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市場調查報告書

商品編碼

1620251

全球圖數據庫市場規模：按公司規模、最終用途、應用、地區、範圍和預測

Global Graph Database Market Size By Enterprise Size, By End-Use Sector, By Application, By Geographic Scope And Forecast

出版日期: 2024年09月25日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

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簡介目錄

圖資料庫市場規模及預測

2023年圖資料庫市場規模為17.4213億美元，2024-2030年預測期間複合年增長率為20.86%，到2030年將達到54.2847億美元。

圖資料庫的全球市場推動因素

圖資料庫市場的成長和發展歸因於某些關鍵的市場推動因素。這些因素對圖資料庫在各領域的需求和採用有重大影響。

連線資料的成長：

圖形資料庫非常適合表示和查詢具有更複雜和互連資料集的企業中的關係。隨著互聯資料在多個產業中變得越來越重要，圖資料庫的需求越來越大。

知識圖譜的出現：

知識圖譜以圖結構排列訊息，在人工智慧、機器學習和數據分析等領域越來越受歡迎。知識圖譜只能透過圖資料庫建立和查詢，這也是它們如此受歡迎的原因。

分析與機器學習的進展：

圖形資料庫可有效處理資料中的關係和模式，從而支援與進階分析和機器學習相關的應用程式。隨著公司希望從數據中提取更多見解，圖資料庫與分析和機器學習相結合的需求越來越大。

即時資料處理：

圖資料庫可以即時處理數據，使其適合需要快速答案和洞察的應用程式。它在詐欺偵測、推薦系統和網路分析等情況下特別有用。

對安全性和詐欺偵測的需求不斷增加：

圖形資料庫對於詐欺安全性和偵測應用程式非常有用，因為它們可以識別連結資料中的模式和異常情況。由於網路安全威脅不斷發展，安全解決方案對圖資料庫的需求不斷增長。

網路與 IT 營運管理：

透過建模和評估不同元件之間的依賴關係，圖形資料庫對於網路和 IT 營運管理至關重要。這對於確保 IT 系統的可靠性、優化效能和識別瓶頸是必要的。

社群媒體與推薦系統的普及：

社群媒體平台和推薦系統的關鍵組成部分是識別和利用人、內容和項目之間的聯繫的能力。圖資料庫在社交媒體和電子商務行業中變得越來越流行，因為它們非常適合這些類型的應用程式。

健康與生命科學領域的應用：

圖形資料庫在健康科學中非常有用，可用於管理和分析患者數據以及對複雜的生物相互作用進行建模。它描述複雜關係的能力正在推動它在這些重要領域的採用。

全球圖資料庫市場的阻礙因素

雖然圖資料庫市場有很大的成長空間，但有一些行業限制使其變得困難。行業利益相關者必須瞭解這些課題。重要的市場限制包括：

複雜性與學習曲線：

實施和維護圖形資料庫有一個學習曲線，特別是從傳統關係型資料庫切換時。有些公司可能會對這種複雜性感到猶豫。

可擴充性問題：

圖資料庫可以很好地處理高度互連的數據，但隨著資料集變得越來越大，可能會出現可擴展性問題。一個持續關注的問題是確保有效擴展以適應不斷增長的數據量。

資料整合問題：

將圖形資料庫與您目前的系統和資料來源整合可能很困難。當您嘗試將圖形資料庫與組織中的其他資料庫類型或舊系統連接時，可能會出現相容性問題。

有限的標準化：

圖資料庫的市場沒有很好的標準化，促使各個系統之間的查詢語言和資料建模技術存在差異。缺乏標準化會降低資料的可移植性和互通性。

某些查詢的效能問題：

圖資料庫適用於某些類型的查詢，但在處理較大的資料集或更複雜的查詢時可能會促使效能問題。最佳化問題可能會促使查詢運行緩慢。

實施與維護成本：

部署圖形資料庫可能會在硬體基礎架構、軟體授權和培訓方面產生高昂的初始成本，尤其是在大型組織中。此外，還必須考慮持續的維護成本。

安全與隱私課題：

確保圖資料庫中的資料安全和隱私非常重要。然而，實施強而有力的安全措施很困難，企業必須應對未經授權的存取和資料外洩等問題。

市場知識與教育：

許多企業可能不知道圖形資料庫必須提供的所有功能和優勢。缺乏關於圖數據庫好處的知識和指導是一個潛在的障礙，特別是對於剛接觸該技術的公司。

Graph Database Market size was valued at USD 1742.13 Million in 2023 and is projected to reach USD 5428.47 Million by 2030, growing at a CAGR of 20.86% during the forecast period 2024-2030. To Learn More: Global Graph Database Market Drivers The growth and development of the Graph Database Market is attributed to certain main market drivers. These factors have a big impact on how Graph Database are demanded and adopted in different sectors. Several of the major market forces are as follows:

Growth of Connected Data:

Graph databases are excellent at expressing and querying relationships as businesses work with datasets that are more complex and interconnected. Graph databases are becoming more and more in demand as connected data gains significance across multiple industries.

Knowledge Graph Emergence:

In fields like artificial intelligence, machine learning, and data analytics, knowledge graphs-which arrange information in a graph structure-are becoming more and more popular. Knowledge graphs can only be created and queried via graph databases, which is what is causing their widespread use.

Analytics and Machine Learning Advancements:

Graph databases handle relationships and patterns in data effectively, enabling applications related to advanced analytics and machine learning. Graph databases are becoming more and more in demand when combined with analytics and machine learning as businesses want to extract more insights from their data.

Real-Time Data Processing:

Graph databases can process data in real-time, which makes them appropriate for applications that need quick answers and insights. In situations like fraud detection, recommendation systems, and network analysis, this is especially helpful.

Increasing Need for Security and Fraud Detection:

Graph databases are useful for fraud security and detection applications because they can identify patterns and abnormalities in linked data. The growing need for graph databases in security solutions is a result of the ongoing evolution of cybersecurity threats.

Network and IT Operations Management:

By modeling and evaluating dependencies between different components, graph databases are essential to network and IT operations management. This is necessary to guarantee the dependability of IT systems, optimize performance, and locate bottlenecks.

Greater Uptake of Social Media and Recommendation Systems:

A major component of social media platforms and recommendation systems is their ability to recognize and make use of the connections among people, content, and items. Graph databases are becoming more and more popular in the social media and e-commerce industries since they are ideal for these kinds of applications.

Applications in the Health and Life Sciences:

Graph databases are useful for managing and analyzing patient data in the health sciences as well as for modeling intricate biological interactions. Their adoption is being driven in these important sectors by their ability to depict complex relationships.

Global Graph Database Market Restraints

The Graph Database Market has a lot of room to grow, but there are several industry limitations that could make it harder for it to do so. It's imperative that industry stakeholders comprehend these difficulties. Among the significant market limitations are:

Complexity and Learning Curve:

Organizations may encounter a learning curve when implementing and maintaining graph databases, particularly if they are switching from conventional relational databases. Some firms may be put off by this complexity.

Scalability Issues:

Graph databases work well with highly interconnected data, however as datasets get larger, scalability issues could appear. One constant concern is ensuring effective scaling to handle growing data volumes.

Problems with Data Integration:

There may be difficulties integrating graph databases with current systems and data sources. When attempting to connect graph databases with other database types or older systems inside an organization, compatibility problems may occur.

Limited Standardization:

The market for graph databases is not well standardized, which causes differences in query languages and data modeling techniques amongst various systems. Data portability and interoperability may suffer from this lack of standards.

Performance Issues with Some Queries:

Graph databases work well with some kinds of queries, but when working with larger datasets or more complicated queries, performance issues may arise. Issues with optimization could slow down the execution of a query.

Cost of Implementation and Maintenance:

Graph database implementation, particularly in large organizations, may include high upfront expenses for hardware infrastructure, software licenses, and training. Costs for ongoing maintenance may also be taken into account.

Security and Privacy Challenges:

It's critical to guarantee the security and privacy of data in graph databases. But putting strong security measures in place may be difficult, and businesses need to deal with issues like illegal access and data breaches.

Market Knowledge and Education:

It's possible that many firms are unaware of all the features and advantages that graph databases offer. One potential barrier is a lack of knowledge and instruction regarding the benefits of graph databases, particularly for companies that are not yet familiar with this technology.