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1841571

圖形資料庫市場 - 全球產業規模、佔有率、趨勢、機會和預測，按組件、類型、最終用戶、地區和競爭細分，2020-2030 年預測

Graph Database Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component, By Type, By End-User, By Region & Competition, 2020-2030F
出版日期: | 出版商: TechSci Research | 英文 185 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2024 年全球圖形資料庫市場價值為 28.9 億美元，預計到 2030 年將達到 120.5 億美元，預測期內複合年成長率為 26.67%。

市場概況
預測期 2026-2030
2024年市場規模 28.9億美元
2030年市場規模 120.5億美元
2025-2030年複合年成長率 26.67%
成長最快的領域 資源描述框架
最大的市場 北美洲

圖形資料庫市場指的是資料庫產業中一個更廣泛的領域，專注於利用由節點、邊和屬性組成的圖形結構來管理、儲存和分析高度互聯資料的解決方案。與依賴僵化表格格式的傳統關係資料庫不同，圖形資料庫強調資料點之間的關係，從而能夠更快、更直覺地分析複雜資料集。這種能力使得圖形資料庫在詐欺偵測、推薦引擎、供應鏈最佳化、網路安全、社交網路分析和知識圖譜等應用中尤為重要。

由於非結構化和半結構化資料的指數級成長、即時決策的需求以及對能夠發現關係資料庫通常無法有效捕捉的隱藏模式和關聯的系統的需求，各行各業的企業擴大採用圖形資料庫解決方案。隨著企業轉型為高度依賴互聯資料模型的高階分析、人工智慧和機器學習技術。此外，對數位轉型、雲端技術應用以及巨量資料分析工具整合的日益關注，也推動了對圖形資料庫解決方案的需求不斷成長。

醫療保健、金融服務、零售和電信等行業也將推動市場成長，這些行業正在積極利用圖數據庫來增強客戶參與度、提升風險管理並簡化營運。此外，包括雲端原生圖資料庫和混合部署模型在內的持續技術進步正在擴展可存取性和可擴展性，使大型企業和中小型企業都能有效地利用這些解決方案。

領先企業的策略性投資，以及不斷成長的合作夥伴關係，將進一步加速圖形資料庫與企業系統的整合，將進一步加速其應用。總體而言，未來幾年，圖形資料庫市場將繼續成長，這得益於對智慧資料管理解決方案日益成長的需求，這些解決方案能夠提供速度、可擴展性，並能夠更深入地洞察不同資料集之間的複雜關係。

關鍵市場促進因素

資料管理的數量和複雜性不斷增加

根據聯合國預測，資料量將增加五倍以上，從 2018 年的 33 zetta位元組增加到 2025 年的 175 zetta位元組 。

主要市場挑戰

與現有系統整合的複雜性

主要市場趨勢

圖形資料庫中人工智慧和機器學習整合的應用日益增多

目錄

第 1 章：產品概述

第2章：研究方法

第3章：執行摘要

第4章：顧客之聲

第5章：全球圖形資料庫市場展望

  • 市場規模和預測
    • 按價值
  • 市場佔有率和預測
    • 按組件（軟體、服務）
    • 按類型（（資源描述框架、屬性圖）
    • 按最終用戶（銀行、金融服務和保險、零售和電子商務、資訊科技和電信、醫療保健和生命科學、政府和國防、運輸和物流、製造業、其他）
    • 按地區（北美、歐洲、南美、中東和非洲、亞太地區）
  • 按公司分類（2024 年）
  • 市場地圖

第6章：北美圖形資料庫市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 北美：國家分析
    • 美國
    • 加拿大
    • 墨西哥

第7章：歐洲圖形資料庫市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 歐洲：國家分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙

第8章：亞太地區圖形資料庫市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

第9章：中東和非洲圖形資料庫市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非

第10章：南美洲圖形資料庫市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 南美洲：國家分析
    • 巴西
    • 哥倫比亞
    • 阿根廷

第 11 章：市場動態

  • 驅動程式
  • 挑戰

第 12 章：市場趨勢與發展

  • 合併與收購（如有）
  • 產品發布（如有）
  • 最新動態

第13章：公司簡介

  • Neo4j Inc.
  • Oracle Corporation
  • IBM Corporation
  • Amazon Web Services Inc.
  • Microsoft Corporation
  • TigerGraph Inc.
  • Ontotext AD
  • DataStax Inc.
  • Franz Inc.
  • ArangoDB GmbH

第 14 章：策略建議

第15章調查會社について,免責事項

簡介目錄
Product Code: 30537

Global Graph Database Market was valued at USD 2.89 billion in 2024 and is expected to reach USD 12.05 billion by 2030 with a CAGR of 26.67% during the forecast period.

Market Overview
Forecast Period2026-2030
Market Size 2024USD 2.89 Billion
Market Size 2030USD 12.05 Billion
CAGR 2025-203026.67%
Fastest Growing SegmentResource Description Framework
Largest MarketNorth America

The graph database market refers to the sector within the broader database industry that focuses on solutions designed to manage, store, and analyze highly interconnected data using graph structures composed of nodes, edges, and properties. Unlike traditional relational databases that rely on rigid tabular formats, graph databases emphasize the relationships between data points, enabling faster and more intuitive analysis of complex datasets. This capability makes graph databases especially valuable in applications such as fraud detection, recommendation engines, supply chain optimization, cybersecurity, social network analysis, and knowledge graphs.

Businesses across industries are increasingly adopting graph database solutions due to the exponential growth of unstructured and semi-structured data, the need for real-time decision-making, and the demand for systems that can uncover hidden patterns and connections that relational databases often fail to capture effectively. The market is set to rise significantly as organizations transition towards advanced analytics, artificial intelligence, and machine learning technologies that depend heavily on interconnected data models. Additionally, the increasing focus on digital transformation, cloud adoption, and the integration of big data analytics tools is driving higher demand for graph database solutions.

The market will also witness growth from sectors like healthcare, financial services, retail, and telecommunications, which are actively leveraging graph databases to strengthen customer engagement, enhance risk management, and streamline operations. Furthermore, continuous technological advancements, including cloud-native graph databases and hybrid deployment models, are expanding accessibility and scalability, enabling both large enterprises and small to medium-sized businesses to utilize these solutions effectively.

Strategic investments from leading players, along with growing partnerships to integrate graph databases into enterprise systems, will further accelerate adoption. Overall, the graph database market will continue to rise in the coming years, driven by the increasing need for intelligent data management solutions that offer speed, scalability, and deeper insights into complex relationships across diverse datasets.

Key Market Drivers

Escalating Volume and Complexity of Data Management

In the dynamic realm of digital transformation, the Graph Database Market is significantly propelled by the escalating volume and complexity of data management, as organizations grapple with an unprecedented influx of interconnected data from diverse sources that traditional relational databases struggle to handle efficiently, thereby necessitating graph-based solutions that excel in modeling relationships, traversing networks, and delivering real-time insights for strategic decision-making.

The exponential growth in data generation, fueled by digital interactions, sensor outputs, and transactional records, creates intricate webs of dependencies that demand agile querying capabilities, where graph databases shine by enabling rapid pathfinding, pattern recognition, and anomaly detection without the performance bottlenecks associated with join-heavy operations in conventional systems. This driver is particularly evident in sectors like finance, where fraud detection relies on analyzing transaction graphs to uncover hidden connections, or in social media platforms that leverage user interaction networks to enhance engagement and content recommendation, underscoring the market's shift towards technologies that prioritize relational depth over mere volume storage.

Enterprises are increasingly adopting graph databases to harness big data analytics, integrating them with data lakes and warehouses to facilitate holistic views of entity relationships, thereby improving operational efficiency and reducing time-to-insight in competitive landscapes where data silos impede innovation. The convergence of structured and unstructured data further amplifies this need, as graph models accommodate heterogeneous formats seamlessly, allowing for semantic enrichment through ontologies and knowledge graphs that support advanced applications in artificial intelligence and machine learning.

Regulatory imperatives around data governance and lineage tracing also bolster this driver, compelling organizations to implement traceable data architectures where graph databases provide auditable trails of relationships and provenance, ensuring compliance with standards like the General Data Protection Regulation while mitigating risks of data mismanagement. Moreover, the rise of edge computing and distributed systems exacerbates data complexity by introducing latency-sensitive scenarios, where graph databases offer decentralized querying and synchronization mechanisms that maintain consistency across global footprints, driving market adoption among multinational corporations seeking resilient data infrastructures.

Technological advancements in graph processing engines, such as those supporting property graphs and RDF triples, enable scalable handling of petabyte-scale datasets, attracting investments from cloud providers who embed these capabilities into their platforms to cater to hybrid workloads. The economic incentives are clear, as inefficient data management leads to substantial opportunity costs, prompting chief information officers to prioritize graph solutions that deliver measurable returns through enhanced analytics and predictive modeling, particularly in industries like telecommunications where network topology optimization is critical for service reliability.

Consumer-driven trends, including personalized experiences in e-commerce, rely on graph-powered recommendation engines that map user preferences and behaviors dynamically, further expanding the market's footprint beyond enterprise confines into consumer-facing applications. Collaborative ecosystems, fostered by open-source communities around projects like Neo4j and JanusGraph, accelerate innovation by providing extensible frameworks that lower entry barriers for small and medium enterprises, democratizing access to sophisticated data management tools. As quantum computing looms, the potential for graph databases to interface with quantum algorithms for complex optimization problems positions them as future-proof assets, encouraging proactive market investments in research and development.

In addition, the integration with blockchain for immutable relationship tracking enhances trust in data ecosystems, particularly in supply chain management where provenance graphs prevent counterfeiting and ensure transparency. The global push towards smart cities and interconnected infrastructures generates vast relational datasets from urban sensors and citizen interactions, creating opportunities for graph databases to underpin intelligent planning and resource allocation.

Ultimately, the interplay of data deluge, relational intricacies, and analytical demands cements this driver as pivotal, ensuring the Graph Database Market thrives by offering unparalleled efficiency in navigating the data labyrinth that defines the modern business environment, fostering agility, insight, and competitive differentiation in an era where data relationships are the new currency of value creation.

According to the United Nations, the amount of data is projected to increase more than fivefold, rising from 33 zettabytes in 2018 to 175 zettabytes by 2025.

The United Nations highlights that global data volume is set to reach 175 zettabytes by 2025, a surge from 33 zettabytes in 2018, driven by digital activities and IoT. World Bank data supports this, noting rapid expansion in data infrastructure needs. OECD reports indicate trade-related data growth, with merchandise exports up 2.0% in Q1 2025. IMF projections align with this trend, emphasizing data's role in economic performance. These figures underscore the imperative for advanced data management solutions like graph databases.

Key Market Challenges

Complexity of Integration with Existing Systems

One of the most pressing challenges in the graph database market is the complexity associated with integrating these solutions with existing enterprise systems and infrastructures. Organizations across industries have long relied on traditional relational databases and structured data management frameworks that follow tabular models. Over time, these systems have accumulated extensive volumes of data, which are deeply embedded into enterprise operations, workflows, and business processes. Transitioning from such long-established systems to graph databases often proves to be both technically and operationally difficult. The fundamental difference in data architecture between relational and graph models requires organizations to restructure their existing data sets, modify application frameworks, and adapt to new query languages such as Cypher or Gremlin. This integration process not only demands a significant investment of time and resources but also introduces risks related to data inconsistency, data migration failures, and disruptions in critical operations.

Furthermore, enterprises often operate in hybrid environments that combine on-premises infrastructures with cloud-based deployments. Integrating graph databases into such environments requires specialized expertise to ensure seamless interoperability, data synchronization, and compliance with security protocols. The lack of standardization in graph database technologies further complicates integration efforts. Unlike relational databases that follow the widely accepted Structured Query Language, graph databases have diverse query languages and frameworks that differ from vendor to vendor. This lack of uniformity makes it difficult for organizations to achieve compatibility across multiple platforms, leading to vendor lock-in and reduced flexibility.

Another dimension of this challenge is the cultural and skill-related barriers within enterprises. Information technology teams and data scientists who are traditionally trained in relational database management often face steep learning curves when working with graph data structures and algorithms. This skill gap necessitates additional training, recruitment, and upskilling efforts, thereby increasing operational costs. Many enterprises, particularly small and medium-sized businesses, find these requirements burdensome, which slows down the adoption of graph database technologies.

The high level of customization required for successful deployment adds to the complexity. Each organization has unique requirements depending on its industry, scale, and specific use cases, which means graph database solutions cannot be deployed as standardized off-the-shelf products. Tailored development, integration of application programming interfaces, and alignment with enterprise resource planning or customer relationship management systems are essential, further extending implementation timelines. In addition, enterprises must also ensure that the adoption of graph databases does not negatively impact system performance, especially in mission-critical operations where downtime can result in significant financial and reputational losses.

Key Market Trends

Growing Adoption of Artificial Intelligence and Machine Learning Integration in Graph Databases

One of the most significant trends shaping the graph database market is the increasing integration of artificial intelligence and machine learning technologies. Businesses across industries are seeking advanced solutions that can analyze complex, interconnected datasets in real time, and graph databases are emerging as a natural fit due to their ability to represent relationships between data points effectively. Artificial intelligence and machine learning algorithms rely heavily on connected datasets for training and predictive modeling, and graph databases provide the underlying framework to store, process, and query such datasets with efficiency.

For example, organizations are using graph databases to detect patterns in financial fraud, cybersecurity threats, customer behavior, and supply chain optimization, all of which require high-speed insights derived from relationships among millions of nodes and edges. The increasing focus on personalization in e-commerce and digital services is another driver of this trend, as graph databases empower recommendation engines to process dynamic user data and generate accurate suggestions. Furthermore, as machine learning and deep learning models become more sophisticated, the reliance on graph-based data representation will continue to expand.

The trend is also reinforced by rising investments from enterprises in hybrid analytics platforms that combine graph databases with artificial intelligence-powered decision-making tools. As artificial intelligence adoption deepens across sectors such as healthcare, finance, telecommunications, and retail, the integration of these technologies with graph databases will not only drive efficiency but also accelerate the scalability and flexibility of data-driven strategies, positioning graph databases as a critical enabler of innovation.

Key Market Players

  • Neo4j Inc.
  • Oracle Corporation
  • IBM Corporation
  • Amazon Web Services Inc.
  • Microsoft Corporation
  • TigerGraph Inc.
  • Ontotext AD
  • DataStax Inc.
  • Franz Inc.
  • ArangoDB GmbH

Report Scope:

In this report, the Global Graph Database Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Graph Database Market, By Component:

  • Software
  • Services

Graph Database Market, By Type:

  • Resource Description Framework
  • Property Graph

Graph Database Market, By End-User:

  • Banking, Financial Services, and Insurance
  • Retail and E-commerce
  • Information Technology and Telecommunications
  • Healthcare and Life Sciences
  • Government and Defense
  • Transportation and Logistics
  • Manufacturing
  • Others

Graph Database Market, By Region:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • France
    • United Kingdom
    • Italy
    • Spain
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Asia-Pacific
    • China
    • India
    • Japan
    • South Korea
    • Australia
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • South Africa

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Graph Database Market.

Available Customizations:

Global Graph Database Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, and Trends

4. Voice of Customer

5. Global Graph Database Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Component (Software, Services)
    • 5.2.2. By Type ((Resource Description Framework, Property Graph)
    • 5.2.3. By End-User (Banking, Financial Services, and Insurance, Retail and E-commerce, Information Technology and Telecommunications, Healthcare and Life Sciences, Government and Defense, Transportation and Logistics, Manufacturing, Others)
    • 5.2.4. By Region (North America, Europe, South America, Middle East & Africa, Asia Pacific)
  • 5.3. By Company (2024)
  • 5.4. Market Map

6. North America Graph Database Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Component
    • 6.2.2. By Type
    • 6.2.3. By End-User
    • 6.2.4. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Graph Database Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Component
        • 6.3.1.2.2. By Type
        • 6.3.1.2.3. By End-User
    • 6.3.2. Canada Graph Database Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Component
        • 6.3.2.2.2. By Type
        • 6.3.2.2.3. By End-User
    • 6.3.3. Mexico Graph Database Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Component
        • 6.3.3.2.2. By Type
        • 6.3.3.2.3. By End-User

7. Europe Graph Database Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Component
    • 7.2.2. By Type
    • 7.2.3. By End-User
    • 7.2.4. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Graph Database Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Component
        • 7.3.1.2.2. By Type
        • 7.3.1.2.3. By End-User
    • 7.3.2. France Graph Database Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Component
        • 7.3.2.2.2. By Type
        • 7.3.2.2.3. By End-User
    • 7.3.3. United Kingdom Graph Database Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Component
        • 7.3.3.2.2. By Type
        • 7.3.3.2.3. By End-User
    • 7.3.4. Italy Graph Database Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Component
        • 7.3.4.2.2. By Type
        • 7.3.4.2.3. By End-User
    • 7.3.5. Spain Graph Database Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Component
        • 7.3.5.2.2. By Type
        • 7.3.5.2.3. By End-User

8. Asia Pacific Graph Database Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Component
    • 8.2.2. By Type
    • 8.2.3. By End-User
    • 8.2.4. By Country
  • 8.3. Asia Pacific: Country Analysis
    • 8.3.1. China Graph Database Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Component
        • 8.3.1.2.2. By Type
        • 8.3.1.2.3. By End-User
    • 8.3.2. India Graph Database Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Component
        • 8.3.2.2.2. By Type
        • 8.3.2.2.3. By End-User
    • 8.3.3. Japan Graph Database Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Component
        • 8.3.3.2.2. By Type
        • 8.3.3.2.3. By End-User
    • 8.3.4. South Korea Graph Database Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Component
        • 8.3.4.2.2. By Type
        • 8.3.4.2.3. By End-User
    • 8.3.5. Australia Graph Database Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Component
        • 8.3.5.2.2. By Type
        • 8.3.5.2.3. By End-User

9. Middle East & Africa Graph Database Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Component
    • 9.2.2. By Type
    • 9.2.3. By End-User
    • 9.2.4. By Country
  • 9.3. Middle East & Africa: Country Analysis
    • 9.3.1. Saudi Arabia Graph Database Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Component
        • 9.3.1.2.2. By Type
        • 9.3.1.2.3. By End-User
    • 9.3.2. UAE Graph Database Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Component
        • 9.3.2.2.2. By Type
        • 9.3.2.2.3. By End-User
    • 9.3.3. South Africa Graph Database Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Component
        • 9.3.3.2.2. By Type
        • 9.3.3.2.3. By End-User

10. South America Graph Database Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Component
    • 10.2.2. By Type
    • 10.2.3. By End-User
    • 10.2.4. By Country
  • 10.3. South America: Country Analysis
    • 10.3.1. Brazil Graph Database Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Component
        • 10.3.1.2.2. By Type
        • 10.3.1.2.3. By End-User
    • 10.3.2. Colombia Graph Database Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Component
        • 10.3.2.2.2. By Type
        • 10.3.2.2.3. By End-User
    • 10.3.3. Argentina Graph Database Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Component
        • 10.3.3.2.2. By Type
        • 10.3.3.2.3. By End-User

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends and Developments

  • 12.1. Merger & Acquisition (If Any)
  • 12.2. Product Launches (If Any)
  • 12.3. Recent Developments

13. Company Profiles

  • 13.1. Neo4j Inc.
    • 13.1.1. Business Overview
    • 13.1.2. Key Revenue and Financials
    • 13.1.3. Recent Developments
    • 13.1.4. Key Personnel
    • 13.1.5. Key Product/Services Offered
  • 13.2. Oracle Corporation
  • 13.3. IBM Corporation
  • 13.4. Amazon Web Services Inc.
  • 13.5. Microsoft Corporation
  • 13.6. TigerGraph Inc.
  • 13.7. Ontotext AD
  • 13.8. DataStax Inc.
  • 13.9. Franz Inc.
  • 13.10. ArangoDB GmbH

14. Strategic Recommendations

15. About Us & Disclaimer