グラフデータベース市場 – 2030年までの世界予測

Report Overview

The Graph Database market is estimated at USD 507.6 million in 2024 to USD 2,143.0 million by 2030, at a Compound Annual Growth Rate (CAGR) of 27.1%.

グラフデータベース市場は、27.1% の年平均成長率 (CAGR) で、2024 年に 5 億 760 万米ドル、2030 年までに 21 億 4,300 万米ドルに達すると推定されています。

Graph databases are at the forefront of the rise of AI and ML by making it possible to analyze data more accurately and with deeper insights. Graph databases handle interconnected data very well, and this is what enables AI/ML models to find more profound relationships and hidden patterns that traditional systems might miss. Complex data structures are supported by graph databases, improving predictive accuracy and making them indispensable in applications such as fraud detection, personalized recommendations, and customer insights. With AI and ML advancement, graph databases are available to support massive datasets so that the predictability would be higher, and the data-driven decisions could be quite reliable.

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

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

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

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

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

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

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

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

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

Research Coverage

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

Key Benefits of Buying the Report

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

The report provides insights on the following pointers:

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

• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Graph Database market.
• Market Development: The report provides comprehensive information about lucrative markets and analyses the Graph Database market across various regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the Graph Database market.
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading include IBM Corporation (US), Oracle (US), Microsoft Corporation (US), AWS (US), Neo4j (US), RelationalAI (US), Progress Software (US), TigerGraph (US), Stardog (US), Datastax (US), Franz Inc (US), Ontotext (Bulgaria), Openlink Software (US), Dgraph Labs (US), Graphwise (US), Altair (US), Bitnine ( South Korea) ArangoDB (US),  Fluree (US), Blazegraph (US), Memgraph UK),  Objectivity (US), GraphBase (Australia), Graph Story (US), Oxford Semantic Tecnologies (UK), and FalkorDB (Israel).

Table of Contents

1               INTRODUCTION              42

1.1           STUDY OBJECTIVES       42

1.2           MARKET DEFINITION   42

1.3           STUDY SCOPE   43

1.3.1        MARKET SEGMENTATION           43

1.3.2        INCLUSIONS AND EXCLUSIONS 44

1.3.3        YEARS CONSIDERED      44

1.4           CURRENCY CONSIDERED            45

1.5           STAKEHOLDERS               45

1.6           SUMMARY OF CHANGES               46

2               RESEARCH METHODOLOGY       47

2.1           RESEARCH DATA              47

2.1.1        SECONDARY DATA          48

2.1.1.1    Key data from secondary sources       48

2.1.2        PRIMARY DATA 49

2.1.2.1    Primary interviews with experts         49

2.1.2.2    Breakdown of primary interviews      49

2.1.2.3    Key industry insights           50

2.2           MARKET SIZE ESTIMATION         50

2.2.1        TOP-DOWN APPROACH                50

2.2.1.1    Supply-side analysis             51

2.2.2        BOTTOM-UP APPROACH              51

2.2.2.1    Demand-side analysis          52

2.3           DATA TRIANGULATION                54

2.4           RESEARCH ASSUMPTIONS           55

2.5           RESEARCH LIMITATIONS             56

2.6           RISK ASSESSMENT           56

3               EXECUTIVE SUMMARY  57

4               PREMIUM INSIGHTS       59

4.1           OPPORTUNITIES FOR KEY PLAYERS IN GRAPH DATABASE MARKET       59

4.2           GRAPH DATABASE MARKET, BY OFFERING         59

4.3           GRAPH DATABASE MARKET, BY SERVICE              60

4.4           GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE               60

4.5           GRAPH DATABASE MARKET, BY APPLICATION  60

4.6           GRAPH DATABASE MARKET, BY MODEL TYPE   61

4.7           GRAPH DATABASE MARKET, BY VERTICAL          61

4.8           NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING AND MODEL TYPE    62

5               MARKET OVERVIEW AND INDUSTRY TRENDS    63

5.1           MARKET DYNAMICS       63

5.1.1        DRIVERS               63

5.1.1.1    Increasing Gen AI applications          63

5.1.1.2    Surging need for incorporating real-time big data mining with result visualization                64

5.1.1.3    Rising demand for solutions that can process low-latency queries                 64

5.1.1.4    Rapid use of virtualization for big data analytics             65

5.1.1.5    Growing demand for semantic search across unstructured content   65

5.1.2        RESTRAINTS      65

5.1.2.1    Lack of standardization and programming ease               65

5.1.2.2    Rapid proliferation of data management technologies   65

5.1.2.3    High implementation costs 66

5.1.3        OPPORTUNITIES              66

5.1.3.1    Data unification and rapid proliferation of knowledge graphs                 66

5.1.3.2    Provision of semantic knowledgeable graphs to address complex-scientific research 66

5.1.3.3    Emphasis on emergence of open knowledge networks   67

5.1.4        CHALLENGES    67

5.1.4.1    Lack of technical expertise  67

5.1.4.2    Difficulty in demonstrating benefits of knowledge graphs in single application or use case              68

5.2           BEST PRACTICES IN GRAPH DATABASE MARKET                 68

5.2.1        VALIDATION OF USE CASES        68

5.2.2        AVOIDANCE OF INEFFICIENT TRAVERSAL PATTERNS                 68

5.2.3        USAGE OF DATA MODELING      69

5.2.4        ENSURING DATA CONSISTENCY               69

5.2.5        PARTITIONING OF COSMOS DB                69

5.2.6        FOSTERING TEAM EXPERTISE IN GRAPH DATABASE                 69

5.3           EVOLUTION OF GRAPH DATABASE MARKET      70

5.4           ECOSYSTEM ANALYSIS  72

5.5           CASE STUDY ANALYSIS 73

5.5.1        NEO4J-POWERED KNOWLEDGE GRAPH HELPED INTUIT PROVIDE REAL-TIME INSIGHTS AND FACILITATE SWIFT RESPONSES TO SECURITY THREATS       73

5.5.2        WESTJET IMPROVED ITS CUSTOMER BOOKING EXPERIENCE BY INTEGRATING NEO4J’S GRAPH TECHNOLOGY                 74

5.5.3        NEWDAY IMPROVED FRAUD DETECTION CAPABILITIES WITH TIGERGRAPH CLOUD        74

5.5.4        CYBER RESILIENCE LEADER LEVERAGED TIGERGRAPH TO ELEVATE ITS NEXT-GENERATION CLOUD-BASED CYBERSECURITY SERVICES         75

5.5.5        XBOX CHOSE TIGERGRAPH TO EMPOWER ITS GRAPH ANALYTICS CAPABILITIES           76

5.5.6        DGRAPH’S CUTTING-EDGE DATABASE SOLUTION ENABLED MOONCAMP TO STREAMLINE ITS BACKEND OPERATIONS     76

5.5.7        NEO4J’S GRAPH DATABASE AND APPLICATION PLATFORM HELPED KERBEROS CONTROL COMPLEX LEGAL OBLIGATIONS  77

5.5.8        BLAZEGRAPH HELPED YAHOO7 DRIVE NATIVE REAL-TIME ADVERTISING USING GRAPH QUERIES      78

5.5.9        NEO4J ENABLED ICU’S TEAM TO VISUALIZE AND ANALYZE CONNECTIONS BETWEEN ELEMENTS OF PANAMA PAPERS LEAKS  78

5.5.10      NEO4J’S GRAPH TECHNOLOGY HELPED U.S. ARMY BY TRACKING AND ANALYZING EQUIPMENT MAINTENANCE                 79

5.5.11      JAGUAR LAND ROVER ACHIEVED REDUCED INVENTORY COSTS AND HIGHER PROFITABILITY USING TIGERGRAPH’S SOLUTION          79

5.5.12      MACY’S REDUCED CATALOG DATA REFRESH TIME BY SIX-FOLD             80

5.5.13      METAPHACTS AND ONTOTEXT ENABLED GLOBAL PHARMA COMPANY TO BOOST R&D KNOWLEDGE DISCOVERY                 80

5.6           SUPPLY CHAIN ANALYSIS             81

5.7           INVESTMENT AND FUNDING SCENARIO               82

5.8           IMPACT OF GENERATIVE AI ON GRAPH DATABASE MARKET               82

5.8.1        USE CASES OF GENERATIVE AI IN GRAPH DATABASE                 83

5.8.1.1    Neo4j LLM Knowledge Graph Builder enabled users to extract nodes and relationships from unstructured text               83

5.8.1.2    Data²’s flagship analytics platform, reView, delivered powerful insights by integrating customer data into Neo4j-backed knowledge graph                 83

5.8.1.3    JPMorgan leveraged LLMs to detect fraudulent activities                 83

5.8.1.4    Mastercard leveraged GenAI capabilities to strengthen its fraud detection system   84

5.9           TECHNOLOGY ROADMAP OF GRAPH DATABASE MARKET               85

5.10         REGULATORY LANDSCAPE         86

5.10.1      REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS             86

5.10.2      KEY REGULATIONS         89

5.10.2.1  North America      89

5.10.2.1.1                SCR 17: Artificial Intelligence Bill (California)                 89

5.10.2.1.2                S1103: Artificial Intelligence Automated Decision Bill (Connecticut)        90

5.10.2.1.3                National Artificial Intelligence Initiative Act (NAIIA)                 90

5.10.2.1.4                The Artificial Intelligence and Data Act (AIDA) – Canada   90

5.10.2.1.5                Cybersecurity Maturity Model Certification (CMMC) (USA)    91

5.10.2.2  Europe   91

5.10.2.2.1                The European Union (EU) – Artificial Intelligence Act (AIA)      91

5.10.2.2.2                General Data Protection Regulation (Europe)                 91

5.10.2.3  Asia Pacific            92

5.10.2.3.1                Interim Administrative Measures for Generative Artificial Intelligence Services (China)             92

5.10.2.3.2                National AI Strategy (Singapore)      92

5.10.2.3.3                Hiroshima AI Process Comprehensive Policy Framework (Japan)              93

5.10.2.4  Middle East & Africa            94

5.10.2.4.1                National Strategy for Artificial Intelligence (UAE)                 94

5.10.2.4.2                National Artificial Intelligence Strategy (Qatar)                 94

5.10.2.4.3                AI Ethics Principles and Guidelines (Dubai)  94

5.10.2.5  Latin America       95

5.10.2.5.1                The Santiago Declaration (Chile)     95

5.10.2.5.2                Brazilian Artificial Intelligence Strategy-EBIA                 95

5.11         PATENT ANALYSIS          96

5.11.1      METHODOLOGY              96

5.11.2      LIST OF MAJOR PATENTS             97

5.12         TECHNOLOGY ANALYSIS             98

5.12.1      KEY TECHNOLOGIES     99

5.12.1.1  Semantic Web       99

5.12.1.2  Generative AI and natural language processing               99

5.12.1.3  Graph RAG           99

5.12.2      COMPLEMENTARY TECHNOLOGIES       100

5.12.2.1  Cloud computing  100

5.12.2.2  AI and ML              100

5.12.2.3  Big data & analytics              101

5.12.2.4  Graph neural networks        101

5.12.2.5  Vector databases and full-text search engines 101

5.12.2.6  Multimodal databases          101

5.12.3      ADJACENT TECHNOLOGIES       102

5.12.3.1  Digital twin            102

5.12.3.2  IoT          102

5.12.3.3  Blockchain             102

5.12.3.4  Edge computing   102

5.13         PRICING ANALYSIS          103

5.13.1      AVERAGE SELLING PRICE OF KEY PLAYERS, BY COUNTRY, 2023 103

5.13.2      INDICATIVE PRICING ANALYSIS, BY KEY PLAYER, 2023                 104

5.14         KEY CONFERENCES AND EVENTS, 2024–2025        106

5.15         PORTER’S FIVE FORCES ANALYSIS           108

5.15.1      THREAT OF NEW ENTRANTS      109

5.15.2      THREAT OF SUBSTITUTES          109

5.15.3      BARGAINING POWER OF SUPPLIERS       109

5.15.4      BARGAINING POWER OF BUYERS             109

5.15.5      INTENSITY OF COMPETITIVE RIVALRY 109

5.16         TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS            110

5.17         KEY STAKEHOLDERS AND BUYING CRITERIA     111

5.17.1      KEY STAKEHOLDERS IN BUYING PROCESS           111

5.17.2      BUYING CRITERIA           112

6               GRAPH DATABASE MARKET, BY OFFERING         113

6.1           INTRODUCTION              114

6.1.1        OFFERING: GRAPH DATABASE MARKET DRIVERS                 114

6.2           SOLUTIONS        115

6.2.1        INCREASING NEED FOR ENHANCING PRODUCTIVITY AND MAINTAINING BUSINESS CONTINUITY TO DRIVE MARKET                 115

6.2.2        BY SOLUTION TYPE        117

6.2.2.1    Graph extensions 117

6.2.2.2    Graph processing engines   118

6.2.2.3    Native graph database         119

6.2.2.4    Knowledge graph engines   119

6.2.3        BY DEPLOYMENT MODE              120

6.2.3.1    Cloud      121

6.2.3.2    On-premises          121

6.3           SERVICES             122

6.3.1        MANAGED SERVICES      124

6.3.1.1    Specialized skills for maintaining and updating graph database solutions to drive market     124

6.3.2        PROFESSIONAL SERVICES            125

6.3.2.1    Consulting services               126

6.3.2.1.1 Integration of graph databases with analytics and virtualization frameworks to boost market                126

6.3.2.2    Deployment & integration services   127

6.3.2.2.1 Growing need to overcome system-related issues effectively to drive market          127

6.3.2.3    Support & maintenance services        128

6.3.2.3.1 Services provided for upgradation and maintenance of operating ecosystem post-implementation to fuel market growth 128

7               GRAPH DATABASE MARKET, BY MODEL TYPE   130

7.1           INTRODUCTION              131

7.1.1        MODEL TYPE: GRAPH DATABASE MARKET DRIVERS                 131

7.2           RESOURCE DESCRIPTION FRAMEWORK                132

7.2.1        NEED FOR INTELLIGENT DATA MANAGEMENT SOLUTIONS TO DRIVE DEMAND FOR GRAPH DATABASE                 132

7.3           PROPERTY GRAPH           133

7.3.1        INCREASING URGE TO FIND RELATIONSHIPS AMONG NUMEROUS ENTITIES TO BOOST MARKET          133

7.3.1.1    Labeled property graph       134

7.3.1.2    Typed property graph          134

8               GRAPH DATABASE MARKET, BY APPLICATION  135

8.1           INTRODUCTION              136

8.1.1        APPLICATION: GRAPH DATABASE MARKET DRIVERS                 136

8.2           DATA GOVERNANCE & MASTER DATA MANAGEMENT                 138

8.2.1        NEED FOR MANAGING, INTEGRATING, AND SECURING COMPLEX DATA RELATIONSHIPS TO DRIVE MARKET    138

8.3           DATA ANALYTICS & BUSINESS INTELLIGENCE  139

8.3.1        SUPERIOR QUERY PERFORMANCE FOR COMPLEX OPERATIONS TO BOOST MARKET           139

8.4           KNOWLEDGE & CONTENT MANAGEMENT          140

8.4.1        INTUITIVE AND DYNAMIC WAY OF ORGANIZING, CONNECTING, AND RETRIEVING INFORMATION TO FUEL MARKET GROWTH          140

8.5           VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY       141

8.5.1        PERSONALIZED, INTELLIGENT, AND CONTEXT-AWARE INTERACTIONS TO SUPPORT MARKET GROWTH             141

8.6           PRODUCT & CONFIGURATION MANAGEMENT 142

8.6.1        VISIBILITY INTO INTERDEPENDENCIES ACROSS TEAMS TO ENSURE TRACEABILITY AND BETTER DECISION-MAKING                 142

8.7           INFRASTRUCTURE & ASSET MANAGEMENT        143

8.7.1        MODELING AND ANALYSIS OF INTRICATE RELATIONSHIPS BETWEEN ASSETS TO DRIVE MARKET 143

8.8           PROCESS OPTIMIZATION & RESOURCE MANAGEMENT                 144

8.8.1        OPTIMIZE PROCESS BY ANALYZING COMPLEX, INTERCONNECTED DATA THROUGH GRAPH DATA SCIENCE                 144

8.9           RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING       145

8.9.1        IDENTIFICATION AND ASSESSMENT OF RISKS BY VISUALIZING CONNECTIONS TO BOOST MARKET           145

8.10         MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION                146

8.10.1      GRAPH DATABASES TO IMPROVE SALES EFFECTIVENESS AND CUSTOMER ENGAGEMENT             146

8.11         OTHER APPLICATIONS 147

9               GRAPH DATABASE MARKET, BY VERTICAL          149

9.1           INTRODUCTION              150

9.1.1        VERTICAL: GRAPH DATABASE MARKET DRIVERS                 150

9.2           BANKING, FINANCIAL SERVICES, AND INSURANCE                 152

9.2.1        GROWING ADOPTION OF FINANCIAL STANDARDS AND COMPLIANCE WITH REGULATIONS TO DRIVE MARKET                 152

9.2.2        CASE STUDY      153

9.2.2.1    Fraud detection & risk management 153

9.2.2.1.1 Neo4j-powered system helped BNP Paribas Personal Finance achieve a 20% reduction in fraud      153

9.2.2.1.2 Zurich Switzerland enhanced fraud investigations with Neo4j                 154

9.2.2.2    Anti-money laundering       154

9.2.2.2.1 US bank leveraged TigerGraph’s graph analytics capabilities to detect intricate money laundering network      154

9.2.2.2.2 KERBEROS enhanced money laundering capabilities with Neo4j’s graph database and Structr application platform               155

9.2.2.3    Identity & access management           155

9.2.2.3.1 Ability for mapping and querying intricate relationships to drive market    155

9.2.2.4    Risk management 155

9.2.2.4.1 Rising usage of graph database tools and services for enhancing risk intelligence capabilities to aid market growth           155

9.2.2.4.2 UBS implemented Neo4j’s graph database to improve its data lineage and governance       156

9.2.2.4.3 Marionete integrated its various databases with the Neo4j graph database, enabling it to reduce credit risk and influence charges 156

9.2.2.5    Data integration & governance           156

9.2.2.5.1 Optimizing data security and privacy                156

9.2.2.5.2 Real-time monitoring and audit         157

9.2.2.6    Know Your Customer (KYC) process               157

9.2.2.6.1 Neo4j’s graph technology helped institutions save time in compliance workflows          157

9.2.2.7    Operational resilience for bank IT systems      158

9.2.2.7.1 Stardog’s platform allowed for easy navigation through interconnected data, helping organizations identify dependencies and analyze systemic risks          158

9.2.2.8    Regulatory compliance        158

9.2.2.8.1 Streamlining regulatory compliance with RDFoc            158

9.2.2.9    Customer 360° view           159

9.2.2.9.1 Unified, holistic perspective of each customer by integrating data from multiple sources          159

9.2.2.10  Market analysis & trend detection     159

9.2.2.10.1                Graph databases to help gain deeper insights into organizations’ complex relationships and enhance customer experiences                 159

9.2.2.11  Policy impact analysis          160

9.2.2.11.1                Real-time updates to ensure quick adaptability to changing regulations, minimizing disruptions, and maintaining operational efficiency           160

9.2.2.12  Self-service data and digital asset discovery     160

9.2.2.12.1                Empowerment of users without technical expertise to independently find, explore, and handle data fosters market growth                 160

9.2.2.13  Customer support 160

9.2.2.13.1                Quick issue resolution, personalized responses, and customized recommendations to boost market                160

9.3           RETAIL & ECOMMERCE 160

9.3.1        INCREASING NEED FOR IDENTIFYING CUSTOMER BEHAVIOR IN

REAL-TIME TO DRIVE MARKET 160

9.3.2        CASE STUDY      162

9.3.2.1    Fraud detection in eCommerce          162

9.3.2.1.1 PayPal leveraged real-time graph databases and graph analysis to combat fraud effectively      162

9.3.2.2    Dynamic pricing optimization            162

9.3.2.2.1 Deployment of Neo4j-based system significantly improved efficiency and scalability in Marriott’s pricing operations              162

9.3.2.3    Personalized product recommendations           162

9.3.2.3.1 Neo4j’s graph-based approach allowed Walmart to enhance online shopping experience and maintain competitive edge         163

9.3.2.3.2 AboutYou transformed personalized shopping with ArangoDB, boosting engagement and efficiency 163

9.3.2.4    Market basket analysis         163

9.3.2.4.1 Analyzing relationship between product pricing and consumer behavior to support development of optimized pricing strategies                 163

9.3.2.5    Customer experience enhancement  163

9.3.2.5.1 Retailer achieved enhanced store operations and improved customer satisfaction with TigerGraph’s platform          164

9.3.2.6    Churn Prediction & Prevention          164

9.3.2.6.1 Predicting churn helps companies identify customers at risk of leaving    164

9.3.2.7    Social media influence on buying behavior      164

9.3.2.7.1 Increasing need for understanding and leveraging dynamics of social media influencing consumer-buying decisions to fuel market growth                 164

9.3.2.8    Product Configuration & Recommendation     165

9.3.2.8.1 Neo4j’s graph database enabled eBay achieve seamless and intelligent product discovery experience           165

9.3.2.9    Customer Segmentation & Targeting                165

9.3.2.9.1 Targeted advertising and personalized shopping experiences to help drive sales     165

9.3.2.10  Customer 360° View          165

9.3.2.10.1                Tracking of customer’s purchase behavior to aid market growth    165

9.3.2.10.2                Neo4j empowered Hästens to build comprehensive 360-degree view of its data, operations, customers, and partners         166

9.3.2.11  Review & reputation management     166

9.3.2.11.1                To enhance and manage customer review to protect reputation               166

9.3.2.12  Customer Support                166

9.3.2.12.1                To improved customer satisfaction, faster response times, and stronger customer loyalty 166

9.4           TELECOM & TECHNOLOGY         166

9.4.1        SURGING DEMAND FOR IMPROVED SERVICES TO DRIVE MARKET 166

9.4.2        CASE STUDY      168

9.4.2.1    Network optimization & management               168

9.4.2.1.1 Australia’s leading carrier enhanced network monitoring and security with ArangoDB      168

9.4.2.2    Data integration & governance           168

9.4.2.2.1 D&B achieved significant revenue growth and expanded its customer base using Neo4j’s graph technology                168

9.4.2.3    IT asset management           168

9.4.2.3.1 Orange leveraged ArangoDB to build digital twin platform for enhanced process optimization          168

9.4.2.4    Network security analysis   169

9.4.2.4.1 Zeta Global chose Amazon Neptune for its scalability, elasticity, and cost-effectiveness          169

9.4.2.5    IoT device management & connectivity            169

9.4.2.5.1 BT Group leveraged Neo4j to deliver lightning-fast inventory management and streamline operations            169

9.4.2.5.2 Amazon Neptune’s capabilities empowered telecom & IT sectors to achieve enhanced device orchestration and seamless integration of IoT data         169

9.4.2.6    Self-service data & digital asset discovery         170

9.4.2.6.1 Optimizing telecom operations with self-service data and digital asset discovery      170

9.4.2.7    Identity & access management           170

9.4.2.7.1 Interconnected data model helped Telenor Norway eliminate performance bottlenecks and deliver faster insights       170

9.4.2.7.2 Enhanced identity management and recommendations with TigerGraph            170

9.4.2.8    Metadata enrichment           170

9.4.2.8.1 Enhancing document findability with metadata enrichment at Cisco       170

9.4.2.9    Service incident management             171

9.4.2.9.1 Proactive incident management with Neo4j-powered intelligent network analysis tool            171

9.5           HEALTHCARE, LIFE SCIENCES, AND PHARMACEUTICALS       171

9.5.1        NEED FOR IMPROVED PATIENT-CENTRIC EXPERIENCE AND REAL-TIME TREATMENT TO DRIVE MARKET           171

9.5.2        CASE STUDY      173

9.5.2.1    Drug discovery & development          173

9.5.2.1.1 Novartis harnessed cutting-edge biological insights for drug discovery                173

9.5.2.1.2 Revolutionizing biodiversity insights with graph-powered knowledge mapping             173

9.5.2.2    Clinical trial management   173

9.5.2.2.1 Neo4j’s knowledge graph-based application helped Novo Nordisk achieve end-to-end consistency and increased automation           173

9.5.2.3    Medical claims processing  174

9.5.2.3.1 UnitedHealth improved medical claim processing with graph databases                174

9.5.2.4    Clinical intelligence              174

9.5.2.4.1 UnitedHealth Group deployed graph database to enhance patient care         174

9.5.2.4.2 Dooloo turned to Neo4j’s Graph Data Platform for delivering personalized, data-driven insights     174

9.5.2.5    Healthcare network provider analysis                174

9.5.2.5.1 Boston Scientific utilized Neo4j’s Graph Data Science Library to simplify complex medical supply chain analysis               175

9.5.2.5.2 Amgen enhanced data analysis and scalability with TigerGraph for healthcare insights         175

9.5.2.6    Customer support 175

9.5.2.6.1 Exact Sciences enhanced customer engagement with implementation of Doctor-and-Product 360 solution powered by TigerGraph            175

9.5.2.6.2 Optimizing healthcare customer support with Graph RAG-powered chatbots 176

9.5.2.7    Patient journey & care pathway analysis           176

9.5.2.7.1 Neo4j’s scalable and interconnected data model empowered Care-for-Rare to transform vast, siloed datasets into actionable medical insights  176

9.5.2.8    Self-service data & digital asset discovery         176

9.5.2.8.1 Stardog-powered enterprise knowledge graph enabled Boehringer Ingelheim to address its challenge of siloed research data                 176

9.6           GOVERNMENT & PUBLIC SECTOR            177

9.6.1        RISING NEED FOR ENHANCED DATA SECURITY AND ADVANCED INTELLIGENCE TO DRIVE MARKET                 177

9.6.2        CASE STUDY      178

9.6.2.1    Government service optimization      178

9.6.2.1.1 Empowering government agencies with Stardog Voicebox for seamless data insights and enhanced decision-making  178

9.6.2.2    Legislative & regulatory analysis        178

9.6.2.2.1 Streamlining legislative and regulatory analysis with graph databases for enhanced compliance and decision-making             178

9.6.2.3    Crisis management& disaster response planning            179

9.6.2.3.1 Strengthening cybersecurity with graph databases for proactive threat detection and risk management              179

9.6.2.4    Environmental impact analysis & ESG              179

9.6.2.4.1 NASA leveraged Stardog’s Enterprise Knowledge Platform, enabling seamless integration and analysis      179

9.6.2.5    Social network analysis for security and law enforcement                 179

9.6.2.5.1 Global financial institution leveraged Neo4j and Linkurious Enterprise (LE) to enhance fraud detection    179

9.6.2.6    Policy impact analysis          180

9.6.2.6.1 Transforming information access at IDB with knowledge graphs                 180

9.6.2.7    Knowledge management     180

9.6.2.7.1 Neo4j’s graph database helped NASA leverage historical insights to reduce project timelines and prevent disasters           180

9.6.2.8    Data integration & governance           180

9.6.2.8.1 Transforming product lifecycle management with graph technology              180

9.7           MANUFACTURING & AUTOMOTIVE        181

9.7.1        GROWING NEED FOR EXTENDING FACTORY EQUIPMENT LIFESPAN AND REDUCING PRODUCTION RISK DELAYS TO BOOST GROWTH     181

9.7.2        CASE STUDY      182

9.7.2.1    Equipment management & predictive maintenance       182

9.7.2.1.1 Leveraging graph databases for flexible and robust operations                 182

9.7.2.2    Product lifecycle management            182

9.7.2.2.1 Japanese automotive manufacturer optimized product life cycle and validation with Neo4j-powered knowledge graph    182

9.7.2.3    Manufacturing process optimization 183

9.7.2.3.1 Optimizing manufacturing processes with Stardog Voicebox and Databricks for enhanced quality and efficiency               183

9.7.2.3.2 Ford enhanced manufacturing efficiency with TigerGraph                 183

9.7.2.4    Enhanced vehicle safety and reliability              183

9.7.2.4.1 Increase vehicle safety with advanced technologies and graph databases                183

9.7.2.5    Optimization of industrial processes 184

9.7.2.5.1 Enhancing smart manufacturing with Siemens’ knowledge graph and AI-driven automation   184

9.7.2.5.2 Optimizing automotive pricing and processes with Neo4j and AWS       184

9.7.2.6    Root cause analysis               184

9.7.2.6.1 Leveraging knowledge graphs for transparent and effective root cause analysis        184

9.7.2.7    Inventory management & demand forecasting                 185

9.7.2.7.1 Optimizing Inventory management with dynamic stock calculation and cost analysis               185

9.7.2.8    Service incident management             185

9.7.2.8.1 Improving service incident management with graph databases in manufacturing and automotive           185

9.7.2.9    Staff & resource allocation  185

9.7.2.9.1 Enhancing resource and staff allocation efficiency using graph databases                185

9.7.2.10  Product configuration & recommendation       186

9.7.2.10.1                Cox Automotive built identity graph using Amazon Neptune to connect and analyze large datasets of shopper information                 186

9.8           MEDIA & ENTERTAINMENT        186

9.8.1        DEMAND FOR MODELING-USER PREFERENCES AND CONTENT INTERACTIONS TO FOSTER MARKET GROWTH                 186

9.8.2        CASE STUDY      187

9.8.2.1    Content recommendation & personalization   187

9.8.2.1.1 Graph databases enable media companies to provide highly accurate content recommendations and personalized experiences                 187

9.8.2.1.2 Kickdynamic adopted TigerGraph on AWS Cloud to power its recommendation engine      187

9.8.2.1.3 Musimap adopted Neo4j graph database to offer personalized music recommendations      188

9.8.2.2    Social media influence analysis          188

9.8.2.2.1 Myntelligence optimized social media campaigns with TigerGraph’s real-time analytics        188

9.8.2.2.2 TigerGraph’s advanced analytics enable OpenCorporates to support complex investigative queries with real-time response times                 188

9.8.2.3    Content recommendation system      189

9.8.2.3.1 IppenDigital’s adoption of TigerGraph’s graph database technology helped deliver hyper-personalized content recommendations                 189

9.8.2.3.2 Netflix leveraged graph databases for personalization and scalability               189

9.8.2.4    User engagement analysis   189

9.8.2.4.1 Enabling enterprises to capture and dissect intricate associations among users          189

9.8.2.4.2 Graph technology powered personalized smart home automation for Xfinity               190

9.8.2.5    Copyright and licensing management               190

9.8.2.5.1 Enhancing license and copyright management in media & entertainment industry through graph database technology         190

9.8.2.6    Knowledge management     190

9.8.2.6.1 Graph technology to enhance collaboration and accelerate decision-making   190

9.8.2.7    Audience segmentation and targeting               191

9.8.2.7.1 Optimizing audience segmentation and targeting for maximum impact    191

9.8.2.8    Self-service data and digital asset discovery     191

9.8.2.8.1 Consistent metadata management, robust security, user training, and scalability required to handle growing volume of assets effectively                 191

9.9           ENERGY & UTILITIES     191

9.9.1        SURGING DEMAND FOR DECREASING OPERATIONAL RISKS AND COSTS TO DRIVE MARKET   191

9.9.2        CASE STUDY      192

9.9.2.1    Smart grid management      192

9.9.2.1.1 Adoption of graph database to manage complex relationships and interconnected data              192

9.9.2.2    Energy trading optimization               193

9.9.2.2.1 Unlocking efficient energy trading with graph database technology              193

9.9.2.3    Renewable energy integration & optimization 193

9.9.2.3.1 Graph databases to enhance visibility into entire energy ecosystem               193

9.9.2.4    Public Infrastructure Management    193

9.9.2.4.1 Enhancing public infrastructure management with graph databases                193

9.9.2.5    Customer Engagement And Billing  194

9.9.2.5.1 Ease billing process to improve customer satisfaction    194

9.9.2.6    Service incident management             194

9.9.2.6.1 Enxchange transformed energy grid management with graph-based digital twins for real-time insights and cost savings             194

9.9.2.7    Environmental impact analysis and ESG          195

9.9.2.7.1 Optimizing energy sustainability and environmental impact with graph databases    195

9.9.2.7.2 Integration of advanced technologies to enhance data management and insights   195

9.9.2.8    Railway asset management 195

9.9.2.8.1 Customized knowledge graphs enable smarter decision-making, predictive maintenance, and cost-effective operations   195

9.9.2.9    Staff and resource allocation               196

9.9.2.9.1 Optimizing staff and resource allocation for sustainable energy operations              196

9.10         TRAVEL & HOSPITALITY               196

9.10.1      FOCUS ON FOSTERING TRAVEL PLANS FOR BETTER CUSTOMER EXPERIENCES TO DRIVE MARKET EXPANSION                 196

9.10.2      CASE STUDY      197

9.10.2.1  Personalized travel recommendations               197

9.10.2.1.1                Revolutionizing personalized travel recommendations with graph databases            197

9.10.2.2  Dynamic pricing optimization            197

9.10.2.2.1                Transforming dynamic price management with graph databases                197

9.10.2.3  Customer journey mapping                 198

9.10.2.3.1                Customer journey mapping to give personalized recommendations 198

9.10.2.4  Booking and reservation management              198

9.10.2.4.1                Graph databases ensure seamless customer experiences and efficient operations       198

9.10.2.5  Customer experience management   198

9.10.2.5.1                Transforming customer experience with unified data and actionable insights        198

9.10.2.6  Product configuration and recommendation   199

9.10.2.6.1                Dynamic product configuration and personalized recommendations in travel and hospitality       199

9.11         TRANSPORTATION & LOGISTICS              199

9.11.1      RISING NEED FOR GAINING COMPLETE AND REAL-TIME VISIBILITY TO DRIVE MARKET       199

9.11.2      TRANSPORT FOR LONDON (TFL) REDUCED CONGESTION BY 10% USING DIGITAL TWIN POWERED BY NEO4J   199

9.11.3      USE CASES           200

9.11.3.1  Route optimization and fleet management       200

9.11.3.1.1                Careem achieved enhanced fraud detection with AWS                 200

9.11.3.1.2                Optimizing delivery routes and scaling logistics with precision data        201

9.11.3.2  Supply chain management  201

9.11.3.2.1                Transforming supply chains with Google Cloud and Neo4j      201

9.11.3.3  Asset tracking and management         201

9.11.3.3.1                Graph databases to model intricate relationships and dependencies between assets, locations, and stakeholders            201

9.11.3.4  Equipment maintenance and predictive maintenance    201

9.11.3.4.1                Optimizing equipment maintenance with predictive insights powered by graph databases                 201

9.11.3.5  Supply chain management  202

9.11.3.5.1                Revolutionizing supply chain visibility through real-time digital twin solutions   202

9.11.3.6  Vendor and supplier analysis              202

9.11.3.6.1                Graph database to enable comprehensive view of supply chain       202

9.11.3.7  Operational efficiency & decision-making       202

9.11.3.7.1                Optimizing delivery routes and scaling logistics with precision data        202

9.12         OTHER VERTICALS         203

10            GRAPH DATABASE MARKET, BY REGION              204

10.1         INTRODUCTION              205

10.2         NORTH AMERICA             206

10.2.1      NORTH AMERICA: MACROECONOMIC OUTLOOK                 206

10.2.2      US           213

10.2.2.1  Increasing use of graph databases in medical science and political campaigns to foster market growth   213

10.2.3      CANADA               219

10.2.3.1  Stringent data regulation and extensive applications of graph databases in research to drive growth                219

10.3         EUROPE               219

10.3.1      EUROPE: MACROECONOMIC OUTLOOK               219

10.3.2      UK          225

10.3.2.1  Government initiatives and healthcare-focused projects to drive market growth       225

10.3.3      ITALY    230

10.3.3.1  Increasing use of graph databases in financial sector to accelerate market growth       230

10.3.4      GERMANY           235

10.3.4.1  Increasing focus on enhancing interoperability to boost market                 235

10.3.5      FRANCE                235

10.3.5.1  Graph databases to drive innovation, enabling data-driven decision-making across key industries               235

10.3.6      SPAIN    236

10.3.6.1  Government initiatives and geographical research to bolster market growth       236

10.3.7      REST OF EUROPE             236

10.4         ASIA PACIFIC     237

10.4.1      ASIA PACIFIC: MACROECONOMIC OUTLOOK     237

10.4.2      CHINA  244

10.4.2.1  Major players and use of graph databases in telecom fueling market growth       244

10.4.3      INDIA    249

10.4.3.1  Increasing focus on digital transformation to support market growth    249

10.4.4      JAPAN   254

10.4.4.1  Integration of knowledge graphs with generative AI to fuel market growth       254

10.4.5      AUSTRALIA & NEW ZEALAND     255

10.4.5.1  Strategic initiatives and presence of major players to drive adoption of graph databases                255

10.4.6      SOUTH KOREA  255

10.4.6.1  Increasing applications of graph databases in fraud detection, network analysis, and AI-powered innovations to aid market growth                 255

10.4.7      REST OF ASIA PACIFIC   255

10.5         MIDDLE EAST & AFRICA                256

10.5.1      MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK                 256

10.5.2      MIDDLE EAST   262

10.5.2.1  KSA        263

10.5.2.1.1                Digitalization initiatives to drive market growth                 263

10.5.2.2  UAE        268

10.5.2.2.1                Increasing applications of graph databases for environmental insights and research collaboration to drive market growth                 268

10.5.2.3  Qatar      268

10.5.2.3.1                Rising demand for advanced data analytics and interconnected data management solutions to drive market growth                 268

10.5.2.4  Turkey    268

10.5.2.4.1                Increasing adoption of graph technologies to address challenges in data analytics, decision-making, and innovation      268

10.5.2.5  Rest of Middle East              269

10.5.3      AFRICA 269

10.5.3.1  Strategic investments in cloud and AI technologies to drive adoption of graph databases                269

10.6         LATIN AMERICA                269

10.6.1      LATIN AMERICA: MACROECONOMIC OUTLOOK                 270

10.6.2      BRAZIL 276

10.6.2.1  Growing adoption of graph databases across industries and key collaborative initiatives to drive market            276

10.6.3      ARGENTINA       281

10.6.3.1  Advancements in cloud infrastructure and AI to further enable scalable deployment of graph databases           281

10.6.4      MEXICO                281

10.6.4.1  Increasing investments in cloud infrastructure to accelerate adoption of graph databases                281

10.6.5      REST OF LATIN AMERICA             281

11            COMPETITIVE LANDSCAPE         282

11.1         INTRODUCTION              282

11.2         KEY PLAYER STRATEGIES/RIGHT TO WIN            282

11.3         MARKET SHARE ANALYSIS, 2024                 284

11.3.1      MARKET RANKING ANALYSIS     286

11.4         REVENUE ANALYSIS, 2019–2023  287

11.5         COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024                 287

11.5.1      STARS   287

11.5.2      EMERGING LEADERS     287

11.5.3      PERVASIVE PLAYERS      288

11.5.4      PARTICIPANTS 288

11.5.5      COMPANY FOOTPRINT: KEY PLAYERS, 2024         289

11.5.5.1  Company footprint               289

11.5.5.2  Offering footprint 289

11.5.5.3  Model type footprint            290

11.5.5.4  Application footprint            291

11.5.5.5  Vertical footprint  291

11.5.5.6  Region footprint   292

11.6         COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024        293

11.6.1      PROGRESSIVE COMPANIES         293

11.6.2      RESPONSIVE COMPANIES            293

11.6.3      DYNAMIC COMPANIES  293

11.6.4      STARTING BLOCKS         293

11.6.5      COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024                 295

11.6.5.1  Detailed list of key startups/SMEs    295

11.6.5.2  Competitive benchmarking of key startups/SMEs          296

11.7         COMPETITIVE SCENARIO             297

11.7.1      PRODUCT LAUNCHES AND ENHANCEMENTS    297

11.7.2      DEALS  299

11.8         BRAND COMPARISON   301

11.9         COMPANY VALUATION AND FINANCIAL METRICS                 302

12            COMPANY PROFILES      303

12.1         KEY PLAYERS     303

12.1.1      NEO4J   303

12.1.1.1  Business overview 303

12.1.1.2  Products/Solutions/Services offered 303

12.1.1.3  Recent developments           305

12.1.1.3.1                Product launches and enhancements                 305

12.1.1.3.2                Deals      305

12.1.1.4  MnM view              306

12.1.1.4.1                Key strengths        306

12.1.1.4.2                Strategic choices   306

12.1.1.4.3                Weaknesses and competitive threats 306

12.1.2      AMAZON WEB SERVICES, INC     307

12.1.2.1  Business overview 307

12.1.2.2  Products/Solutions/Services offered 308

12.1.2.3  Recent developments           308

12.1.2.3.1                Product launches and enhancements                 308

12.1.2.3.2                Deals      309

12.1.2.4  MnM view              309

12.1.2.4.1                Key strengths        309

12.1.2.4.2                Strategic choices   309

12.1.2.4.3                Weaknesses and competitive threats 310

12.1.3      TIGERGRAPH     311

12.1.3.1  Business overview 311

12.1.3.2  Products/Solutions/Services offered 311

12.1.3.3  Recent developments           312

12.1.3.3.1                Product launches and enhancements                 312

12.1.3.3.2                Deals      313

12.1.3.4  MnM view              313

12.1.3.4.1                Key strengths        313

12.1.3.4.2                Strategic choices   313

12.1.3.4.3                Weaknesses and competitive threats 314

12.1.4      RELATIONALAI 315

12.1.4.1  Business overview 315

12.1.4.2  Products/Solutions/Services offered 315

12.1.4.3  Recent developments           316

12.1.4.3.1                Product launches and enhancements                 316

12.1.4.4  MnM view              316

12.1.4.4.1                Key strengths        316

12.1.4.4.2                Strategic choices   316

12.1.4.4.3                Weaknesses and competitive threats 316

12.1.5      GRAPHWISE       317

12.1.5.1  Business overview 317

12.1.5.2  Products/Solutions/Services offered 317

12.1.5.3  Recent developments           317

12.1.5.3.1                Product launches and enhancements                 317

12.1.5.4  MnM view              318

12.1.5.4.1                Key strengths        318

12.1.5.4.2                Strategic choices   318

12.1.5.4.3                Weaknesses and competitive threats 318

12.1.6      IBM CORPORATION        319

12.1.6.1  Business overview 319

12.1.6.2  Products/Solutions/Services offered 320

12.1.6.3  Recent developments           321

12.1.6.3.1                Deals      321

12.1.7      MICROSOFT CORPORATION, INC.            322

12.1.7.1  Business overview 322

12.1.7.2  Products/Solutions/Services offered 323

12.1.7.3  Recent developments           324

12.1.7.3.1                Deals      324

12.1.8      ONTOTEXT        325

12.1.8.1  Business overview 325

12.1.8.2  Products/Solutions/Services offered 325

12.1.8.3  Recent developments           327

12.1.8.3.1                Product launches and enhancements                 327

12.1.8.3.2                Deals      327

12.1.9      STAR DOG           329

12.1.9.1  Business overview 329

12.1.9.2  Products/Solutions/Services offered 329

12.1.9.3  Recent developments           330

12.1.9.3.1                Product launches and enhancements                 330

12.1.9.3.2                Deals      330

12.1.10   ALTAIR 331

12.1.10.1                 Business overview 331

12.1.10.2                 Products/Solutions/Services offered 332

12.1.10.3                 Recent developments           333

12.1.10.3.1             Product launches and enhancements                 333

12.1.10.3.2             Deals      333

12.1.11   ORACLE CORPORATION               334

12.1.11.1                 Business overview 334

12.1.11.2                 Products/Solutions/Services offered 335

12.1.11.3                 Recent developments           336

12.1.11.3.1             Product launches and enhancements                 336

12.1.12   PROGRESS SOFTWARE  337

12.1.12.1                 Business overview 337

12.1.12.2                 Products/Solutions/Services offered 338

12.1.12.3                 Recent developments           338

12.1.12.3.1             Deals      338

12.1.13   FRANZ INC.         339

12.1.13.1                 Business overview 339

12.1.13.2                 Products/Solutions/Services offered 339

12.1.13.3                 Recent developments           340

12.1.13.3.1             Product launches and enhancements                 340

12.1.14   DATASTAX          341

12.1.14.1                 Business overview 341

12.1.14.2                 Products/Solutions/Services offered 341

12.1.14.3                 Recent developments           342

12.1.14.3.1             Product launches and enhancements                 342

12.1.14.3.2             Deals      342

12.1.15   DGRAPH LABS   343

12.1.16   OPENLINK SOFTWARE  344

12.2         STARTUPS/SMES              345

12.2.1      OXFORD SEMANTIC TECHNOLOGIES    345

12.2.2      BITNINE               345

12.2.3      ARANGODB        346

12.2.4      FLUREE                 347

12.2.5      BLAZEGRAPH    348

12.2.6      MEMGRAPH       348

12.2.7      OBJECTIVITY INC             349

12.2.8      GRAPHBASE       349

12.2.9      GRAPH STORY  350

12.2.10   FALKORDB         350

13            ADJACENT AND RELATED MARKETS      351

13.1         INTRODUCTION              351

13.2         MARKET DEFINITION   351

13.3         CLOUD DATABASE AND DBAAS MARKET              351

13.3.1      MARKET DEFINITION   351

13.3.2      MARKET OVERVIEW       351

13.3.2.1  Cloud database and DBaaS market, by component        352

13.3.2.2  Cloud database and DBaaS market, by deployment model                 352

13.3.2.3  Cloud database and DBaaS market, by organization size                 353

13.3.2.4  Cloud database and DBaaS market, by vertical                354

13.3.2.5  Cloud database and DBaaS market, by region 355

13.4         VECTOR DATABASE MARKET     356

13.4.1      MARKET DEFINITION   356

13.4.2      VECTOR DATABASE MARKET, BY OFFERING      356

13.4.3      VECTOR DATABASE MARKET, BY TECHNOLOGY                 357

13.4.4      VECTOR DATABASE MARKET, BY VERTICAL       357

13.4.5      VECTOR DATABASE MARKET, BY REGION            358

14            APPENDIX           360

14.1         DISCUSSION GUIDE        360

14.2         KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL                365

14.3         CUSTOMIZATION OPTIONS        367

14.4         RELATED REPORTS         367

14.5         AUTHOR DETAILS           368

LIST OF TABLES

TABLE 1                USD EXCHANGE RATE, 2021–2023              45

TABLE 2                PRIMARY INTERVIEWS WITH EXPERTS  49

TABLE 3                RISK ASSESSMENT           56

TABLE 4                GRAPH DATABASE MARKET: ECOSYSTEM                 72

TABLE 5                TECHNOLOGY ROADMAP OF GRAPH DATABASE MARKET, 2024–2030  85

TABLE 6                NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES,

AND OTHER ORGANIZATIONS  86

TABLE 7                EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES,

AND OTHER ORGANIZATIONS  87

TABLE 8                ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES,

AND OTHER ORGANIZATIONS  88

TABLE 9                REST OF THE WORLD: REGULATORY BODIES, GOVERNMENT AGENCIES,

AND OTHER ORGANIZATIONS  89

TABLE 10              GRAPH DATABASE MARKET: KEY PATENTS, 2014–2022              97

TABLE 11              AVERAGE SELLING PRICES OF GRAPH DATABASE SOLUTIONS, BY REGION, 2023             104

TABLE 12              INDICATIVE PRICING ANALYSIS OF KEY PLAYERS, 2023 (USD)      104

TABLE 13              GRAPH DATABASE MARKET: CONFERENCES AND EVENTS, 2024–2025                 106

TABLE 14              IMPACT OF PORTER’S FIVE FORCES ON GRAPH DATABASE MARKET       108

TABLE 15              INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS   111

TABLE 16              KEY BUYING CRITERIA FOR TOP THREE INDUSTRIES       112

TABLE 17              GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)            115

TABLE 18              GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)            115

TABLE 19              GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)            116

TABLE 20              GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)            116

TABLE 21              SOLUTIONS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)          117

TABLE 22              SOLUTIONS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)          117

TABLE 23              GRAPH EXTENSIONS: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            117

TABLE 24              GRAPH EXTENSIONS: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            118

TABLE 25              GRAPH PROCESSING ENGINES: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            118

TABLE 26              GRAPH PROCESSING ENGINES: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            118

TABLE 27              NATIVE GRAPH DATABASE: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)          119

TABLE 28              NATIVE GRAPH DATABASE: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            119

TABLE 29              KNOWLEDGE GRAPH ENGINES: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            120

TABLE 30              KNOWLEDGE GRAPH ENGINES: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            120

TABLE 31              GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)             120

TABLE 32              GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION)             120

TABLE 33              CLOUD: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)          121

TABLE 34              CLOUD: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)          121

TABLE 35              ON-PREMISES: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            122

TABLE 36              ON-PREMISES: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            122

TABLE 37              GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)       123

TABLE 38              GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)       123

TABLE 39              SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)          123

TABLE 40              SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)          123

TABLE 41              MANAGED SERVICES: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            124

TABLE 42              MANAGED SERVICES: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            124

TABLE 43              PROFESSIONAL SERVICES: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            125

TABLE 44              PROFESSIONAL SERVICES: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            126

TABLE 45              GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            126

TABLE 46              GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            126

TABLE 47              CONSULTING SERVICES: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)          127

TABLE 48              CONSULTING SERVICES: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            127

TABLE 49              DEPLOYMENT & INTEGRATION SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)            128

TABLE 50              DEPLOYMENT & INTEGRATION SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)            128

TABLE 51              SUPPORT & MAINTENANCE SERVICES: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)                 129

TABLE 52              SUPPORT & MAINTENANCE SERVICES: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)                 129

TABLE 53              GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)            131

TABLE 54              GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)            132

TABLE 55              RESOURCE DESCRIPTION FRAMEWORK: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)                 132

TABLE 56              RESOURCE DESCRIPTION FRAMEWORK: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)                 133

TABLE 57             PROPERTY GRAPH: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            133

TABLE 58             PROPERTY GRAPH: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            134

TABLE 59              GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)            137

TABLE 60              GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION)            137

TABLE 61              DATA GOVERNANCE & MASTER DATA MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)       138

TABLE 62              DATA GOVERNANCE & MASTER DATA MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)       139

TABLE 63              DATA ANALYTICS & BUSINESS INTELLIGENCE: GRAPH DATABASE MARKET,

BY REGION, 2019–2023 (USD MILLION)   139

TABLE 64              DATA ANALYTICS & BUSINESS INTELLIGENCE: GRAPH DATABASE MARKET,

BY REGION, 2024–2030 (USD MILLION)   140

TABLE 65              KNOWLEDGE & CONTENT MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)            140

TABLE 66              KNOWLEDGE & CONTENT MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)            141

TABLE 67              VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)   141

TABLE 68              VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)   142

TABLE 69              PRODUCT & CONFIGURATION MANAGEMENT: GRAPH DATABASE MARKET,

BY REGION, 2019–2023 (USD MILLION)   142

TABLE 70              PRODUCT & CONFIGURATION MANAGEMENT: GRAPH DATABASE MARKET,

BY REGION, 2024–2030 (USD MILLION)   143

TABLE 71              INFRASTRUCTURE & ASSET MANAGEMENT: GRAPH DATABASE MARKET,

BY REGION, 2019–2023 (USD MILLION) 143

TABLE 72              INFRASTRUCTURE & ASSET MANAGEMENT: GRAPH DATABASE MARKET,

BY REGION, 2024–2030 (USD MILLION)   144

TABLE 73              PROCESS OPTIMIZATION & RESOURCE MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)       144

TABLE 74              PROCESS OPTIMIZATION & RESOURCE MANAGEMENT: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)       145

TABLE 75              RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING:

GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)            146

TABLE 76              RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING:

GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)            146

TABLE 77              MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION:

GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)            147

TABLE 78              MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION:

GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)            147

TABLE 79              OTHER APPLICATIONS: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            148

TABLE 80              OTHER APPLICATIONS: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            148

TABLE 81              GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)            151

TABLE 82              GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)            152

TABLE 83              BANKING, FINANCIAL SERVICES, AND INSURANCE: GRAPH DATABASE MARKET,

BY REGION, 2019–2023 (USD MILLION)   153

TABLE 84              BANKING, FINANCIAL SERVICES, AND INSURANCE: GRAPH DATABASE MARKET,

BY REGION, 2024–2030 (USD MILLION)   153

TABLE 85              RETAIL & ECOMMERCE: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            161

TABLE 86              RETAIL & ECOMMERCE: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            161

TABLE 87              TELECOM & TECHNOLOGY: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            167

TABLE 88              TELECOM & IT: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            167

TABLE 89              HEALTHCARE, LIFESCIENCES, AND PHARMACEUTICALS: GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)            172

TABLE 90              HEALTHCARE, LIFESCIENCES, AND PHARMACEUTICALS: GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)            172

TABLE 91              GOVERNMENT & PUBLIC SECTOR: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            177

TABLE 92              GOVERNMENT & PUBLIC SECTOR: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            178

TABLE 93              MANUFACTURING & AUTOMOTIVE: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            181

TABLE 94              MANUFACTURING & AUTOMOTIVE: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            182

TABLE 95              MEDIA & ENTERTAINMENT: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            186

TABLE 96              MEDIA & ENTERTAINMENT: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            187

TABLE 97              ENERGY & UTILITIES: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)          192

TABLE 98              ENERGY & UTILITIES: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            192

TABLE 99              TRAVEL & HOSPITALITY: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            196

TABLE 100            TRAVEL & HOSPITALITY: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            197

TABLE 101            TRANSPORTATION & LOGISTICS: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)            200

TABLE 102            TRANSPORTATION & LOGISTICS: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            200

TABLE 103          OTHER VERTICALS: GRAPH DATABASE MARKET, BY REGION,

2019–2023 (USD MILLION)          203

TABLE 104          OTHER VERTICALS: GRAPH DATABASE MARKET, BY REGION,

2024–2030 (USD MILLION)            203

TABLE 105            GRAPH DATABASE MARKET, BY REGION, 2019–2023 (USD MILLION)       205

TABLE 106            GRAPH DATABASE MARKET, BY REGION, 2024–2030 (USD MILLION)       205

TABLE 107            NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING,

2019–2023 (USD MILLION)            207

TABLE 108            NORTH AMERICA: GRAPH DATABASE MARKET, BY OFFERING,

2024–2030 (USD MILLION)          208

TABLE 109            NORTH AMERICA: GRAPH DATABASE MARKET, BY SOLUTION,

2019–2023 (USD MILLION)            208

TABLE 110            NORTH AMERICA: GRAPH DATABASE MARKET, BY SOLUTION,

2024–2030 (USD MILLION)            208

TABLE 111            NORTH AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            208

TABLE 112            NORTH AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            209

TABLE 113            NORTH AMERICA: GRAPH DATABASE MARKET, BY SERVICE,

2019–2023 (USD MILLION)            209

TABLE 114            NORTH AMERICA: GRAPH DATABASE MARKET, BY SERVICE,

2024–2030 (USD MILLION)            209

TABLE 115            NORTH AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            209

TABLE 116            NORTH AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            210

TABLE 117            NORTH AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE,

2019–2023 (USD MILLION)            210

TABLE 118            NORTH AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE,

2024–2030 (USD MILLION)            210

TABLE 119            NORTH AMERICA: GRAPH DATABASE MARKET, BY APPLICATION,

2019–2023 (USD MILLION)            211

TABLE 120            NORTH AMERICA: GRAPH DATABASE MARKET, BY APPLICATION,

2024–2030 (USD MILLION)            211

TABLE 121            NORTH AMERICA: GRAPH DATABASE MARKET, BY VERTICAL,

2019–2023 (USD MILLION)            212

TABLE 122            NORTH AMERICA: GRAPH DATABASE MARKET, BY VERTICAL,

2024–2030 (USD MILLION)            212

TABLE 123            NORTH AMERICA: GRAPH DATABASE MARKET, BY COUNTRY,

2019–2023 (USD MILLION)            213

TABLE 124            NORTH AMERICA: GRAPH DATABASE MARKET, BY COUNTRY,

2024–2030 (USD MILLION)            213

TABLE 125            US: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)            214

TABLE 126            US: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)            214

TABLE 127            US: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)            214

TABLE 128            US: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)            214

TABLE 129            US: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            214

TABLE 130            US: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            215

TABLE 131            US: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)            215

TABLE 132            US: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)            215

TABLE 133            US: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            215

TABLE 134            US: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            216

TABLE 135            US: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)                216

TABLE 136            US: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)                216

TABLE 137            US: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)              217

TABLE 138            US: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION)              217

TABLE 139            US: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)            218

TABLE 140            US: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)            218

TABLE 141            EUROPE: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)     220

TABLE 142            EUROPE: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)     220

TABLE 143            EUROPE: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)    220

TABLE 144            EUROPE: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)    221

TABLE 145            EUROPE: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            221

TABLE 146            EUROPE: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            221

TABLE 147            EUROPE: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)         221

TABLE 148            EUROPE: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)         221

TABLE 149            EUROPE: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            222

TABLE 150            EUROPE: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            222

TABLE 151            EUROPE: GRAPH DATABASE MARKET, BY MODEL TYPE,

2019–2023 (USD MILLION)            222

TABLE 152            EUROPE: GRAPH DATABASE MARKET, BY MODEL TYPE,

2024–2030 (USD MILLION)            222

TABLE 153            EUROPE: GRAPH DATABASE MARKET, BY APPLICATION,

2019–2023 (USD MILLION)            223

TABLE 154            EUROPE: GRAPH DATABASE MARKET, BY APPLICATION,

2024–2030 (USD MILLION)            223

TABLE 155            EUROPE: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)     224

TABLE 156            EUROPE: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)     224

TABLE 157            EUROPE: GRAPH DATABASE MARKET, BY COUNTRY, 2019–2023 (USD MILLION)     225

TABLE 158            EUROPE: GRAPH DATABASE MARKET, BY COUNTRY, 2024–2030 (USD MILLION)     225

TABLE 159            UK: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)            226

TABLE 160            UK: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)            226

TABLE 161            UK: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)            226

TABLE 162            UK: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)            226

TABLE 163            UK: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            226

TABLE 164            UK: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)          227

TABLE 165            UK: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)            227

TABLE 166            UK: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)            227

TABLE 167            UK: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            227

TABLE 168            UK: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            228

TABLE 169            UK: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)                228

TABLE 170            UK: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)                228

TABLE 171            UK: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)              228

TABLE 172            UK: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION)              229

TABLE 173            UK: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)            229

TABLE 174            UK: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)            230

TABLE 175            ITALY: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)     230

TABLE 176            ITALY: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)     231

TABLE 177            ITALY: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)    231

TABLE 178            ITALY: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)    231

TABLE 179            ITALY: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            231

TABLE 180            ITALY: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            232

TABLE 181          ITALY: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)            232

TABLE 182          ITALY: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)            232

TABLE 183            ITALY: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            232

TABLE 184            ITALY: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            233

TABLE 185            ITALY: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)                233

TABLE 186            ITALY: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)                233

TABLE 187            ITALY: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)              233

TABLE 188            ITALY: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION)              234

TABLE 189            ITALY: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)     234

TABLE 190            ITALY: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)     235

TABLE 191            ASIA PACIFIC: GRAPH DATABASE MARKET, BY OFFERING,

2019–2023 (USD MILLION)            238

TABLE 192            ASIA PACIFIC: GRAPH DATABASE MARKET, BY OFFERING,

2024–2030 (USD MILLION)            239

TABLE 193            ASIA PACIFIC: GRAPH DATABASE MARKET, BY SOLUTION,

2019–2023 (USD MILLION)            239

TABLE 194            ASIA PACIFIC: GRAPH DATABASE MARKET, BY SOLUTION,

2024–2030 (USD MILLION)            239

TABLE 195            ASIA PACIFIC: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            239

TABLE 196            ASIA PACIFIC: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            240

TABLE 197            ASIA PACIFIC: GRAPH DATABASE MARKET, BY SERVICE,

2019–2023 (USD MILLION)            240

TABLE 198            ASIA PACIFIC: GRAPH DATABASE MARKET, BY SERVICE,

2024–2030 (USD MILLION)            240

TABLE 199            ASIA PACIFIC: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            240

TABLE 200            ASIA PACIFIC: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            241

TABLE 201            ASIA PACIFIC: GRAPH DATABASE MARKET, BY MODEL TYPE,

2019–2023 (USD MILLION)            241

TABLE 202            ASIA PACIFIC: GRAPH DATABASE MARKET, BY MODEL TYPE,

2024–2030 (USD MILLION)          241

TABLE 203            ASIA PACIFIC: GRAPH DATABASE MARKET, BY APPLICATION,

2019–2023 (USD MILLION)            241

TABLE 204            ASIA PACIFIC: GRAPH DATABASE MARKET, BY APPLICATION,

2024–2030 (USD MILLION)            242

TABLE 205            ASIA PACIFIC: GRAPH DATABASE MARKET, BY VERTICAL,

2019–2023 (USD MILLION)            242

TABLE 206            ASIA PACIFIC: GRAPH DATABASE MARKET, BY VERTICAL,

2024–2030 (USD MILLION)            243

TABLE 207            ASIA PACIFIC: GRAPH DATABASE MARKET, BY COUNTRY,

2019–2023 (USD MILLION)            243

TABLE 208            ASIA PACIFIC: GRAPH DATABASE MARKET, BY COUNTRY,

2024–2030 (USD MILLION)            243

TABLE 209            CHINA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)     244

TABLE 210            CHINA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)     244

TABLE 211            CHINA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)    245

TABLE 212            CHINA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)    245

TABLE 213            CHINA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            245

TABLE 214            CHINA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            245

TABLE 215            CHINA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)         245

TABLE 216            CHINA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)         246

TABLE 217            CHINA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            246

TABLE 218            CHINA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            246

TABLE 219            CHINA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)                246

TABLE 220            CHINA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)                247

TABLE 221            CHINA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)              247

TABLE 222            CHINA: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION)              248

TABLE 223            CHINA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)     248

TABLE 224            CHINA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)     249

TABLE 225            INDIA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)     249

TABLE 226            INDIA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)     250

TABLE 227            INDIA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)    250

TABLE 228            INDIA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)    250

TABLE 229            INDIA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            250

TABLE 230            INDIA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            250

TABLE 231            INDIA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)            251

TABLE 232            INDIA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)            251

TABLE 233            INDIA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            251

TABLE 234            INDIA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            251

TABLE 235            INDIA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)                252

TABLE 236            INDIA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)                252

TABLE 237            INDIA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)              252

TABLE 238            INDIA: GRAPH DATABASE MARKET, BY APPLICATIONS, 2024–2030 (USD MILLION)           253

TABLE 239            INDIA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)     253

TABLE 240            INDIA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)     254

TABLE 241            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY OFFERING,

2019–2023 (USD MILLION)            256

TABLE 242            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY OFFERING,

2024–2030 (USD MILLION)            257

TABLE 243            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SOLUTION,

2019–2023 (USD MILLION)            257

TABLE 244            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SOLUTION,

2024–2030 (USD MILLION)            257

TABLE 245            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)                 257

TABLE 246            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE, 2024–2030 (USD MILLION)                 258

TABLE 247            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SERVICE,

2019–2023 (USD MILLION)            258

TABLE 248            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY SERVICE,

2024–2030 (USD MILLION)            258

TABLE 249            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2019–2023 (USD MILLION)                 258

TABLE 250            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE, 2024–2030 (USD MILLION)                 259

TABLE 251            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY MODEL TYPE,

2019–2023 (USD MILLION)            259

TABLE 252            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY MODEL TYPE,

2024–2030 (USD MILLION)            259

TABLE 253            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY APPLICATION,

2019–2023 (USD MILLION)            260

TABLE 254            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY APPLICATION,

2024–2030 (USD MILLION)            260

TABLE 255            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY VERTICAL,

2019–2023 (USD MILLION)            261

TABLE 256            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY VERTICAL,

2024–2030 (USD MILLION)            261

TABLE 257            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY COUNTRY,

2019–2023 (USD MILLION)            262

TABLE 258            MIDDLE EAST & AFRICA: GRAPH DATABASE MARKET, BY COUNTRY,

2024–2030 (USD MILLION)            262

TABLE 259            MIDDLE EAST: GRAPH DATABASE MARKET, BY COUNTRY,

2019–2023 (USD MILLION)            262

TABLE 260            MIDDLE EAST: GRAPH DATABASE MARKET, BY COUNTRY,

2024–2030 (USD MILLION)            263

TABLE 261            KSA: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)            263

TABLE 262            KSA: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)            263

TABLE 263          KSA: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)            264

TABLE 264          KSA: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)            264

TABLE 265            KSA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            264

TABLE 266            KSA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            264

TABLE 267            KSA: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)            264

TABLE 268            KSA: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)            265

TABLE 269            KSA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            265

TABLE 270            KSA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            265

TABLE 271            KSA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)                265

TABLE 272            KSA: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)                265

TABLE 273            KSA: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)              266

TABLE 274            KSA: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION)              266

TABLE 275            KSA: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)            267

TABLE 276            KSA: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)            267

TABLE 277            LATIN AMERICA: GRAPH DATABASE MARKET, BY OFFERING,

2019–2023 (USD MILLION)            270

TABLE 278            LATIN AMERICA: GRAPH DATABASE MARKET, BY OFFERING,

2024–2030 (USD MILLION)            270

TABLE 279            LATIN AMERICA: GRAPH DATABASE MARKET, BY SOLUTION,

2019–2023 (USD MILLION)            271

TABLE 280            LATIN AMERICA: GRAPH DATABASE MARKET, BY SOLUTION,

2024–2030 (USD MILLION)            271

TABLE 281            LATIN AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            271

TABLE 282            LATIN AMERICA: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            271

TABLE 283            LATIN AMERICA: GRAPH DATABASE MARKET, BY SERVICE,

2019–2023 (USD MILLION)            272

TABLE 284            LATIN AMERICA: GRAPH DATABASE MARKET, BY SERVICE,

2024–2030 (USD MILLION)            272

TABLE 285            LATIN AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            272

TABLE 286            LATIN AMERICA: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            272

TABLE 287            LATIN AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE,

2019–2023 (USD MILLION)            273

TABLE 288            LATIN AMERICA: GRAPH DATABASE MARKET, BY MODEL TYPE,

2024–2030 (USD MILLION)            273

TABLE 289            LATIN AMERICA: GRAPH DATABASE MARKET, BY APPLICATION,

2019–2023 (USD MILLION)            273

TABLE 290            LATIN AMERICA: GRAPH DATABASE MARKET, BY APPLICATION,

2024–2030 (USD MILLION)            274

TABLE 291            LATIN AMERICA: GRAPH DATABASE MARKET, BY VERTICAL,

2019–2023 (USD MILLION)            274

TABLE 292            LATIN AMERICA: GRAPH DATABASE MARKET, BY VERTICAL,

2024–2030 (USD MILLION)            275

TABLE 293            LATIN AMERICA: GRAPH DATABASE MARKET, BY COUNTRY,

2019–2023 (USD MILLION)            275

TABLE 294            LATIN AMERICA: GRAPH DATABASE MARKET, BY COUNTRY,

2024–2030 (USD MILLION)            275

TABLE 295            BRAZIL: GRAPH DATABASE MARKET, BY OFFERING, 2019–2023 (USD MILLION)     276

TABLE 296            BRAZIL: GRAPH DATABASE MARKET, BY OFFERING, 2024–2030 (USD MILLION)     276

TABLE 297            BRAZIL: GRAPH DATABASE MARKET, BY SOLUTION, 2019–2023 (USD MILLION)    276

TABLE 298            BRAZIL: GRAPH DATABASE MARKET, BY SOLUTION, 2024–2030 (USD MILLION)    277

TABLE 299            BRAZIL: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2019–2023 (USD MILLION)            277

TABLE 300            BRAZIL: GRAPH DATABASE MARKET, BY DEPLOYMENT MODE,

2024–2030 (USD MILLION)            277

TABLE 301            BRAZIL: GRAPH DATABASE MARKET, BY SERVICE, 2019–2023 (USD MILLION)         277

TABLE 302            BRAZIL: GRAPH DATABASE MARKET, BY SERVICE, 2024–2030 (USD MILLION)         277

TABLE 303            BRAZIL: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2019–2023 (USD MILLION)            278

TABLE 304            BRAZIL: GRAPH DATABASE MARKET, BY PROFESSIONAL SERVICE,

2024–2030 (USD MILLION)            278

TABLE 305            BRAZIL: GRAPH DATABASE MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)                278

TABLE 306            BRAZIL: GRAPH DATABASE MARKET, BY MODEL TYPE, 2024–2030 (USD MILLION)                278

TABLE 307            BRAZIL: GRAPH DATABASE MARKET, BY APPLICATION, 2019–2023 (USD MILLION)              279

TABLE 308            BRAZIL: GRAPH DATABASE MARKET, BY APPLICATION, 2024–2030 (USD MILLION)              279

TABLE 309            BRAZIL: GRAPH DATABASE MARKET, BY VERTICAL, 2019–2023 (USD MILLION)     280

TABLE 310            BRAZIL: GRAPH DATABASE MARKET, BY VERTICAL, 2024–2030 (USD MILLION)     280

TABLE 311            OVERVIEW OF STRATEGIES DEPLOYED

BY KEY GRAPH DATABASE MARKET PLAYERS, 2021–2024                 282

TABLE 312            GRAPH DATABASE MARKET: DEGREE OF COMPETITION 284

TABLE 313            GRAPH DATABASE MARKET: OFFERING FOOTPRINT       289

TABLE 314            GRAPH DATABASE MARKET: MODEL TYPE FOOTPRINT       290

TABLE 315            GRAPH DATABASE MARKET: APPLICATION FOOTPRINT       291

TABLE 316            GRAPH DATABASE MARKET: VERTICAL FOOTPRINT       291

TABLE 317            GRAPH DATABASE MARKET: REGION FOOTPRINT       292

TABLE 318            GRAPH DATABASE MARKET: LIST OF KEY STARTUPS/SMES              295

TABLE 319            COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES              296

TABLE 320            GRAPH DATABASE: PRODUCT LAUNCHES AND ENHANCEMENTS, SEPTEMBER 2022–OCTOBER 2024        297

TABLE 321            GRAPH DATABASE MARKET: DEALS, JANUARY 2023–NOVEMBER 2024    299

TABLE 322            NEO4J: COMPANY OVERVIEW    303

TABLE 323            NEO4J: PRODUCTS/SOLUTIONS/SERVICES OFFERED             303

TABLE 324            NEO4J: PRODUCT LAUNCHES AND ENHANCEMENTS             305

TABLE 325            NEO4J: DEALS   305

TABLE 326            AMAZON WEB SERVICES: COMPANY OVERVIEW                 307

TABLE 327            AMAZON WEB SERVICES: PRODUCTS/SOLUTIONS/SERVICES OFFERED    308

TABLE 328            AMAZON WEB SERVICES: PRODUCT LAUNCHES AND ENHANCEMENTS  308

TABLE 329            AMAZON WEB SERVICES: DEALS               309

TABLE 330            TIGERGRAPH: COMPANY OVERVIEW     311

TABLE 331            TIGERGRAPH: PRODUCTS/SOLUTIONS/SERVICES OFFERED    311

TABLE 332            TIGERGRAPH: PRODUCT LAUNCHES AND ENHANCEMENTS             312

TABLE 333            TIGERGRAPH: DEALS     313

TABLE 334            RELATIONALAI: COMPANY OVERVIEW 315

TABLE 335            RELATIONALAI: PRODUCTS/SOLUTIONS/SERVICES OFFERED    315

TABLE 336            RELATIONALAI: PRODUCT LAUNCHES AND ENHANCEMENTS             316

TABLE 337            GRAPHWISE: COMPANY OVERVIEW        317

TABLE 338            GRAPHWISE: PRODUCTS/SOLUTIONS/SERVICES OFFERED    317

TABLE 339            GRAPHWISE: PRODUCT LAUNCHES AND ENHANCEMENTS             317

TABLE 340            IBM: COMPANY OVERVIEW         319

TABLE 341            IBM: PRODUCTS/SOLUTIONS/SERVICES OFFERED             320

TABLE 342            IBM: DEALS        321

TABLE 343            MICROSOFT: COMPANY OVERVIEW        322

TABLE 344            MICROSOFT: PRODUCTS/SOLUTIONS/SERVICES OFFERED    323

TABLE 345            MICROSOFT: DEALS       324

TABLE 346            ONTOTEXT: COMPANY OVERVIEW         325

TABLE 347            ONTOTEXT: PRODUCTS/SOLUTIONS/SERVICES OFFERED    325

TABLE 348            ONTOTEXT: PRODUCT LAUNCHES AND ENHANCEMENTS             327

TABLE 349            ONTOTEXT: DEALS        327

TABLE 350            STARDOG: COMPANY OVERVIEW             329

TABLE 351            STARDOG: PRODUCTS/SOLUTIONS/SERVICES OFFERED             329

TABLE 352            STARDOG: PRODUCT LAUNCHES AND ENHANCEMENTS             330

TABLE 353            STARDOG: DEALS            330

TABLE 354            ALTAIR: COMPANY OVERVIEW 331

TABLE 355            ALTAIR: PRODUCTS/SOLUTIONS/SERVICES OFFERED             332

TABLE 356            ALTAIR: PRODUCT LAUNCHES AND ENHANCEMENTS             333

TABLE 357            ALTAIR: DEALS 333

TABLE 358            ORACLE: COMPANY OVERVIEW                334

TABLE 359            ORACLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED             335

TABLE 360            ORACLE: PRODUCT LAUNCHES AND ENHANCEMENTS             336

TABLE 361            PROGRESS SOFTWARE: COMPANY OVERVIEW                 337

TABLE 362            PROGRESS SOFTWARE: PRODUCTS/SOLUTIONS/SERVICES OFFERED    338

TABLE 363            PROGRESS SOFTWARE: DEALS  338

TABLE 364            FRANZ INC: COMPANY OVERVIEW           339

TABLE 365            FRANZ INC: PRODUCTS/SOLUTIONS/SERVICES OFFERED             339

TABLE 366            FRANZ INC.: PRODUCT LAUNCHES AND ENHANCEMENTS             340

TABLE 367            DATASTAX: COMPANY OVERVIEW           341

TABLE 368            DATASTAX: PRODUCTS/SOLUTIONS/SERVICES OFFERED             341

TABLE 369            DATASTAX: PRODUCT LAUNCHES AND ENHANCEMENTS             342

TABLE 370            DATASTAX: DEALS          342

TABLE 371            CLOUD DATABASE AND DBAAS MARKET, BY COMPONENT,

2018–2022 (USD MILLION)            352

TABLE 372            CLOUD DATABASE AND DBAAS MARKET, BY COMPONENT,

2023–2028 (USD MILLION)            352

TABLE 373            CLOUD DATABASE AND DBAAS MARKET, BY SERVICE, 2018–2022 (USD MILLION)         352

TABLE 374            CLOUD DATABASE AND DBAAS MARKET, BY SERVICE, 2023–2028 (USD MILLION)         352

TABLE 375            CLOUD DATABASE AND DBAAS MARKET, BY DEPLOYMENT MODEL,

2018–2022 (USD MILLION)            353

TABLE 376            CLOUD DATABASE AND DBAAS MARKET, BY DEPLOYMENT MODEL,

2023–2028 (USD MILLION)            353

TABLE 377            CLOUD DATABASE AND DBAAS MARKET, BY ORGANIZATION SIZE,

2018–2022 (USD MILLION)            353

TABLE 378            CLOUD DATABASE AND DBAAS MARKET, BY ORGANIZATION SIZE,

2023–2028 (USD MILLION)            354

TABLE 379            CLOUD DATABASE AND DBAAS MARKET, BY VERTICAL, 2018–2022 (USD MILLION)     354

TABLE 380            CLOUD DATABASE AND DBAAS MARKET, BY VERTICAL, 2023–2028 (USD MILLION)     355

TABLE 381            CLOUD DATABASE AND DBAAS MARKET, BY REGION, 2018–2022 (USD MILLION)          355

TABLE 382            VECTOR DATABASE MARKET, BY OFFERING, 2019–2022 (USD MILLION)            356

TABLE 383            VECTOR DATABASE MARKET, BY OFFERING, 2023–2028 (USD MILLION)            356

TABLE 384            VECTOR DATABASE MARKET, BY TECHNOLOGY, 2019–2022 (USD MILLION)            357

TABLE 385            VECTOR DATABASE MARKET, BY TECHNOLOGY, 2023–2028 (USD MILLION)            357

TABLE 386            VECTOR DATABASE MARKET, BY VERTICAL, 2019–2022 (USD MILLION)            358

TABLE 387            VECTOR DATABASE MARKET, BY REGION, 2019–2022 (USD MILLION)       358

TABLE 388            VECTOR DATABASE MARKET, BY REGION, 2023–2028 (USD MILLION)       359

TABLE 389            VECTOR DATABASE MARKET, BY VERTICAL, 2023–2028 (USD MILLION)            359

LIST OF FIGURES

FIGURE 1              GRAPH DATABASE MARKET: RESEARCH DESIGN                 47

FIGURE 2              KEY DATA FROM SECONDARY SOURCES                 48

FIGURE 3              TOP-DOWN APPROACH                51

FIGURE 4              APPROACH 1 (SUPPLY SIDE): REVENUE OF VENDORS IN

GRAPH DATABASE MARKET       51

FIGURE 5              BOTTOM-UP APPROACH              52

FIGURE 6              DEMAND-SIDE ANALYSIS             52

FIGURE 7              BOTTOM-UP (SUPPLY SIDE) ANALYSIS: COLLECTIVE REVENUE FROM SOLUTIONS/SERVICES OF EMOTION AI MARKET   53

FIGURE 8              DATA TRIANGULATION                54

FIGURE 9              GRAPH DATABASE MARKET, 2024–2030 (USD MILLION)            57

FIGURE 10            GRAPH DATABASE MARKET, BY REGION (2024)                 58

FIGURE 11            INCREASING RELIANCE ON REAL-TIME ANALYTICS FOR CRITICAL DECISION-MAKING ACROSS INDUSTRIES TO DRIVE MARKET               59

FIGURE 12            SOLUTIONS SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD       59

FIGURE 13            MANAGED SERVICES SEGMENT TO ACCOUNT FOR HIGHER CAGR DURING FORECAST PERIOD               60

FIGURE 14            DEPLOYMENT & INTEGRATION SERVICES SEGMENT TO ACCOUNT FOR LARGEST MARKET DURING FORECAST PERIOD         60

FIGURE 15            DATA GOVERNANCE & MASTER DATA MANAGEMENT SEGMENT

TO ACCOUNT FOR LARGEST MARKET SHARE DURING FORECAST PERIOD         60

FIGURE 16            PROPERTY GRAPH TO ACCOUNT FOR LARGER MARKET SHARE

DURING FORECAST PERIOD       61

FIGURE 17            BFSI SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE DURING FORECAST PERIOD     61

FIGURE 18            SOLUTIONS & PROPERTY GRAPH SEGMENTS TO ACCOUNT FOR SIGNIFICANT MARKET SHARES IN 2024                 62

FIGURE 19            GRAPH DATABASE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES         63

FIGURE 20            EVOLUTION OF GRAPH DATABASE MARKET                 70

FIGURE 21            GRAPH DATABASE MARKET: ECOSYSTEM ANALYSIS            72

FIGURE 22            GRAPH DATABASE MARKET: SUPPLY CHAIN ANALYSIS            81

FIGURE 23            GRAPH DATABASE MARKET: INVESTMENT AND FUNDING SCENARIO,

2020–2024 (USD MILLION)            82

FIGURE 24            USE CASES OF GENERATIVE AI IN GRAPH DATABASE MARKET       84

FIGURE 25            LIST OF MAJOR PATENTS FOR GRAPH DATABASE MARKET (2014–2024)                96

FIGURE 26            AVERAGE SELLING PRICE OF KEY PLAYERS, BY COUNTRY, 2023 103

FIGURE 27            GRAPH DATABASE MARKET: PORTER’S FIVE FORCES ANALYSIS           108

FIGURE 28            GRAPH DATABASE MARKET: TRENDS/DISRUPTIONS INFLUENCING CUSTOMER BUSINESS                 110

FIGURE 29            INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS   111

FIGURE 30            KEY BUYING CRITERIA FOR TOP THREE INDUSTRIES       112

FIGURE 31            SERVICES SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD          115

FIGURE 32            KNOWLEDGE GRAPH ENGINES SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD 116

FIGURE 33            MANAGED SERVICES TO GROW AT HIGHER CAGR DURING FORECAST PERIOD          122

FIGURE 34            SUPPORT & MAINTENANCE SERVICES TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 125

FIGURE 35            RESOURCE DESCRIPTION FRAMEWORK SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD                131

FIGURE 36            VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD       136

FIGURE 37            BFSI TO ACCOUNT FOR LARGEST MARKET DURING FORECAST PERIOD       151

FIGURE 38            NORTH AMERICA: GRAPH DATABASE MARKET SNAPSHOT          207

FIGURE 39            ASIA PACIFIC: GRAPH DATABASE MARKET SNAPSHOT          238

FIGURE 40            SHARE ANALYSIS OF LEADING COMPANIES IN GRAPH DATABASE MARKET, 2024             284

FIGURE 41            MARKET RANKING ANALYSIS OF TOP FIVE PLAYERS              286

FIGURE 42            REVENUE ANALYSIS OF KEY PLAYERS IN GRAPH DATABASE MARKET,

2019–2023 (USD MILLION)            287

FIGURE 43            GRAPH DATABASE MARKET: COMPANY EVALUATION MATRIX (KEY PLAYERS), 2024         288

FIGURE 44            GRAPH DATABASE MARKET: COMPANY FOOTPRINT       289

FIGURE 45            GRAPH DATABASE MARKET: COMPANY EVALUATION MATRIX

(STARTUPS/SMES), 2024                294

FIGURE 46            BRAND COMPARISON   301

FIGURE 47            COMPANY VALUATION 302

FIGURE 48            FINANCIAL METRICS      302

FIGURE 49            AMAZON WEB SERVICES: COMPANY SNAPSHOT                 307

FIGURE 50            IBM: COMPANY SNAPSHOT         320

FIGURE 51            MICROSOFT: COMPANY SNAPSHOT        323

FIGURE 52            ALTAIR: COMPANY SNAPSHOT 332

FIGURE 53            ORACLE: COMPANY SNAPSHOT                335

FIGURE 54            PROGRESS SOFTWARE: COMPANY SNAPSHOT                 337

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