그래프 데이터베이스 시장 보고서 : 컴포넌트별, 데이터베이스 유형별, 분석 유형별, 도입 모델별, 용도별, 업종별, 지역별(2026-2034년)

The global graph database market size reached USD 2.4 Billion in 2025. Looking forward, IMARC Group expects the market to reach USD 9.4 Billion by 2034, exhibiting a growth rate (CAGR) of 16.19% during 2026-2034. The increasing adoption of graph databases in cybersecurity for threat detection and network analysis, growing demand for real-time analytics and AI-driven insights, and expanding application in industries, such as healthcare and finance, for data integration and personalized services, are some of the key factors catalyzing the market growth.

GRAPH DATABASE MARKET ANALYSIS:

• Major Market Drivers: The rising usage of graph database solutions in different industry verticals, such as retail, information technology (IT), telecommunications, manufacturing, transportation, and banking, financial services and insurance (BFSI), represents one of the key factors propelling the market growth.
• Key Market Trends: The growing adoption of artificial intelligence (AI)-based graph database tools across the world is one of the significant key trends driving the growth of the market.
• Competitive Landscape: Some of the leading graph database market companies are Amazon Web Services Inc. (Amazon.com Inc.), Datastax Inc., Franz Inc., International Business Machines Corporation, Marklogic Corporation, Microsoft Corporation, Neo4j Inc., Objectivity Inc., Oracle Corporation, Stardog Union, Tibco Software Inc., and Tigergraph Inc., among others.
• Geographical Trends: According to the report, North America currently dominates the global market. The expanding use of technology in the region is one of the main reasons promoting the growth of the graph database market. The expansion of graph database players across North America, such as IBM, Microsoft, Neo4j, and Oracle., is anticipated to drive market expansion further.
• Challenges and Opportunities: Challenges in the graph database market include data privacy concerns, complexity in migrating from relational databases, and the need for skilled personnel. Opportunities lie in addressing evolving use cases such as fraud detection, personalized recommendation systems, and knowledge graph applications, driving innovation and market expansion.

GRAPH DATABASE MARKET TRENDS:

Rising Volume and Complexity of Data

One of the primary factors driving the growth of the market is the increasing volume of data generated by numerous organizations across the world. With the advent of next-generation technologies and the proliferation of connected devices, businesses are producing vast amounts of data from various sources, including social media, customer interaction, IoT devices, transactions, and cloud computing. For instance, according to Cisco, the IoT generated approximately 507.5 zettabytes of data in 2019. A survey by the Ponemon Institute and the Shared Assessments Program also shared that at least 81% of risk oversight and corporate governance professionals believe data breaches happened by an unsecured IoT device within their company. In response to this, companies are increasingly integrating graph database solutions to drive valuable insights from the data and ensure the security and accuracy of their data by providing data cleansing, validation, and enrichment capabilities. This, in turn, is projected to fuel the graph database market demand in the coming years.

Increasing Product Offerings

Various key players are introducing a variety of graph database solutions catering to different use cases and requirements. For instance, in April 2024, Neo4j partnered with Google Cloud to launch new GraphRAG capabilities for GenAI applications. This launch will speed up generative AI application development and deployment across several crucial stages. The results solve a problem for enterprises that struggle with complexity and hallucinations when building and deploying successful GenAI applications requiring real-time, contextually rich data and accurate, explainable results. Similarly, in December 2023, Amazon Web Services (AWS) launched a new analytics database engine that combines the power of vector search and graph data. The general availability of the new service, named Amazon Neptune Analytics, was unveiled at the re-invest conference in Las Vegas. The new service is available as a pay-as-you-go model with no one-time setup fees or recurring subscriptions. It is now available in some AWS regions, including the US East, the US West, Asia Pacific, and Europe. Such innovations in graph databases are anticipated to propel the graph database market share in the coming years.

Growing Product Application across Various Industries

Graph databases are being adopted across various industries, including finance, healthcare, retail, logistics, and manufacturing, to address specific use cases and business challenges. For instance, in January 2024, the R&D arm of global pharmaceutical company Servier started to utilize graph technologies to cut drug research time and improve the success rate of drug candidates in the clinical phase. The company is using Neo4j's graph called Pegasus, which allows them to better organize and probe both third-party and proprietary data. Similarly, the escalating utilization of graph databases in the financial sector to detect and prevent fraudulent activities is also catalyzing the graph database market's recent price. For instance, graph databases such as Amazon Neptune are increasingly being used to perform queries because they can traverse the data and perform calculations simultaneously. Graphs represent transactions and parties over a multi-connected network and discover patterns and chains of connections. As a result, graph databases are extensively being used in anti-money laundering (AML) applications since they can help find patterns of suspicious transactions.

GLOBAL GRAPH DATABASE MARKET SEGMENTATION:

Breakup by Component:

• Software
• Services

Software represents the largest market share

Based on the component, the global graph database market can be segmented into software and services. According to the report, software represents the largest market share.

The growth of the segment can be attributed to the increasing adoption of software-as-a-service (SaaS) by numerous companies to manage their complex data. Moreover, graph database market statistics by IMARC indicate that software deployment often involves upfront licensing or subscription fees, which can be more cost-effective in the long run, especially for organizations with ongoing data management needs.

Breakup by Type of Database:

• Relational (SQL)
• Non-Relational (NoSQL)

Relational (SQL) database exhibits a clear dominance in the market

Based on the type of database, the global graph database market can be segmented into relational (SQL) and non-relational (NoSQL). According to the report, relational (SQL) database exhibits a clear dominance in the market.

Integrating relational (SQL) databases with graph databases allows for leveraging the strengths of both models. SQL databases excel in structured data storage and complex queries, while graph databases specialize in managing and querying complex relationships. Combining them enables efficient storage of structured data alongside flexible representation and traversal of relationships, offering a comprehensive solution for diverse data management needs. This integration facilitates seamless data analysis, insights generation, and application development across a wide range of use cases, enhancing overall agility and scalability.

Breakup by Analysis Type:

• Path Analysis
• Connectivity Analysis
• Community Analysis
• Centrality Analysis

Path analysis holds the majority of the total market share

Based on the analysis type, the global graph database market can be segmented into path analysis, connectivity analysis, community analysis, and centrality analysis. According to the graph database market report, path analysis holds the majority of the total market share.

Path analysis in graph databases involves traversing the relationships between nodes to identify patterns or paths of interest. It enables querying and analyzing the sequence of nodes and edges to uncover insights or answer specific questions about the data. Path analysis is crucial for tasks like recommendation systems, fraud detection, and network analysis, offering valuable insights into the structure and behavior of interconnected data. By examining paths within the graph, organizations can derive actionable insights and make informed decisions based on the underlying relationships.

Breakup by Deployment Model:

• On-premises
• Cloud-based

On-premises model accounts for the majority of the total market share

Based on the deployment model, the global graph database market can be segmented into on-premises and cloud-based. According to the report, on-premises model accounts for the majority of the total market share.

On-premises deployment of graph databases involves installing and managing the database software within an organization's own data center or infrastructure. This approach offers greater control over data security, compliance, and performance compared to cloud-based alternatives. On-premises deployment is preferred in industries with strict regulatory requirements or sensitive data concerns, providing a dedicated environment for data management and processing. For instance, various companies operating in the BFSI sector are increasingly deploying graph databases within their own data centers, which is positively impacting the graph database market outlook. Manhattan-based FinTech Current's is increasingly utilizing graph database technology to build new financial services for customers and creating a set of 'hybrid finance' products based on integrated views of individuals and their family connections. Besides this, on-premises deployment of graph databases allows organizations to leverage existing infrastructure investments and tailor the deployment to their specific needs and preferences.

Breakup by Application:

• Fraud Detection and Risk Management
• Master Data Management
• Customer Analytics
• Identity and Access Management
• Recommendation Engine
• Privacy and Risk Compliance
• Others

Based on the application, the global graph database market can be segmented into fraud detection and risk management, master data management, customer analytics, identity and access management, recommendation engine, privacy and risk compliance, and others.

Graph databases are widely used in the banking and financial sector to detect and prevent fraudulent activities. For instance, graph databases such as Amazon Neptune are increasingly being used to perform queries, because they can traverse the data and perform calculations simultaneously. Graphs represent transactions and parties over a multi-connected network and discover patterns and chains of connections. As a result, graph databases are extensively being used in anti-money laundering (AML) applications, since they can help find patterns of suspicious transactions. Besides this, the demand for data compliance and the growing usage of master data management solutions in prominent companies to improve business operations is likely to fuel the graph database market revenue.

Breakup by Industry Vertical:

• BFSI
• Retail and E-Commerce
• IT and Telecom
• Healthcare and Life Science
• Government and Public Sector
• Media and Entertainment
• Manufacturing
• Transportation and Logistics
• Others

IT and telecom industry represents the largest market share

Based on the industry vertical, the global graph database market has been segmented into BFSI, retail and e-commerce, IT and telecom, healthcare and life science, government and public sector, media and entertainment, manufacturing, transportation and logistics, and others. According to the report, the IT and telecom industry represents the largest market share.

In the IT and telecom industry, graph databases are utilized for network topology management, fault analysis, and service provisioning. They enable real-time monitoring of network infrastructure, identifying bottlenecks, and optimizing resource allocation. Graph databases also facilitate customer relationship management by mapping complex interactions between customers, services, and devices, enhancing service personalization and troubleshooting efficiency.

Breakup by Region:

• North America
  • United States
  • Canada
• Asia-Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
• Latin America
  • Brazil
  • Mexico
  • Others
• Middle East and Africa

North America currently dominates the global market

The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America currently dominates the global market.

The expanding use of technology in the region is one of the main reasons promoting the growth of the graph database market in North America. The expansion of graph database players across the region, such as IBM, Microsoft, Neo4j, Oracle, etc., is anticipated to drive market expansion further. Moreover, the graph database market overview by IMARC indicates that the growth of R&D spending by significant regional economies is helping the development of new technologies in the North America graph database market. For instance, in June 2022, Ataccama, a unified data management and governance solutions provider, secured US$150 Million in a growth capital investment round, money that was used to finance the company's efforts to develop new products and expand its market presence.

COMPETITIVE LANDSCAPE:

The market research report has provided a comprehensive analysis of the competitive landscape and outlook. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

• Amazon Web Services Inc. (Amazon.com Inc.)
• Datastax Inc.
• Franz Inc.
• International Business Machines Corporation
• Marklogic Corporation
• Microsoft Corporation
• Neo4j Inc.
• Objectivity Inc.
• Oracle Corporation
• Stardog Union
• Tibco Software Inc.
• Tigergraph Inc.

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global Graph Database Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Component

• 6.1 Software
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 Services
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast

7 Market Breakup by Type of Database

• 7.1 Relational (SQL)
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Non-Relational (NoSQL)
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast

8 Market Breakup by Analysis Type

• 8.1 Path Analysis
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Connectivity Analysis
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast
• 8.3 Community Analysis
  • 8.3.1 Market Trends
  • 8.3.2 Market Forecast
• 8.4 Centrality Analysis
  • 8.4.1 Market Trends
  • 8.4.2 Market Forecast

9 Market Breakup by Deployment Model

• 9.1 On-premises
  • 9.1.1 Market Trends
  • 9.1.2 Market Forecast
• 9.2 Cloud-based
  • 9.2.1 Market Trends
  • 9.2.2 Market Forecast

10 Market Breakup by Application

• 10.1 Fraud Detection and Risk Management
  • 10.1.1 Market Trends
  • 10.1.2 Market Forecast
• 10.2 Master Data Management
  • 10.2.1 Market Trends
  • 10.2.2 Market Forecast
• 10.3 Customer Analytics
  • 10.3.1 Market Trends
  • 10.3.2 Market Forecast
• 10.4 Identity and Access Management
  • 10.4.1 Market Trends
  • 10.4.2 Market Forecast
• 10.5 Recommendation Engine
  • 10.5.1 Market Trends
  • 10.5.2 Market Forecast
• 10.6 Privacy and Risk Compliance
  • 10.6.1 Market Trends
  • 10.6.2 Market Forecast
• 10.7 Others
  • 10.7.1 Market Trends
  • 10.7.2 Market Forecast

11 Market Breakup by Industry Vertical

• 11.1 BFSI
  • 11.1.1 Market Trends
  • 11.1.2 Market Forecast
• 11.2 Retail and E-Commerce
  • 11.2.1 Market Trends
  • 11.2.2 Market Forecast
• 11.3 IT and Telecom
  • 11.3.1 Market Trends
  • 11.3.2 Market Forecast
• 11.4 Healthcare and Life Science
  • 11.4.1 Market Trends
  • 11.4.2 Market Forecast
• 11.5 Government and Public Sector
  • 11.5.1 Market Trends
  • 11.5.2 Market Forecast
• 11.6 Media and Entertainment
  • 11.6.1 Market Trends
  • 11.6.2 Market Forecast
• 11.7 Manufacturing
  • 11.7.1 Market Trends
  • 11.7.2 Market Forecast
• 11.8 Transportation and Logistics
  • 11.8.1 Market Trends
  • 11.8.2 Market Forecast
• 11.9 Others
  • 11.9.1 Market Trends
  • 11.9.2 Market Forecast

12 Market Breakup by Region

• 12.1 North America
  • 12.1.1 United States
    • 12.1.1.1 Market Trends
    • 12.1.1.2 Market Forecast
  • 12.1.2 Canada
    • 12.1.2.1 Market Trends
    • 12.1.2.2 Market Forecast
• 12.2 Asia-Pacific
  • 12.2.1 China
    • 12.2.1.1 Market Trends
    • 12.2.1.2 Market Forecast
  • 12.2.2 Japan
    • 12.2.2.1 Market Trends
    • 12.2.2.2 Market Forecast
  • 12.2.3 India
    • 12.2.3.1 Market Trends
    • 12.2.3.2 Market Forecast
  • 12.2.4 South Korea
    • 12.2.4.1 Market Trends
    • 12.2.4.2 Market Forecast
  • 12.2.5 Australia
    • 12.2.5.1 Market Trends
    • 12.2.5.2 Market Forecast
  • 12.2.6 Indonesia
    • 12.2.6.1 Market Trends
    • 12.2.6.2 Market Forecast
  • 12.2.7 Others
    • 12.2.7.1 Market Trends
    • 12.2.7.2 Market Forecast
• 12.3 Europe
  • 12.3.1 Germany
    • 12.3.1.1 Market Trends
    • 12.3.1.2 Market Forecast
  • 12.3.2 France
    • 12.3.2.1 Market Trends
    • 12.3.2.2 Market Forecast
  • 12.3.3 United Kingdom
    • 12.3.3.1 Market Trends
    • 12.3.3.2 Market Forecast
  • 12.3.4 Italy
    • 12.3.4.1 Market Trends
    • 12.3.4.2 Market Forecast
  • 12.3.5 Spain
    • 12.3.5.1 Market Trends
    • 12.3.5.2 Market Forecast
  • 12.3.6 Russia
    • 12.3.6.1 Market Trends
    • 12.3.6.2 Market Forecast
  • 12.3.7 Others
    • 12.3.7.1 Market Trends
    • 12.3.7.2 Market Forecast
• 12.4 Latin America
  • 12.4.1 Brazil
    • 12.4.1.1 Market Trends
    • 12.4.1.2 Market Forecast
  • 12.4.2 Mexico
    • 12.4.2.1 Market Trends
    • 12.4.2.2 Market Forecast
  • 12.4.3 Others
    • 12.4.3.1 Market Trends
    • 12.4.3.2 Market Forecast
• 12.5 Middle East and Africa
  • 12.5.1 Market Trends
  • 12.5.2 Market Breakup by Country
  • 12.5.3 Market Forecast

13 SWOT Analysis

• 13.1 Overview
• 13.2 Strengths
• 13.3 Weaknesses
• 13.4 Opportunities
• 13.5 Threats

14 Value Chain Analysis

15 Porters Five Forces Analysis

• 15.1 Overview
• 15.2 Bargaining Power of Buyers
• 15.3 Bargaining Power of Suppliers
• 15.4 Degree of Competition
• 15.5 Threat of New Entrants
• 15.6 Threat of Substitutes

16 Price Analysis

17 Competitive Landscape

• 17.1 Market Structure
• 17.2 Key Players
• 17.3 Profiles of Key Players
  • 17.3.1 Amazon Web Services Inc. (Amazon.com Inc.)
    • 17.3.1.1 Company Overview
    • 17.3.1.2 Product Portfolio
    • 17.3.1.3 SWOT Analysis
  • 17.3.2 Datastax Inc.
    • 17.3.2.1 Company Overview
    • 17.3.2.2 Product Portfolio
  • 17.3.3 Franz Inc.
    • 17.3.3.1 Company Overview
    • 17.3.3.2 Product Portfolio
  • 17.3.4 International Business Machines Corporation
    • 17.3.4.1 Company Overview
    • 17.3.4.2 Product Portfolio
    • 17.3.4.3 Financials
    • 17.3.4.4 SWOT Analysis
  • 17.3.5 Marklogic Corporation
    • 17.3.5.1 Company Overview
    • 17.3.5.2 Product Portfolio
  • 17.3.6 Microsoft Corporation
    • 17.3.6.1 Company Overview
    • 17.3.6.2 Product Portfolio
    • 17.3.6.3 Financials
    • 17.3.6.4 SWOT Analysis
  • 17.3.7 Neo4j Inc.
    • 17.3.7.1 Company Overview
    • 17.3.7.2 Product Portfolio
  • 17.3.8 Objectivity Inc.
    • 17.3.8.1 Company Overview
    • 17.3.8.2 Product Portfolio
  • 17.3.9 Oracle Corporation
    • 17.3.9.1 Company Overview
    • 17.3.9.2 Product Portfolio
    • 17.3.9.3 Financials
    • 17.3.9.4 SWOT Analysis
  • 17.3.10 Stardog Union
    • 17.3.10.1 Company Overview
    • 17.3.10.2 Product Portfolio
  • 17.3.11 Tibco Software Inc.
    • 17.3.11.1 Company Overview
    • 17.3.11.2 Product Portfolio
    • 17.3.11.3 SWOT Analysis
  • 17.3.12 Tigergraph Inc.
    • 17.3.12.1 Company Overview
    • 17.3.12.2 Product Portfolio