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Important points of ZDNET
- The AI-driven graph database market grows at a rate of around 25% per year.
- The graph database supports knowledge graphs and provides visual guidance for AI development.
- There are several dedicated graph database vendors on the market.
Over the past decade, there have been endless churn in the technology that forms the database behind the applications that run. The rise of NOSQL databases, document databases, and databases built on the web for the web gave us a greater choice.
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Recently, both the rise of back-end systems and the rise of generation technology has seen a boom in the use of artificial intelligence (AI), creating an insatiable demand for databases that can handle and process ultra-high chemical workloads. This demand has led to a surge in graph databases and knowledge graphs, visual databases that help users manage AI requirements.
The graph database has been growing for several years and is now comprised of the fastest growing categories within the $137 billion annual database market. Again, the AI-Graph database is considered the most optimal data backend for AI systems. These expenditures on technologies have a five-year annual growth rate of over 26%. estimate It was published by Tech Analyst Gartner at the end of 2024. The overall market for the entire database management system increases by 16% per year. Business research company in 2025 projection Combined annual growth rate of 24%.
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AI requires both structured and unstructured data gobs that are not only fed to applications, but also woven into connected patterns that bring in inference. “The push towards semantic understanding and reasoning for AI systems is the struggle to support a flat relational database,” said Tony Tong, co-founder and CTO of Intellectia AI.
Although graph databases are isolated and often confused, “graph databases are tools, engines for identifying connections within a particular dataset. Knowledge graphs are representations of the data itself, which are representations of the data itself.
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“Knowledge graphs provide AI systems with how real information is related to how that information is related. This helps AI answer questions with more accuracy and nuance. Graph databases allow data to be searched more efficiently and provide context that is not just raw data.”
Shalvi Singh, founder of HealthEngine.us, can enable graphing environments to be applied to features such as real-time analytics, fraud analytics, retail, logistics and more.
Rankings for the most popular graph databases include the following technologies (source: db-engines):
- Neo4J Graph
- Microsoft Azure Cosmos DB
- Aerospace
- arangodb
- OrientDB
- graphdb
- Master
- Amazon Neptune
- Memgraph graph
- nebulagraph
Of course, implementation of graph databases is not an overnight project. For example, “incorporating data from various sources is subject to inconsistent or outdated information,” warned Singh.
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Scalability is also an issue as the performance of these data environments can deteriorate as datasets increase in size and complexity. “These technologies are not going to replace traditional databases,” she added. More hybrid deployments may be required for scalability purposes.
Furthermore, graph databases and knowledge graphs “often require special expertise, detailed planning and careful structuring of interconnected data,” Bukowski said. “Although knowledge graphs have been in use for decades, graph databases are a newer, growing segment of the database market, meaning that both of these tools are difficult to acquire, implement, and master.”
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Without data, there is no AI. For those who want to provide greater data support for their AI efforts, graph databases and adjacent knowledge graphs represent visual connections that guarantee more target AI efforts.
