 9.9.2.3.1 Graph databases to enhance visibility into entire energy ecosystem 193 9.9.2.4 Public Infrastructure Management 193 9.9.2.4.1 Enhancing public infrastructure management with graph databases 193 9.9.2.5 Customer Engagement And Billing 194 9.9.2.5.1 Ease billing process to improve customer satisfaction 194 9.9.2.6 Service incident management 194 9.9.2.6.1 Enxchange transformed energy grid management with graph-based digital twins for real-time insights and cost savings 194 9.9.2.7 Environmental impact analysis and ESG 195 9.9.2.7.1 Optimizing energy sustainability and environmental impact with graph databases 195 9.9.2.7.2 Integration of advanced technologies to enhance data management and insights 195 9.9.2.8 Railway asset management 195 9.9.2.8.1 Customized knowledge graphs enable smarter decision-making, predictive maintenance, and cost-effective operations 195 9.9.2.9 Staff and resource allocation 196 9.9.2.9.1 Optimizing staff and resource allocation for sustainable energy operations 196 9.10 TRAVEL & HOSPITALITY 196 9.10.1 FOCUS ON FOSTERING TRAVEL PLANS FOR BETTER CUSTOMER EXPERIENCES TO DRIVE MARKET EXPANSION 196 9.10.2 CASE STUDY 197 9.10.2.1 Personalized travel recommendations 197 9.10.2.1.1 Revolutionizing personalized travel recommendations with graph databases 197 9.10.2.2 Dynamic pricing optimization 197 9.10.2.2.1 Transforming dynamic price management with graph databases 197 9.10.2.3 Customer journey mapping 198 9.10.2.3.1 Customer journey mapping to give personalized recommendations 198 9.10.2.4 Booking and reservation management 198 9.10.2.4.1 Graph databases ensure seamless customer experiences and efficient operations 198 9.10.2.5 Customer experience management 198 9.10.2.5.1 Transforming customer experience with unified data and actionable insights 198 9.10.2.6 Product configuration and recommendation 199 9.10.2.6.1 Dynamic product configuration and personalized recommendations in travel and hospitality 199 9.11 TRANSPORTATION & LOGISTICS 199 9.11.1 RISING NEED FOR GAINING COMPLETE AND REAL-TIME VISIBILITY TO DRIVE MARKET 199 9.11.2 TRANSPORT FOR LONDON (TFL) REDUCED CONGESTION BY 10% USING DIGITAL TWIN POWERED BY NEO4J 199 9.11.3 USE CASES 200 9.11.3.1 Route optimization and fleet management 200 9.11.3.1.1 Careem achieved enhanced fraud detection with AWS 200 9.11.3.1.2 Optimizing delivery routes and scaling logistics with precision data 201 9.11.3.2 Supply chain management 201 9.11.3.2.1 Transforming supply chains with Google Cloud and Neo4j 201 9.11.3.3 Asset tracking and management 201 9.11.3.3.1 Graph databases to model intricate relationships and dependencies between assets, locations, and stakeholders 201 9.11.3.4 Equipment maintenance and predictive maintenance 201 9.11.3.4.1 Optimizing equipment maintenance with predictive insights powered by graph databases 201 9.11.3.5 Supply chain management 202 9.11.3.5.1 Revolutionizing supply chain visibility through real-time digital twin solutions 202 9.11.3.6 Vendor and supplier analysis 202 9.11.3.6.1 Graph database to enable comprehensive view of supply chain 202 9.11.3.7 Operational efficiency & decision-making 202 9.11.3.7.1 Optimizing delivery routes and scaling logistics with precision data 202 9.12 OTHER VERTICALS 203 10 GRAPH DATABASE MARKET, BY REGION 204 10.1 INTRODUCTION 205 10.2 NORTH AMERICA 206 10.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK 206 10.2.2 US 213 10.2.2.1 Increasing use of graph databases in medical science and political campaigns to foster market growth 213   10.2.3 CANADA 219 10.2.3.1 Stringent data regulation and extensive applications of graph databases in research to drive growth 219 10.3 EUROPE 219 10.3.1 EUROPE: MACROECONOMIC OUTLOOK 219 10.3.2 UK 225 10.3.2.1 Government initiatives and healthcare-focused projects to drive market growth 225 10.3.3 ITALY 230 10.3.3.1 Increasing use of graph databases in financial sector to accelerate market growth 230 10.3.4 GERMANY 235 10.3.4.1 Increasing focus on enhancing interoperability to boost market 235 10.3.5 FRANCE 235 10.3.5.1 Graph databases to drive innovation, enabling data-driven decision-making across key industries 235 10.3.6 SPAIN 236 10.3.6.1 Government initiatives and geographical research to bolster market growth 236 10.3.7 REST OF EUROPE 236 10.4 ASIA PACIFIC 237 10.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK 237 10.4.2 CHINA 244 10.4.2.1 Major players and use of graph databases in telecom fueling market growth 244 10.4.3 INDIA 249 10.4.3.1 Increasing focus on digital transformation to support market growth 249 10.4.4 JAPAN 254 10.4.4.1 Integration of knowledge graphs with generative AI to fuel market growth 254 10.4.5 AUSTRALIA & NEW ZEALAND 255 10.4.5.1 Strategic initiatives and presence of major players to drive adoption of graph databases 255 10.4.6 SOUTH KOREA 255 10.4.6.1 Increasing applications of graph databases in fraud detection, network analysis, and AI-powered innovations to aid market growth 255 10.4.7 REST OF ASIA PACIFIC 255 10.5 MIDDLE EAST & AFRICA 256 10.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 256 10.5.2 MIDDLE EAST 262 10.5.2.1 KSA 263 10.5.2.1.1 Digitalization initiatives to drive market growth 263   10.5.2.2 UAE 268 10.5.2.2.1 Increasing applications of graph databases for environmental insights and research collaboration to drive market growth 268 10.5.2.3 Qatar 268 10.5.2.3.1 Rising demand for advanced data analytics and interconnected data management solutions to drive market growth 268 10.5.2.4 Turkey 268 10.5.2.4.1 Increasing adoption of graph technologies to address challenges in data analytics, decision-making, and innovation 268 10.5.2.5 Rest of Middle East 269 10.5.3 AFRICA 269 10.5.3.1 Strategic investments in cloud and AI technologies to drive adoption of graph databases 269 10.6 LATIN AMERICA 269 10.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK 270 10.6.2 BRAZIL 276 10.6.2.1 Growing adoption of graph databases across industries and key collaborative initiatives to drive market 276 10.6.3 ARGENTINA 281 10.6.3.1 Advancements in cloud infrastructure and AI to further enable scalable deployment of graph databases 281 10.6.4 MEXICO 281 10.6.4.1 Increasing investments in cloud infrastructure to accelerate adoption of graph databases 281 10.6.5 REST OF LATIN AMERICA 281 11 COMPETITIVE LANDSCAPE 282 11.1 INTRODUCTION 282 11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 282 11.3 MARKET SHARE ANALYSIS, 2024 284 11.3.1 MARKET RANKING ANALYSIS 286 11.4 REVENUE ANALYSIS, 2019–2023 287 11.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024 287 11.5.1 STARS 287 11.5.2 EMERGING LEADERS 287 11.5.3 PERVASIVE PLAYERS 288 11.5.4 PARTICIPANTS 288 11.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024 289 11.5.5.1 Company footprint 289 11.5.5.2 Offering footprint 289 11.5.5.3 Model type footprint 290 11.5.5.4 Application footprint 291 11.5.5.5 Vertical footprint 291 11.5.5.6 Region footprint 292 11.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024 293 11.6.1 PROGRESSIVE COMPANIES 293 11.6.2 RESPONSIVE COMPANIES 293 11.6.3 DYNAMIC COMPANIES 293 11.6.4 STARTING BLOCKS 293 11.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024 295 11.6.5.1 Detailed list of key startups/SMEs 295 11.6.5.2 Competitive benchmarking of key startups/SMEs 296 11.7 COMPETITIVE SCENARIO 297 11.7.1 PRODUCT LAUNCHES AND ENHANCEMENTS 297 11.7.2 DEALS 299 11.8 BRAND COMPARISON 301 11.9 COMPANY VALUATION AND FINANCIAL METRICS 302 12 COMPANY PROFILES 303 12.1 KEY PLAYERS 303 12.1.1 NEO4J 303 12.1.1.1 Business overview 303 12.1.1.2 Products/Solutions/Services offered 303 12.1.1.3 Recent developments 305 12.1.1.3.1 Product launches and enhancements 305 12.1.1.3.2 Deals 305 12.1.1.4 MnM view 306 12.1.1.4.1 Key strengths 306 12.1.1.4.2 Strategic choices 306 12.1.1.4.3 Weaknesses and competitive threats 306 12.1.2 AMAZON WEB SERVICES, INC 307 12.1.2.1 Business overview 307 12.1.2.2 Products/Solutions/Services offered 308 12.1.2.3 Recent developments 308 12.1.2.3.1 Product launches and enhancements 308 12.1.2.3.2 Deals 309 12.1.2.4 MnM view 309 12.1.2.4.1 Key strengths 309 12.1.2.4.2 Strategic choices 309 12.1.2.4.3 Weaknesses and competitive threats 310 12.1.3 TIGERGRAPH 311 12.1.3.1 Business overview 311 12.1.3.2 Products/Solutions/Services offered 311 12.1.3.3 Recent developments 312 12.1.3.3.1 Product launches and enhancements 312 12.1.3.3.2 Deals 313 12.1.3.4 MnM view 313 12.1.3.4.1 Key strengths 313 12.1.3.4.2 Strategic choices 313 12.1.3.4.3 Weaknesses and competitive threats 314 12.1.4 RELATIONALAI 315 12.1.4.1 Business overview 315 12.1.4.2 Products/Solutions/Services offered 315 12.1.4.3 Recent developments 316 12.1.4.3.1 Product launches and enhancements 316 12.1.4.4 MnM view 316 12.1.4.4.1 Key strengths 316 12.1.4.4.2 Strategic choices 316 12.1.4.4.3 Weaknesses and competitive threats 316 12.1.5 GRAPHWISE 317 12.1.5.1 Business overview 317 12.1.5.2 Products/Solutions/Services offered 317 12.1.5.3 Recent developments 317 12.1.5.3.1 Product launches and enhancements 317 12.1.5.4 MnM view 318 12.1.5.4.1 Key strengths 318 12.1.5.4.2 Strategic choices 318 12.1.5.4.3 Weaknesses and competitive threats 318 12.1.6 IBM CORPORATION 319 12.1.6.1 Business overview 319 12.1.6.2 Products/Solutions/Services offered 320 12.1.6.3 Recent developments 321 12.1.6.3.1 Deals 321 12.1.7 MICROSOFT CORPORATION, INC. 322 12.1.7.1 Business overview 322 12.1.7.2 Products/Solutions/Services offered 323 12.1.7.3 Recent developments 324 12.1.7.3.1 Deals 324 12.1.8 ONTOTEXT 325 12.1.8.1 Business overview 325 12.1.8.2 Products/Solutions/Services offered 325 12.1.8.3 Recent developments 327 12.1.8.3.1 Product launches and enhancements 327 12.1.8.3.2 Deals 327   12.1.9 STAR DOG 329 12.1.9.1 Business overview 329 12.1.9.2 Products/Solutions/Services offered 329 12.1.9.3 Recent developments 330 12.1.9.3.1 Product launches and enhancements 330 12.1.9.3.2 Deals 330 12.1.10 ALTAIR 331 12.1.10.1 Business overview 331 12.1.10.2 Products/Solutions/Services offered 332 12.1.10.3 Recent developments 333 12.1.10.3.1 Product launches and enhancements 333 12.1.10.3.2 Deals 333 12.1.11 ORACLE CORPORATION 334 12.1.11.1 Business overview 334 12.1.11.2 Products/Solutions/Services offered 335 12.1.11.3 Recent developments 336 12.1.11.3.1 Product launches and enhancements 336 12.1.12 PROGRESS SOFTWARE 337 12.1.12.1 Business overview 337 12.1.12.2 Products/Solutions/Services offered 338 12.1.12.3 Recent developments 338 12.1.12.3.1 Deals 338 12.1.13 FRANZ INC. 339 12.1.13.1 Business overview 339 12.1.13.2 Products/Solutions/Services offered 339 12.1.13.3 Recent developments 340 12.1.13.3.1 Product launches and enhancements 340 12.1.14 DATASTAX 341 12.1.14.1 Business overview 341 12.1.14.2 Products/Solutions/Services offered 341 12.1.14.3 Recent developments 342 12.1.14.3.1 Product launches and enhancements 342 12.1.14.3.2 Deals 342 12.1.15 DGRAPH LABS 343 12.1.16 OPENLINK SOFTWARE 344 12.2 STARTUPS/SMES 345 12.2.1 OXFORD SEMANTIC TECHNOLOGIES 345 12.2.2 BITNINE 345 12.2.3 ARANGODB 346 12.2.4 FLUREE 347 12.2.5 BLAZEGRAPH 348 12.2.6 MEMGRAPH 348 12.2.7 OBJECTIVITY INC 349 12.2.8 GRAPHBASE 349 12.2.9 GRAPH STORY 350 12.2.10 FALKORDB 350 13 ADJACENT AND RELATED MARKETS 351 13.1 INTRODUCTION 351 13.2 MARKET DEFINITION 351 13.3 CLOUD DATABASE AND DBAAS MARKET 351 13.3.1 MARKET DEFINITION 351 13.3.2 MARKET OVERVIEW 351 13.3.2.1 Cloud database and DBaaS market, by component 352 13.3.2.2 Cloud database and DBaaS market, by deployment model 352 13.3.2.3 Cloud database and DBaaS market, by organization size 353 13.3.2.4 Cloud database and DBaaS market, by vertical 354 13.3.2.5 Cloud database and DBaaS market, by region 355 13.4 VECTOR DATABASE MARKET 356 13.4.1 MARKET DEFINITION 356 13.4.2 VECTOR DATABASE MARKET, BY OFFERING 356 13.4.3 VECTOR DATABASE MARKET, BY TECHNOLOGY 357 13.4.4 VECTOR DATABASE MARKET, BY VERTICAL 357 13.4.5 VECTOR DATABASE MARKET, BY REGION 358 14 APPENDIX 360 14.1 DISCUSSION GUIDE 360 14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 365 14.3 CUSTOMIZATION OPTIONS 367 14.4 RELATED REPORTS 367 14.5 AUTHOR DETAILS 368

TABLE 1 USD EXCHANGE RATE, 2021–2023 45 TABLE 2 PRIMARY INTERVIEWS WITH EXPERTS 49 TABLE 3 RISK ASSESSMENT 56 TABLE 4 GRAPH DATABASE MARKET: ECOSYSTEM 72 TABLE 5 TECHNOLOGY ROADMAP OF GRAPH DATABASE MARKET, 2024–2030 85 TABLE 6 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 86 TABLE 7 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 87 TABLE 8 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 88 TABLE 9 REST OF THE WORLD: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 89 TABLE 10 GRAPH DATABASE MARKET: KEY PATENTS, 2014–2022 97 TABLE 11 AVERAGE SELLING PRICES OF GRAPH DATABASE SOLUTIONS, BY REGION, 2023 104 TABLE 12 INDICATIVE PRICING ANALYSIS OF KEY PLAYERS, 2023 (USD) 104 TABLE 13 GRAPH DATABASE MARKET: CONFERENCES AND EVENTS, 2024–2025 106 TABLE 14 IMPACT OF PORTER’S FIVE FORCES ON GRAPH DATABASE MARKET 108 TABLE 15 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS 111 TABLE 16 KEY BUYING CRITERIA FOR TOP THREE INDUSTRIES 112 TABLE 17 GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 115 TABLE 18 GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 115 TABLE 19 GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 116 TABLE 20 GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 116 TABLE 21 SOLUTIONS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 117 TABLE 22 SOLUTIONS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 117 TABLE 23 GRAPH EXTENSIONS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 117 TABLE 24 GRAPH EXTENSIONS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 118 TABLE 25 GRAPH PROCESSING ENGINES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 118 TABLE 26 GRAPH PROCESSING ENGINES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 118 TABLE 27 NATIVE GRAPH DATABASE: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 119 TABLE 28 NATIVE GRAPH DATABASE: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 119 TABLE 29 KNOWLEDGE GRAPH ENGINES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 120 TABLE 30 KNOWLEDGE GRAPH ENGINES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 120 TABLE 31 GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 120 TABLE 32 GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 120 TABLE 33 CLOUD: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 121 TABLE 34 CLOUD: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 121 TABLE 35 ON-PREMISES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 122 TABLE 36 ON-PREMISES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 122 TABLE 37 GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 123 TABLE 38 GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 123 TABLE 39 SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 123 TABLE 40 SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 123 TABLE 41 MANAGED SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 124 TABLE 42 MANAGED SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 124 TABLE 43 PROFESSIONAL SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 125 TABLE 44 PROFESSIONAL SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 126 TABLE 45 GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 126 TABLE 46 GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 126 TABLE 47 CONSULTING SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 127 TABLE 48 CONSULTING SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 127 TABLE 49 DEPLOYMENT & INTEGRATION SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 128 TABLE 50 DEPLOYMENT & INTEGRATION SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 128 TABLE 51 SUPPORT & MAINTENANCE SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 129 TABLE 52 SUPPORT & MAINTENANCE SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 129 TABLE 53 GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 131 TABLE 54 GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 132 TABLE 55 RESOURCE DESCRIPTION FRAMEWORK: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 132 TABLE 56 RESOURCE DESCRIPTION FRAMEWORK: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 133 TABLE 57 PROPERTY GRAPH: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 133 TABLE 58 PROPERTY GRAPH: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 134 TABLE 59 GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 137 TABLE 60 GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 137 TABLE 61 DATA GOVERNANCE & MASTER DATA MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 138 TABLE 62 DATA GOVERNANCE & MASTER DATA MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 139 TABLE 63 DATA ANALYTICS & BUSINESS INTELLIGENCE: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 139 TABLE 64 DATA ANALYTICS & BUSINESS INTELLIGENCE: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 140 TABLE 65 KNOWLEDGE & CONTENT MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 140 TABLE 66 KNOWLEDGE & CONTENT MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 141 TABLE 67 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 141 TABLE 68 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 142 TABLE 69 PRODUCT & CONFIGURATION MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 142 TABLE 70 PRODUCT & CONFIGURATION MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 143 TABLE 71 INFRASTRUCTURE & ASSET MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 143 TABLE 72 INFRASTRUCTURE & ASSET MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 144 TABLE 73 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 144 TABLE 74 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 145 TABLE 75 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 146 TABLE 76 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 146 TABLE 77 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 147 TABLE 78 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 147 TABLE 79 OTHER APPLICATIONS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 148 TABLE 80 OTHER APPLICATIONS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 148 TABLE 81 GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 151 TABLE 82 GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 152 TABLE 83 BANKING, FINANCIAL SERVICES, AND INSURANCE: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 153 TABLE 84 BANKING, FINANCIAL SERVICES, AND INSURANCE: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 153 TABLE 85 RETAIL & ECOMMERCE: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 161 TABLE 86 RETAIL & ECOMMERCE: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 161 TABLE 87 TELECOM & TECHNOLOGY: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 167 TABLE 88 TELECOM & IT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 167 TABLE 89 HEALTHCARE, LIFESCIENCES, AND PHARMACEUTICALS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 172 TABLE 90 HEALTHCARE, LIFESCIENCES, AND PHARMACEUTICALS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 172 TABLE 91 GOVERNMENT & PUBLIC SECTOR: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 177 TABLE 92 GOVERNMENT & PUBLIC SECTOR: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 178 TABLE 93 MANUFACTURING & AUTOMOTIVE: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 181 TABLE 94 MANUFACTURING & AUTOMOTIVE: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 182 TABLE 95 MEDIA & ENTERTAINMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 186 TABLE 96 MEDIA & ENTERTAINMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 187 TABLE 97 ENERGY & UTILITIES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 192 TABLE 98 ENERGY & UTILITIES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 192 TABLE 99 TRAVEL & HOSPITALITY: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 196 TABLE 100 TRAVEL & HOSPITALITY: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 197 TABLE 101 TRANSPORTATION & LOGISTICS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 200 TABLE 102 TRANSPORTATION & LOGISTICS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 200 TABLE 103 OTHER VERTICALS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 203 TABLE 104 OTHER VERTICALS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 203 TABLE 105 GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION) 205 TABLE 106 GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION) 205 TABLE 107 NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 207 TABLE 108 NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 208 TABLE 109 NORTH AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 208 TABLE 110 NORTH AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 208 TABLE 111 NORTH AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 208 TABLE 112 NORTH AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 209 TABLE 113 NORTH AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 209 TABLE 114 NORTH AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 209 TABLE 115 NORTH AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 209 TABLE 116 NORTH AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 210 TABLE 117 NORTH AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 210 TABLE 118 NORTH AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 210 TABLE 119 NORTH AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 211 TABLE 120 NORTH AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 211 TABLE 121 NORTH AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 212 TABLE 122 NORTH AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 212 TABLE 123 NORTH AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2019–2023 (USD MILLION) 213 TABLE 124 NORTH AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 213 TABLE 125 US: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 214 TABLE 126 US: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 214 TABLE 127 US: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 214 TABLE 128 US: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 214 TABLE 129 US: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 214 TABLE 130 US: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 215 TABLE 131 US: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 215 TABLE 132 US: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 215 TABLE 133 US: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 215 TABLE 134 US: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 216 TABLE 135 US: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 216 TABLE 136 US: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 216 TABLE 137 US: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 217 TABLE 138 US: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 217 TABLE 139 US: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 218 TABLE 140 US: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 218 TABLE 141 EUROPE: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 220 TABLE 142 EUROPE: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 220 TABLE 143 EUROPE: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 220 TABLE 144 EUROPE: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 221 TABLE 145 EUROPE: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 221 TABLE 146 EUROPE: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 221 TABLE 147 EUROPE: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 221 TABLE 148 EUROPE: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 221 TABLE 149 EUROPE: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 222 TABLE 150 EUROPE: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 222 TABLE 151 EUROPE: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 222 TABLE 152 EUROPE: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 222 TABLE 153 EUROPE: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 223 TABLE 154 EUROPE: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 223 TABLE 155 EUROPE: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 224 TABLE 156 EUROPE: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 224 TABLE 157 EUROPE: GRAPH DATABASE MARKET, BY COUNTRY, 2019–2023 (USD MILLION) 225 TABLE 158 EUROPE: GRAPH DATABASE MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 225 TABLE 159 UK: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 226 TABLE 160 UK: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 226 TABLE 161 UK: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 226 TABLE 162 UK: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 226 TABLE 163 UK: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 226 TABLE 164 UK: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 227 TABLE 165 UK: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 227 TABLE 166 UK: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 227 TABLE 167 UK: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 227 TABLE 168 UK: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 228 TABLE 169 UK: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 228 TABLE 170 UK: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 228 TABLE 171 UK: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 228 TABLE 172 UK: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 229 TABLE 173 UK: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 229 TABLE 174 UK: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 230 TABLE 175 ITALY: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 230 TABLE 176 ITALY: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 231 TABLE 177 ITALY: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 231 TABLE 178 ITALY: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 231 TABLE 179 ITALY: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 231 TABLE 180 ITALY: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 232 TABLE 181 ITALY: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 232 TABLE 182 ITALY: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 232 TABLE 183 ITALY: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 232 TABLE 184 ITALY: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 233 TABLE 185 ITALY: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 233 TABLE 186 ITALY: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 233 TABLE 187 ITALY: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 233 TABLE 188 ITALY: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 234 TABLE 189 ITALY: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 234 TABLE 190 ITALY: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 235 TABLE 191 ASIA PACIFIC: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 238 TABLE 192 ASIA PACIFIC: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 239 TABLE 193 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 239 TABLE 194 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 239 TABLE 195 ASIA PACIFIC: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 239 TABLE 196 ASIA PACIFIC: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 240 TABLE 197 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 240 TABLE 198 ASIA PACIFIC: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 240 TABLE 199 ASIA PACIFIC: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 240 TABLE 200 ASIA PACIFIC: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 241 TABLE 201 ASIA PACIFIC: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 241 TABLE 202 ASIA PACIFIC: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 241 TABLE 203 ASIA PACIFIC: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 241 TABLE 204 ASIA PACIFIC: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 242 TABLE 205 ASIA PACIFIC: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 242 TABLE 206 ASIA PACIFIC: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 243 TABLE 207 ASIA PACIFIC: GRAPH DATABASE MARKET, BY COUNTRY, 2019–2023 (USD MILLION) 243 TABLE 208 ASIA PACIFIC: GRAPH DATABASE MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 243 TABLE 209 CHINA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 244 TABLE 210 CHINA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 244 TABLE 211 CHINA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 245 TABLE 212 CHINA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 245 TABLE 213 CHINA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 245 TABLE 214 CHINA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 245 TABLE 215 CHINA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 245 TABLE 216 CHINA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 246 TABLE 217 CHINA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 246 TABLE 218 CHINA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 246 TABLE 219 CHINA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 246 TABLE 220 CHINA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 247 TABLE 221 CHINA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 247 TABLE 222 CHINA: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 248 TABLE 223 CHINA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 248 TABLE 224 CHINA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 249 TABLE 225 INDIA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 249 TABLE 226 INDIA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 250 TABLE 227 INDIA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 250 TABLE 228 INDIA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 250 TABLE 229 INDIA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 250 TABLE 230 INDIA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 250 TABLE 231 INDIA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 251 TABLE 232 INDIA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 251 TABLE 233 INDIA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 251 TABLE 234 INDIA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 251 TABLE 235 INDIA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 252 TABLE 236 INDIA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 252 TABLE 237 INDIA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 252 TABLE 238 INDIA: GRAPH DATABASE MARKET, BY APPLICATIONS, 2024–2030 (USD MILLION) 253 TABLE 239 INDIA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 253 TABLE 240 INDIA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 254 TABLE 241 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 256 TABLE 242 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 257 TABLE 243 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 257 TABLE 244 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 257 TABLE 245 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 257 TABLE 246 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 258 TABLE 247 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 258 TABLE 248 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 258 TABLE 249 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 258 TABLE 250 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 259 TABLE 251 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 259 TABLE 252 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 259 TABLE 253 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 260 TABLE 254 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 260 TABLE 255 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 261 TABLE 256 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 261 TABLE 257 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY COUNTRY, 2019–2023 (USD MILLION) 262 TABLE 258 MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 262 TABLE 259 MIDDLE EAST: GRAPH DATABASE MARKET, BY COUNTRY, 2019–2023 (USD MILLION) 262 TABLE 260 MIDDLE EAST: GRAPH DATABASE MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 263 TABLE 261 KSA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 263 TABLE 262 KSA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 263 TABLE 263 KSA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 264 TABLE 264 KSA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 264 TABLE 265 KSA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 264 TABLE 266 KSA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 264 TABLE 267 KSA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 264 TABLE 268 KSA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 265 TABLE 269 KSA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 265 TABLE 270 KSA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 265 TABLE 271 KSA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 265 TABLE 272 KSA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 265 TABLE 273 KSA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 266 TABLE 274 KSA: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 266 TABLE 275 KSA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 267 TABLE 276 KSA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 267 TABLE 277 LATIN AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 270 TABLE 278 LATIN AMERICA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 270 TABLE 279 LATIN AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 271 TABLE 280 LATIN AMERICA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 271 TABLE 281 LATIN AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 271 TABLE 282 LATIN AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 271 TABLE 283 LATIN AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 272 TABLE 284 LATIN AMERICA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 272 TABLE 285 LATIN AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 272 TABLE 286 LATIN AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 272 TABLE 287 LATIN AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 273 TABLE 288 LATIN AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 273 TABLE 289 LATIN AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 273 TABLE 290 LATIN AMERICA: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 274 TABLE 291 LATIN AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 274 TABLE 292 LATIN AMERICA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 275 TABLE 293 LATIN AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2019–2023 (USD MILLION) 275 TABLE 294 LATIN AMERICA: GRAPH DATABASE MARKET, BY COUNTRY, 2024–2030 (USD MILLION) 275 TABLE 295 BRAZIL: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION) 276 TABLE 296 BRAZIL: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION) 276 TABLE 297 BRAZIL: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION) 276 TABLE 298 BRAZIL: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION) 277 TABLE 299 BRAZIL: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION) 277 TABLE 300 BRAZIL: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION) 277 TABLE 301 BRAZIL: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION) 277 TABLE 302 BRAZIL: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION) 277 TABLE 303 BRAZIL: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION) 278 TABLE 304 BRAZIL: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION) 278 TABLE 305 BRAZIL: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION) 278 TABLE 306 BRAZIL: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION) 278 TABLE 307 BRAZIL: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION) 279 TABLE 308 BRAZIL: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION) 279 TABLE 309 BRAZIL: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION) 280 TABLE 310 BRAZIL: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION) 280 TABLE 311 OVERVIEW OF STRATEGIES DEPLOYED BY KEY GRAPH DATABASE MARKET PLAYERS, 2021–2024 282 TABLE 312 GRAPH DATABASE MARKET: DEGREE OF COMPETITION 284 TABLE 313 GRAPH DATABASE MARKET: OFFERING FOOTPRINT 289 TABLE 314 GRAPH DATABASE MARKET: MODEL TYPE FOOTPRINT 290 TABLE 315 GRAPH DATABASE MARKET: APPLICATION FOOTPRINT 291 TABLE 316 GRAPH DATABASE MARKET: VERTICAL FOOTPRINT 291 TABLE 317 GRAPH DATABASE MARKET: REGION FOOTPRINT 292 TABLE 318 GRAPH DATABASE MARKET: LIST OF KEY STARTUPS/SMES 295 TABLE 319 COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES 296 TABLE 320 GRAPH DATABASE: PRODUCT LAUNCHES AND ENHANCEMENTS, SEPTEMBER 2022–OCTOBER 2024 297 TABLE 321 GRAPH DATABASE MARKET: DEALS, JANUARY 2023–NOVEMBER 2024 299 TABLE 322 NEO4J: COMPANY OVERVIEW 303 TABLE 323 NEO4J: PRODUCTS/SOLUTIONS/SERVICES OFFERED 303 TABLE 324 NEO4J: PRODUCT LAUNCHES AND ENHANCEMENTS 305 TABLE 325 NEO4J: DEALS 305 TABLE 326 AMAZON WEB SERVICES: COMPANY OVERVIEW 307 TABLE 327 AMAZON WEB SERVICES: PRODUCTS/SOLUTIONS/SERVICES OFFERED 308 TABLE 328 AMAZON WEB SERVICES: PRODUCT LAUNCHES AND ENHANCEMENTS 308 TABLE 329 AMAZON WEB SERVICES: DEALS 309 TABLE 330 TIGERGRAPH: COMPANY OVERVIEW 311 TABLE 331 TIGERGRAPH: PRODUCTS/SOLUTIONS/SERVICES OFFERED 311 TABLE 332 TIGERGRAPH: PRODUCT LAUNCHES AND ENHANCEMENTS 312 TABLE 333 TIGERGRAPH: DEALS 313 TABLE 334 RELATIONALAI: COMPANY OVERVIEW 315 TABLE 335 RELATIONALAI: PRODUCTS/SOLUTIONS/SERVICES OFFERED 315 TABLE 336 RELATIONALAI: PRODUCT LAUNCHES AND ENHANCEMENTS 316 TABLE 337 GRAPHWISE: COMPANY OVERVIEW 317 TABLE 338 GRAPHWISE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 317 TABLE 339 GRAPHWISE: PRODUCT LAUNCHES AND ENHANCEMENTS 317 TABLE 340 IBM: COMPANY OVERVIEW 319 TABLE 341 IBM: PRODUCTS/SOLUTIONS/SERVICES OFFERED 320 TABLE 342 IBM: DEALS 321 TABLE 343 MICROSOFT: COMPANY OVERVIEW 322 TABLE 344 MICROSOFT: PRODUCTS/SOLUTIONS/SERVICES OFFERED 323 TABLE 345 MICROSOFT: DEALS 324 TABLE 346 ONTOTEXT: COMPANY OVERVIEW 325 TABLE 347 ONTOTEXT: PRODUCTS/SOLUTIONS/SERVICES OFFERED 325 TABLE 348 ONTOTEXT: PRODUCT LAUNCHES AND ENHANCEMENTS 327 TABLE 349 ONTOTEXT: DEALS 327 TABLE 350 STARDOG: COMPANY OVERVIEW 329 TABLE 351 STARDOG: PRODUCTS/SOLUTIONS/SERVICES OFFERED 329 TABLE 352 STARDOG: PRODUCT LAUNCHES AND ENHANCEMENTS 330 TABLE 353 STARDOG: DEALS 330 TABLE 354 ALTAIR: COMPANY OVERVIEW 331 TABLE 355 ALTAIR: PRODUCTS/SOLUTIONS/SERVICES OFFERED 332 TABLE 356 ALTAIR: PRODUCT LAUNCHES AND ENHANCEMENTS 333 TABLE 357 ALTAIR: DEALS 333 TABLE 358 ORACLE: COMPANY OVERVIEW 334 TABLE 359 ORACLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 335 TABLE 360 ORACLE: PRODUCT LAUNCHES AND ENHANCEMENTS 336 TABLE 361 PROGRESS SOFTWARE: COMPANY OVERVIEW 337 TABLE 362 PROGRESS SOFTWARE: PRODUCTS/SOLUTIONS/SERVICES OFFERED 338 TABLE 363 PROGRESS SOFTWARE: DEALS 338 TABLE 364 FRANZ INC: COMPANY OVERVIEW 339 TABLE 365 FRANZ INC: PRODUCTS/SOLUTIONS/SERVICES OFFERED 339 TABLE 366 FRANZ INC.: PRODUCT LAUNCHES AND ENHANCEMENTS 340 TABLE 367 DATASTAX: COMPANY OVERVIEW 341 TABLE 368 DATASTAX: PRODUCTS/SOLUTIONS/SERVICES OFFERED 341 TABLE 369 DATASTAX: PRODUCT LAUNCHES AND ENHANCEMENTS 342 TABLE 370 DATASTAX: DEALS 342 TABLE 371 CLOUD DATABASE AND DBAAS MARKET, BY COMPONENT, 2018–2022 (USD MILLION) 352 TABLE 372 CLOUD DATABASE AND DBAAS MARKET, BY COMPONENT, 2023–2028 (USD MILLION) 352 TABLE 373 CLOUD DATABASE AND DBAAS MARKET, BY SERVICE, 2018–2022 (USD MILLION) 352 TABLE 374 CLOUD DATABASE AND DBAAS MARKET, BY SERVICE, 2023–2028 (USD MILLION) 352 TABLE 375 CLOUD DATABASE AND DBAAS MARKET, BY DEPLOYMENT MODEL, 2018–2022 (USD MILLION) 353 TABLE 376 CLOUD DATABASE AND DBAAS MARKET, BY DEPLOYMENT MODEL, 2023–2028 (USD MILLION) 353 TABLE 377 CLOUD DATABASE AND DBAAS MARKET, BY ORGANIZATION SIZE, 2018–2022 (USD MILLION) 353 TABLE 378 CLOUD DATABASE AND DBAAS MARKET, BY ORGANIZATION SIZE, 2023–2028 (USD MILLION) 354 TABLE 379 CLOUD DATABASE AND DBAAS MARKET, BY VERTICAL, 2018–2022 (USD MILLION) 354 TABLE 380 CLOUD DATABASE AND DBAAS MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 355 TABLE 381 CLOUD DATABASE AND DBAAS MARKET, BY REGION, 2018–2022 (USD MILLION) 355 TABLE 382 VECTOR DATABASE MARKET, BY OFFERING, 2019–2022 (USD MILLION) 356 TABLE 383 VECTOR DATABASE MARKET, BY OFFERING, 2023–2028 (USD MILLION) 356 TABLE 384 VECTOR DATABASE MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION) 357 TABLE 385 VECTOR DATABASE MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION) 357 TABLE 386 VECTOR DATABASE MARKET, BY VERTICAL, 2019–2022 (USD MILLION) 358 TABLE 387 VECTOR DATABASE MARKET, BY REGION, 2019–2022 (USD MILLION) 358 TABLE 388 VECTOR DATABASE MARKET, BY REGION, 2023–2028 (USD MILLION) 359 TABLE 389 VECTOR DATABASE MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 359 _ FIGURE 1 GRAPH DATABASE MARKET: RESEARCH DESIGN 47 FIGURE 2 KEY DATA FROM SECONDARY SOURCES 48 FIGURE 3 TOP-DOWN APPROACH 51 FIGURE 4 APPROACH 1 (SUPPLY SIDE): REVENUE OF VENDORS IN GRAPH DATABASE MARKET 51 FIGURE 5 BOTTOM-UP APPROACH 52 FIGURE 6 DEMAND-SIDE ANALYSIS 52 FIGURE 7 BOTTOM-UP (SUPPLY SIDE) ANALYSIS: COLLECTIVE REVENUE FROM SOLUTIONS/SERVICES OF EMOTION AI MARKET 53 FIGURE 8 DATA TRIANGULATION 54 FIGURE 9 GRAPH DATABASE MARKET, 2024–2030 (USD MILLION) 57 FIGURE 10 GRAPH DATABASE MARKET, BY REGION (2024) 58 FIGURE 11 INCREASING RELIANCE ON REAL-TIME ANALYTICS FOR CRITICAL DECISION-MAKING ACROSS INDUSTRIES TO DRIVE MARKET 59 FIGURE 12 SOLUTIONS SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD 59 FIGURE 13 MANAGED SERVICES SEGMENT TO ACCOUNT FOR HIGHER CAGR DURING FORECAST PERIOD 60 FIGURE 14 DEPLOYMENT & INTEGRATION SERVICES SEGMENT TO ACCOUNT FOR LARGEST MARKET DURING FORECAST PERIOD 60 FIGURE 15 DATA GOVERNANCE & MASTER DATA MANAGEMENT SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE DURING FORECAST PERIOD 60 FIGURE 16 PROPERTY GRAPH TO ACCOUNT FOR LARGER MARKET SHARE DURING FORECAST PERIOD 61 FIGURE 17 BFSI SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE DURING FORECAST PERIOD 61 FIGURE 18 SOLUTIONS & PROPERTY GRAPH SEGMENTS TO ACCOUNT FOR SIGNIFICANT MARKET SHARES IN 2024 62 FIGURE 19 GRAPH DATABASE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES 63 FIGURE 20 EVOLUTION OF GRAPH DATABASE MARKET 70 FIGURE 21 GRAPH DATABASE MARKET: ECOSYSTEM ANALYSIS 72 FIGURE 22 GRAPH DATABASE MARKET: SUPPLY CHAIN ANALYSIS 81 FIGURE 23 GRAPH DATABASE MARKET: INVESTMENT AND FUNDING SCENARIO, 2020–2024 (USD MILLION) 82 FIGURE 24 USE CASES OF GENERATIVE AI IN GRAPH DATABASE MARKET 84 FIGURE 25 LIST OF MAJOR PATENTS FOR GRAPH DATABASE MARKET (2014–2024) 96 FIGURE 26 AVERAGE SELLING PRICE OF KEY PLAYERS, BY COUNTRY, 2023 103 FIGURE 27 GRAPH DATABASE MARKET: PORTER’S FIVE FORCES ANALYSIS 108 FIGURE 28 GRAPH DATABASE MARKET: TRENDS/DISRUPTIONS INFLUENCING CUSTOMER BUSINESS 110 FIGURE 29 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS 111 FIGURE 30 KEY BUYING CRITERIA FOR TOP THREE INDUSTRIES 112 FIGURE 31 SERVICES SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD 115 FIGURE 32 KNOWLEDGE GRAPH ENGINES SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD 116 FIGURE 33 MANAGED SERVICES TO GROW AT HIGHER CAGR DURING FORECAST PERIOD 122 FIGURE 34 SUPPORT & MAINTENANCE SERVICES TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 125 FIGURE 35 RESOURCE DESCRIPTION FRAMEWORK SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD 131 FIGURE 36 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 136 FIGURE 37 BFSI TO ACCOUNT FOR LARGEST MARKET DURING FORECAST PERIOD 151 FIGURE 38 NORTH AMERICA: GRAPH DATABASE MARKET SNAPSHOT 207 FIGURE 39 ASIA PACIFIC: GRAPH DATABASE MARKET SNAPSHOT 238 FIGURE 40 SHARE ANALYSIS OF LEADING COMPANIES IN GRAPH DATABASE MARKET, 2024 284 FIGURE 41 MARKET RANKING ANALYSIS OF TOP FIVE PLAYERS 286 FIGURE 42 REVENUE ANALYSIS OF KEY PLAYERS IN GRAPH DATABASE MARKET, 2019–2023 (USD MILLION) 287 FIGURE 43 GRAPH DATABASE MARKET: COMPANY EVALUATION MATRIX (KEY PLAYERS), 2024 288 FIGURE 44 GRAPH DATABASE MARKET: COMPANY FOOTPRINT 289 FIGURE 45 GRAPH DATABASE MARKET: COMPANY EVALUATION MATRIX (STARTUPS/SMES), 2024 294 FIGURE 46 BRAND COMPARISON 301 FIGURE 47 COMPANY VALUATION 302 FIGURE 48 FINANCIAL METRICS 302 FIGURE 49 AMAZON WEB SERVICES: COMPANY SNAPSHOT 307 FIGURE 50 IBM: COMPANY SNAPSHOT 320 FIGURE 51 MICROSOFT: COMPANY SNAPSHOT 323 FIGURE 52 ALTAIR: COMPANY SNAPSHOT 332 FIGURE 53 ORACLE: COMPANY SNAPSHOT 335 FIGURE 54 PROGRESS SOFTWARE: COMPANY SNAPSHOT 337