GVR Report cover Graph Technology Market Size, Share & Trends Report

Graph Technology Market Size, Share & Trends Analysis Report By Component, By Graph Type, By Database Type, By Analysis Model, By Deployment, By Application, By Industry, By Region, And Segment Forecasts, 2023 - 2030

  • Report ID: GVR-4-68040-134-7
  • Number of Report Pages: 140
  • Format: PDF, Horizon Databook
  • Historical Range: 2018 - 2021
  • Forecast Period: 2023 - 2030 
  • Industry: Technology

Graph Technology Market Size & Trends

The global graph technology market size was estimated at USD 3.26 billion in 2022 and is anticipated to grow at a compound annual growth rate (CAGR) of 22.0% from 2023 to 2030. The market is experiencing remarkable growth, largely driven by a confluence of factors that are reshaping how data is managed and leveraged across various industries. The growth is attributable to the increasing complexity of data in today's digital landscape. Traditional relational databases struggle when dealing with data that is highly interconnected and semi-structured. Graph databases, on the other hand, excel in this arena by allowing organizations to model and query relationships between data points with ease. This capability has led to their widespread adoption in sectors ranging from social media (where understanding and analyzing social connections are vital) to logistics (where optimizing supply chain networks demands a sophisticated understanding of relationships).

U.S. Graph Technology Market size and growth rate, 2023 - 2030

Knowledge graphs have emerged as a pivotal driver for the growth of the market. Organizations are now recognizing the immense value in creating structured representations of their data, facilitating better decision-making and enhanced data discovery. These knowledge graphs, built on graph technology, empower organizations to connect disparate data sources, unlocking deeper insights and facilitating smarter, data-driven decisions. Furthermore, knowledge graphs not only aid in uncovering hidden relationships within data but also enable organizations to respond swiftly to changing market dynamics and customer demands, making them an integral component of today's data-driven landscape.

The explosive growth of the Internet of Things (IoT) has inundated businesses with data characterized by intricate relationships. Graph databases are ideally suited to handle IoT data, given their inherent ability to represent complex connections. Industries such as healthcare, logistics, and smart cities are leveraging graph technology to make sense of this deluge of data and turn it into actionable insights. Moreover, real-time data processing is another driving force behind the adoption of graph technology. These databases shine when it comes to handling real-time queries and updates, making them indispensable for applications like fraud detection, recommendation systems, and social network analysis.

Furthermore, the stringent regulatory environment in industries like finance and healthcare has mandated robust data management and privacy measures. Graph databases offer a compelling solution by providing fine-grained control over data access and relationships. This not only ensures compliance with regulations such as GDPR and HIPAA but also enhances overall data security. In addition, the open-source nature of many graph database solutions has also contributed significantly to their adoption. Projects like Apache TinkerPop and Neo4j have fostered innovation and lowered the barrier to entry for organizations looking to experiment with and implement graph technology.

One significant restraint in the market is the relatively steep learning curve associated with graph databases and their query languages. Graph databases, with their unique data modeling approach, can be challenging for organizations accustomed to traditional relational databases. Overcoming this restraint requires investment in education and training. Companies can offer comprehensive training programs to their employees, enabling them to grasp the fundamentals of graph databases and query languages like Cypher effectively.

Component Insights

In terms of component, the software segment dominated the market in 2022 with a revenue share of more than 69.0%. Software has predominantly dominated the market due to its flexibility, scalability, and ease of integration. Unlike hardware-based solutions that often require substantial investments in infrastructure, software components can be deployed on existing hardware, reducing both cost and implementation complexity. This accessibility has democratized the adoption of graph technology, making it more feasible for a wide range of organizations, from startups to large enterprises.

The services segment is anticipated to register the fastest CAGR of 23.7% over the forecast period. The services segment is emerging at the fastest rate primarily due to the increasing recognition among organizations that the successful adoption of graph technology extends beyond just acquiring software tools. Services such as consulting, training, and support have become indispensable in helping businesses harness the full potential of graph databases. Many enterprises are now realizing that they require specialized expertise to design and implement effective graph data models, query languages, and analytical frameworks tailored to their unique needs.

Database Type Insights

Based on database type, the relational (SQL) segment dominated the market in 2022 with a revenue share of more than 78.0%. SQL databases gained prominence because they effectively manage structured data in tabular formats, which was the predominant data structure for many years. They offer a mature and standardized query language that is widely understood and supported, making it easier for organizations to work with data. Moreover, relational databases have been considered reliable and transactional, making them suitable for critical applications like financial systems and e-commerce platforms.

The non-relational (No SQL) segment is anticipated to register significant growth over the forecast period. Non-relational databases are experiencing rapid growth due to their ability to efficiently handle complex, interconnected data structures. Unlike traditional relational databases, which excel at managing structured data in tabular formats, non-relational graph databases are purpose-built for modeling and querying relationships between data points. In today's data landscape, where the importance of understanding and leveraging connections between data elements is paramount, these databases provide a more intuitive and efficient solution. This versatility has made them increasingly popular in a wide range of applications, from social networks and recommendation engines to fraud detection and supply chain optimization.

Graph Type Insights

In terms of graph type, the property graph segment dominated the market in 2022 with the largest revenue share of over 41.0%. Property graphs offer a versatile and intuitive way to model real-world data by associating properties or attributes with nodes and edges, allowing for rich, context-aware representations. This flexibility makes property graphs suitable for a wide range of applications where data entities have attributes that are crucial for analysis. Moreover, the query language for property graphs, such as Cypher, is designed to be user-friendly and expressive, making it accessible to a broader audience, including developers and data analysts with varying levels of expertise.

The hypergraph segment is anticipated to register the fastest CAGR of 23.6% over the forecast period. Hypergraphs are gaining rapid traction due to their unique ability to represent complex relationships in data. Unlike traditional property graphs where edges connect two nodes, hypergraphs allow for edges that connect any number of nodes, enabling the modeling of higher-order relationships more intuitively. This capability is especially valuable in domains like knowledge graphs, biology, social networks, and recommendation systems, where data entities often participate in multifaceted relationships.

Analysis Model Insights

Based on the analysis model, the path analysis segment dominated the market in 2022 with a revenue share of over 31.0%. Path analysis has emerged as a dominant force due to its profound utility in a myriad of applications. Path analysis provides unparalleled insights by enabling the exploration and understanding of intricate relationships within data. It is a fundamental concept underpinning various fields, from social network analysis and recommendation engines to cybersecurity and logistics optimization.

The community analysis segment is anticipated to register significant growth. Community analysis is rapidly emerging as a dominant force due to its ability to unveil hidden structures and insights within interconnected data. In today's information-rich world, understanding the communities or clusters within a network is paramount for various applications, including social network analysis, fraud detection, and recommendation systems. Graph technology, equipped with specialized algorithms and techniques, has made community analysis more accessible and efficient.

Deployment Insights

In terms of deployment, the on-premise segment dominated the market in 2022 with the largest revenue share over 58.0%. Concerns around data security and privacy often lead organizations, particularly in highly regulated industries like finance and healthcare, to opt for on-premise solutions where they can maintain direct control over their data. Moreover, latency-sensitive applications, such as real-time fraud detection and IoT analytics, benefit from on-premise deployment as they can reduce network latency and ensure immediate access to data. In addition, some organizations have legacy systems and infrastructure in place, making on-premise deployment a more practical choice for integration.

The cloud segment is anticipated to register the fastest CAGR of 24.6% over the forecast period. Cloud deployment is experiencing rapid growth due to its inherent advantages in scalability, cost-efficiency, and accessibility. Cloud-based graph databases offer organizations the ability to scale their resources up or down on demand, making it easier to accommodate fluctuating workloads without significant upfront infrastructure investments. Furthermore, the cloud's pay-as-you-go pricing model allows companies to avoid overprovisioning and pay only for the resources they consume, leading to cost savings.

Application Insights

Based on application, the data management & analysis segment dominated the market in 2022 with a revenue share of over 25.0%. The data management and analysis segment dominates the market due to the increasing recognition of the pivotal role data plays in today's digital economy. Graph databases, with their ability to efficiently model and query complex relationships, are ideally suited to address the growing challenges of data integration, connectivity, and insights extraction. Industries ranging from e-commerce and social media to healthcare and logistics rely on graph technology to gain deeper insights into their data ecosystems, improve decision-making, and enhance customer experiences.

The customer analysis segment is anticipated to register the fastest CAGR of 23.7% over the forecast period. The segment is experiencing rapid growth due to the increasing recognition of its pivotal role in shaping business strategies. In today's data-driven era, companies are harnessing advanced analytics tools to gain profound insights into customer behavior, preferences, and trends. This analysis enables businesses to personalize their products and services, enhance customer experiences, and tailor marketing efforts for maximum impact. Moreover, the proliferation of digital platforms and the availability of vast amounts of customer data have made it more accessible and cost-effective to conduct in-depth customer analysis.

Industry Insights

Based on industry, the IT & telecom segment dominated the market in 2022 with a global revenue share of over 19.0%. The IT & telecom industry dominates the market due to its inherent need for effective data management and analysis in the rapidly evolving digital landscape. These sectors deal with vast volumes of interconnected data, from network topologies to user behavior patterns and service provisioning. Graph technology's ability to model and query complex relationships offers a strategic advantage in optimizing network performance, identifying potential cybersecurity threats, and enhancing customer experiences through personalized services.

Global Graph Technology Market share and size, 2022

The government and public sector segment is anticipated to register the fastest CAGR of 24.1% over the forecast period. The government and public sector are rapidly emerging as key players in the market due to their pressing need for efficient data management, transparency, and enhanced decision-making. Government agencies deal with vast and diverse datasets that often contain intricate relationships, such as citizen records, infrastructure networks, and public services. Graph technology's ability to model, query, and analyze complex relationships is proving invaluable in optimizing resource allocation, enhancing public service delivery, and improving policy making.

Regional Insights

North America dominated the market in 2022 with a revenue share of more than 28.0%. North America is home to a thriving technology ecosystem, including Silicon Valley, which has fostered innovation and investment in emerging technologies like graph databases. The region boasts a high concentration of technology startups and established enterprises that readily adopt cutting-edge solutions to remain competitive. Moreover, North American organizations often encounter complex data challenges, and they have been quick to recognize the advantages of graph technology in addressing these issues, particularly in industries like e-commerce, healthcare, and finance.

Graph Technology Market Trends by Region, 2023 - 2030

The Asia Pacific regional market is anticipated to register the fastest CAGR of 24.3% over the forecast period. The Asia Pacific region is emerging at an exceptionally fast rate due to the convergence of factors that align with the region's rapid digital transformation. As businesses across the Asia Pacific region embrace data-driven strategies, the need to harness complex relationships within their data has become increasingly evident. Graph technology offers a solution uniquely suited to address this demand. Moreover, the region's diverse economies, from established technology hubs like India and Singapore to emerging markets in Southeast Asia, provide a dynamic landscape for technology adoption and innovation.

Key Companies & Market Share Insights

In January 2023, Katana Graph, the provider of an AI-driven Graph Intelligence Platform, unveiled an expansion of its collaboration with Intel. This move aims to expedite the process through which data scientists can efficiently derive profound insights from extensive interconnected datasets. Katana Graph has maintained a robust partnership with Intel, jointly developing a distributed Graph Neural Network (GNN) training solution. Notably, this solution has already demonstrated impressive speed, showcasing a fourfold improvement in performance on 4th Gen Intel Xeon scalable processors compared to existing commercial alternatives in the market.

The competitive landscape of the market is dynamic and evolving. Established players like Neo4j continue to lead with their mature, feature-rich, and scalable graph database offerings, while open-source solutions like Apache TinkerPop provide accessible alternatives. New entrants and startups, such as TigerGraph and Stardog, are gaining traction by offering innovative approaches to graph data management and analysis. Some prominent players in the global graph technology market include:

  • Oracle Corporation

  • IBM

  • Neo4j, Inc.

  • Stardog

  • Amazon Web Services, Inc.

  • Microsoft

  • ArangoDB, Inc.

  • TigerGraph

  • Progress Software Corporation (MarkLogic)

  • DataStax

Graph Technology Market Report Scope

Report Attribute


Market size value in 2023

USD 3.93 billion

Revenue forecast in 2030

USD 15.80 billion

Growth rate

CAGR of 22.0% from 2023 to 2030

Base year of estimation


Historical data

2017 - 2021

Forecast period

2023 - 2030

Quantitative units

Revenue in USD million/billion and CAGR from 2023 to 2030

Report coverage

Revenue forecast, company market share, competitive landscape, growth factors, and trends

Segments covered

Component, database type, graph type, analysis model, deployment, application, industry, region

Regional scope

North America; Europe; Asia Pacific; Latin America; Middle East & Africa

Country scope

U.S.; Canada; UK; Germany; France; China; India; Japan; South Korea; Australia; Brazil; Mexico; Kingdom of Saudi Arabia (KSA); UAE; South Africa

Key companies profiled

Oracle Corporation; IBM; Neo4j, Inc.; Stardog; Amazon Web Services, Inc.; Microsoft; ArangoDB, Inc.; TigerGraph; Progress Software Corporation (MarkLogic); DataStax

Customization scope

Free report customization (equivalent to up to 8 analysts' working days) with purchase. Addition or alteration to country, regional & segment scope

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Global Graph Technology Market Report Segmentation

The report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global graph technology market report based on component, database type, graph type, analysis model, deployment, application, industry, and region:

  • Component Outlook (Revenue, USD Million, 2017 - 2030)

    • Software

    • Services

  • Database Type Outlook (Revenue, USD Million, 2017 - 2030)

    • Relational (SQL)

    • Non-relational (No SQL)

  • Graph Type Outlook (Revenue, USD Million, 2017 - 2030)

    • Property Graph

    • Resource Description Framework (RDF)

    • Hypergraph

  • Analysis Model Outlook (Revenue, USD Million, 2017 - 2030)

    • Path Analysis

    • Connectivity Analysis

    • Community Analysis

    • Centrality Analysis

  • Deployment Outlook (Revenue, USD Million, 2017 - 2030)

    • Cloud

    • On-premise

  • Application Outlook (Revenue, USD Million, 2017 - 2030)

    • Fraud Detection

    • Data Management & Analysis

    • Customer Analysis

    • Identity & Access Management

    • Compliance & Risk

    • Others

  • Industry Outlook (Revenue, USD Million, 2017 - 2030)

    • BFSI

    • Retail & E-commerce

    • IT & Telecom

    • Healthcare & Life Science

    • Government & Public Sector

    • Media & Entertainment

    • Supply Chain & Logistics

    • Others

  • Regional Outlook (Revenue, USD Million, 2017 - 2030)

    • North America

      • U.S.

      • Canada

    • Europe

      • UK

      • Germany

      • France

    • Asia Pacific

      • China

      • India

      • Japan

      • South Korea

      • Australia

    • Latin America

      • Brazil

      • Mexico

    • Middle East & Africa

      • Kingdom of Saudi Arabia (KSA)

      • UAE

      • South Africa

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