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

TABLE OF CONTENTS

1 INTRODUCTION

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

2 RESEARCH METHODOLOGY

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

3 EXECUTIVE SUMMARY

4 PREMIUM INSIGHTS

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

5 MARKET OVERVIEW AND INDUSTRY TRENDS

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

6 GRAPH DATABASE MARKET, BY OFFERING

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

7 GRAPH DATABASE MARKET, BY MODEL TYPE

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

8 GRAPH DATABASE MARKET, BY APPLICATION

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

9 GRAPH DATABASE MARKET, BY VERTICAL

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

10 GRAPH DATABASE MARKET, BY REGION

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

11 COMPETITIVE LANDSCAPE

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

12 COMPANY PROFILES

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

13 ADJACENT AND RELATED MARKETS

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

14 APPENDIX

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

List of Tables

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

List of Figures

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