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시장보고서
상품코드
1614974
그래프 데이터베이스 시장 규모, 점유율, 성장 분석 : 제공별, 모델 유형별, 분석 유형별, 최종 용도별, 지역별 - 산업 예측(2024-2031년)Graph Database Market Size, Share, Growth Analysis, By Offering, By Model Type, By Analysis Type, By End Use, By Region - Industry Forecast 2024-2031 |
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그래프 데이터베이스 세계 시장 규모는 2022년 31억 3,000만 달러로 평가되며, 2023년 38억 달러에서 2031년 152억 7,000만 달러로 성장할 것으로 예상되며, 예측 기간(2024-2031년) 동안 21.9%의 CAGR을 기록할 것으로 예상됩니다.
그래프 데이터베이스의 미래는 커넥티드 데이터 분석에 대한 수요 증가와 상호연결된 시스템에서 생성되는 데이터의 급증에 힘입어 번창할 것으로 보입니다. 머신러닝과 인공지능과 같은 첨단 기술은 그래프 데이터베이스 제공업체에게 새로운 비즈니스 기회를 제공할 준비가 되어 있습니다. 또한, 빅데이터 애플리케이션과 IoT 기기에 대한 의존도가 높아짐에 따라 시장 성장을 크게 촉진할 것으로 예상됩니다. 비즈니스 인텔리전스 및 데이터 기반 의사결정이 전 세계 조직에서 부상하고 있으며, 이는 그래프 데이터베이스의 채택을 더욱 촉진할 것입니다. 또한, 사이버 보안에 대한 관심이 높아지면서 수요가 증가할 것으로 예상됩니다. 그러나 표준화 부족, 쿼리 언어의 복잡성, 데이터 보안 문제, 레거시 시스템과의 통합 문제 등이 이 시기의 성장을 저해할 수 있습니다.
Global Graph Database Market size was valued at USD 3.13 billion in 2022 and is poised to grow from USD 3.8 billion in 2023 to USD 15.27 billion by 2031, growing at a CAGR of 21.9% in the forecast period (2024-2031).
The future of graph databases is set to thrive, driven by increasing demand for connected data analysis and the proliferation of data from interconnected systems. Advanced technologies like machine learning and artificial intelligence are poised to create new business opportunities for graph database providers. Additionally, the growing reliance on big data applications and IoT devices is expected to significantly enhance market growth. The rise of business intelligence and data-driven decision-making among organizations globally will further fuel the adoption of graph databases. Moreover, heightened focus on cybersecurity is likely to increase demand. However, challenges such as lack of standardization, the complexity of query languages, data security concerns, and integration issues with legacy systems may hinder growth during this period.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global Graph Database market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Global Graph Database Market Segmental Analysis
Global Graph Database Market is segmented by Offering, Model Type, Analysis Type, End Use and region. Based on Offering, the market is segmented into Solutions (Solution Type, Deployment mode), Services (Professional Services, Managed Services). Based on Model Type, the market is segmented into RDF, Labelled Propert Graph, Hypergraph. Based on Analysis Type, the market is segmented into Community Analysis, Connectivity Analysis, Centrality Analysis, Path Analysis. Based on End Use, the market is segmented into BFSI, Retail & eCommerce, Telecom & IT, Healthcare, Pharmaceuticals, & Life Sciences, Government & Public Sector, Manufacturing & Automotive, Media & Entertainment, Energy & Utilities, Travel & Hospitality, Transportation & Logistics, Others. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Global Graph Database Market
The Global Graph Database market is anticipated to flourish due to the rising demand for connected data analysis. Organizations are increasingly utilizing complex data derived from various interconnected systems, recognizing the necessity for enhanced data analysis capabilities. Graph databases are uniquely positioned to meet this demand, as they excel in visualizing and querying relationships among data points. This ability allows organizations to gain valuable real-time insights crucial for effective decision-making, solidifying the importance of graph databases in the realm of connected data analytics. Consequently, this growing need for sophisticated data analysis tools is a significant driver of market growth moving forward.
Restraints in the Global Graph Database Market
One significant restraint in the Global Graph Database market is the steep learning curve associated with specialized query languages like Cypher for Neo4j. Mastering these complex languages requires considerable time and effort, leading to a need for extensive training for database administrators. This challenge results in a limited pool of skilled professionals proficient in these languages, subsequently hindering the widespread adoption of graph databases across various industries. As organizations may be reluctant to invest in training due to these complexities, the overall growth and integration of graph database technologies face notable constraints in the current market landscape.
Market Trends of the Global Graph Database Market
The Global Graph Database market is experiencing a significant trend towards real-time analytics, driven by the increasing demand for immediate data processing capabilities. The inherent capacity of graph databases to efficiently manage and analyze live data streams positions them as essential tools for various applications, including social media analysis, recommendation systems, and IoT implementations. As organizations seek to leverage real-time insights for enhanced decision-making and user engagement, the adoption of graph databases is poised to accelerate, creating extensive opportunities for growth and innovation within the market. This trend not only broadens the functional scope for graph database providers but also solidifies their relevance in a data-driven landscape.