$2.14 Billion Graph Database Market Forecast To 2030: Asia Pacific Set To Witness The Highest Market Growth Rate During The Forecast Period

(MENAFN- GlobeNewsWire - Nasdaq) 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 and services, by model type (resource description framework, property graph), by application, by vertical and region . 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.

Dublin, Jan. 30, 2025 (GLOBE NEWSWIRE) -- The "Graph Database Market by Solutions (Graph Extension, Graph Processing Engines, Native Graph Database, Knowledge Graph Engines), Application (Data Governance and Master Data Management, Infrastructure and Asset Management) - Global Forecast to 2030" report has been added to ResearchAndMarkets.com's offering.
The Graph Database market is estimated at USD 507.6 million in 2024, and is forecast to grow to USD 2.14 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 27.1%.

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

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.

The major players in the Graph Database market include IBM, Oracle, Microsoft, AWS, Neo4j, RelationaAI, Progress Software, TigerGraph, Stardog, Datastax, Franz, Ontotext, Openlink Software, Dgraph Labs, Graphwise, Altair, Bitnine, ArangoDB, Fluree, Blazegraph, Memgraph, Objectivity, GraphBase, Graph Story, Oxford Semantic Technologies and FalkorDB. 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.
The report provides insights on the following:

• 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).

Key Attributes:

Report Attribute	Details
No. of Pages	369
Forecast Period	2024 - 2030
Estimated Market Value (USD) in 2024	$507.6 Million
Forecasted Market Value (USD) by 2030	$2143 Million
Compound Annual Growth Rate	27.1%
Regions Covered	Global

Key Topics Covered:
Premium Insights

• Opportunities for Key Players in Graph Database Market
• Graph Database Market, by Offering
• Graph Database Market, by Service
• Graph Database Market, by Professional Service
• Graph Database Market, by Application
• Graph Database Market, by Model Type
• Graph Database Market, by Vertical
• North America: Graph Database Market, by Offering and Model Type

Market Overview and Industry Trends

Market Dynamics

Drivers

• Increasing Gen AI Applications
• Surging Need for Incorporating Real-Time Big Data Mining with Result Visualization
• Rising Demand for Solutions That Can Process Low-Latency Queries
• Rapid Use of Virtualization for Big Data Analytics
• Growing Demand for Semantic Search Across Unstructured Content

Restraints

• Lack of Standardization and Programming Ease
• Rapid Proliferation of Data Management Technologies
• High Implementation Costs

Opportunities

• Data Unification and Rapid Proliferation of Knowledge Graphs
• Provision of Semantic Knowledgeable Graphs to Address Complex-Scientific Research
• Emphasis on Emergence of Open Knowledge Networks

Challenges

• Lack of Technical Expertise
• Difficulty in Demonstrating Benefits of Knowledge Graphs in Single Application or Use Case

Case Study Analysis

• Neo4J-Powered Knowledge Graph Helped Intuit Provide Real-Time Insights and Facilitate Swift Responses to Security Threats
• Westjet Improved Its Customer Booking Experience by Integrating Neo4J's Graph Technology
• Newday Improved Fraud Detection Capabilities with Tigergraph Cloud
• Cyber Resilience Leader Leveraged Tigergraph to Elevate Its Next-Generation Cloud-based Cybersecurity Services
• Xbox Chose Tigergraph to Empower Its Graph Analytics Capabilities
• Dgraph's Cutting-Edge Database Solution Enabled Mooncamp to Streamline Its Backend Operations
• Neo4J's Graph Database and Application Platform Helped Kerberos Control Complex Legal Obligations
• Blazegraph Helped Yahoo7 Drive Native Real-Time Advertising Using Graph Queries
• Neo4J Enabled Icu's Team to Visualize and Analyze Connections Between Elements of Panama Papers Leaks
• Neo4J's Graph Technology Helped U.S. Army by Tracking and Analyzing Equipment Maintenance
• Jaguar Land Rover Achieved Reduced Inventory Costs and Higher Profitability Using Tigergraph's Solution
• Macy's Reduced Catalog Data Refresh Time by Six-Fold
• Metaphacts and Ontotext Enabled Global Pharma Company to Boost R&D Knowledge Discovery

Company Profiles

Key Players

• Neo4J
• Amazon Web Services
• Tigergraph
• Relationalai
• Graphwise
• IBM
• Microsoft
• Ontotext
• Star Dog
• Altair
• Oracle Corporation
• Progress Software
• Franz Inc.
• Datastax
• Dgraph Labs
• Openlink Software

Startups/SMEs

• Oxford Semantic Technologies
• Bitnine
• Arangodb
• Fluree
• Blazegraph
• Memgraph
• Objectivity Inc.
• Graphbase
• Graph Story
• Falkordb

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Attachment

• Graph Database Market

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