Report

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 Data�'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 Hastens 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