The global graph database market is expected to expand from USD 2.9 billion in 2023 to USD 7.3 billion by 2028, representing a Compound Annual Growth Rate (CAGR) of 20.2% during the forecast period. Graph databases play a crucial role in managing and ensuring compliance with data governance regulations.
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The services segment to hold higher CAGR during the forecast period
Based on the offering, the graph database market is segmented into solutions and services. The services segment has been further divided into professional and managed services. Graph database services encompass a range of offerings designed to support the implementation, management, and optimization of graph databases for various applications. The growing adoption of graph database solutions is expected to boost the adoption of professional and managed services.
Community Analysis segment to hold the largest market size during the forecast period
Community analysis refers to the process of identifying and characterizing groups or clusters of nodes within a graph that exhibit a higher degree of interconnectedness among themselves compared to the rest of the graph. The need for deeper insights into complex relationships and structures within data networks drives the adoption of community analysis techniques.
The graph extension segment to hold a larger market size during the forecast period
The graph extension segment is expected to hold highest market share during the forecast period. Graph extensions find applications in various fields such as social networks, recommendation systems, knowledge graphs, and more, enabling richer and more nuanced representations of data. The need for advanced functionality beyond basic graph storage and querying drives the adoption of graph extensions.
BFSI segment to have the largest market size during the forecast period
In the BFSI sector, graph databases are gaining traction as vital tools for fraud detection, risk assessment, and customer relationship management. The ability to analyze intricate financial connections and patterns allows for improved fraud prevention, while modeling customer interactions enhances personalized services. Graph databases empower the BFSI industry to navigate complex relationships and provide real-time insights, contributing to more informed decision-making and streamlined operations.The need to leverage interconnected data to improve fraud detection, risk management, customer relationships, compliance, and overall operational efficiency will drive the BFSI segment.
Asia Pacific to hold higher CAGR during the forecast period
Asia Pacific is expected to grow at a good pace during the forecast period. Opportunities for smaller graph database vendors to introduce graph database solutions for numerous sectors have also increased. increasing adoption of graph databases across various industries in the Asia Pacific region. Organizations recognize the value of graph databases in addressing complex data challenges, uncovering insights, and driving innovation in an ever-evolving technological landscape.
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Unique Features in the Graph Database Market
Graph databases excel in managing and analyzing complex relationships between data points. Their graph-based structure, consisting of nodes, edges, and properties, makes them particularly effective for mapping interconnections, which traditional relational databases struggle to handle efficiently.
Graph databases are optimized for real-time data querying and processing. This is essential for applications that require instant access to complex relationships, such as fraud detection, recommendation engines, and social network analysis.
Unlike traditional databases that require a predefined schema, graph databases are highly flexible in terms of data modeling. This allows businesses to easily adapt to evolving data structures without the need for complex schema redesigns.
Graph databases offer superior performance when working with large and complex datasets, especially in scenarios where relationships between entities are key. Their ability to efficiently traverse intricate connections results in faster query responses compared to relational databases.
Graph databases provide native support for graph algorithms such as shortest path, centrality, and clustering, enabling advanced analytics. These algorithms are crucial for use cases like network optimization, supply chain management, and social graph analysis.
Major Highlights of the Graph Database Market
As businesses face growing volumes of interconnected data, the demand for real-time analytics is driving the adoption of graph databases. These databases are ideal for applications that require fast querying and instant access to data relationships, such as recommendation systems, fraud detection, and social network analysis.
Graph databases are being increasingly adopted across a wide range of sectors, including finance, healthcare, retail, telecommunications, and technology. Their ability to handle complex data relationships makes them essential for use cases like supply chain management, customer 360 views, and network optimization.
With data privacy and governance regulations tightening globally, graph databases are becoming critical for organizations looking to ensure compliance. Their ability to track and model complex relationships helps companies manage data flows, monitor access, and ensure regulatory compliance effectively.
Graph databases are increasingly being integrated with emerging technologies such as machine learning (ML), artificial intelligence (AI), and blockchain. These integrations enhance the capabilities of graph databases, providing advanced analytics and enabling more intelligent data processing.
The growing interest in graph algorithms for data analytics, such as clustering, centrality, and shortest path analysis, is one of the key drivers of the graph database market. These algorithms support advanced data insights, which are crucial for applications in areas like fraud detection, social network analysis, and predictive maintenance.
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Top Companies in the Graph Database Market
The major vendors in the graph database market include Oracle Corporation (US), IBM Corporation (US), Amazon Web Services, Inc. (US), DataStax (US), Ontotext (Bulgaria), Stardog Union (US), Hewlett Packard Enterprise (US), ArangoDB (US), Blazegraph (US), Microsoft Corporation (US), SAP SE (Germany), Teradata Corporation (US), Openlink Software (US), TIBCO Software, Inc. (US), Neo4j, Inc. (US), GraphBase (Australia), Cambridge Semantics (US), TigerGraph, Inc. (US), Objectivity Inc. (US), Bitnine Co, Ltd. (US), Franz Inc. (US), Redis Labs (US), Graph Story (US), Dgraph Labs (US), Eccenca (Germany), and Fluree (US).
As per AWS, Graph databases are designed specifically to record and navigate relationships. Relationships are first-class citizens in graph databases, and they account for the majority of the value of graph databases. Graph databases hold data entities in nodes and relationships between entities in edges. An edge always has a start node, an end node, a type, and a direction, and it can indicate parent-child connections, actions, ownership, and other such things. There is no restriction on the number or type of relationships that a node can have.
Microsoft creates software, services, devices, and solutions to compete in the intelligent cloud and intelligent edge. Microsoft’s increased expenditures in mixed-reality cloud help its clients to digitalize their business processes. Cloud-based solutions that provide customers with software, platforms, and content are among the company’s products. Operating systems (OS), cross-device productivity apps, server applications, business solution applications, desktop and server management tools, software development tools, and video games are among its product offerings. Microsoft’s platforms and solutions help boost small business productivity, large corporate competitiveness, and government efficiency. To improve its service offerings, it focuses on investing in data centres and other hybrid and edge infrastructure. Microsoft is divided into three business units: productivity and business processes, intelligent cloud computing, and more personal computing. Dynamics business solutions for productivity and business operations include Dynamics 365, a suite of cloud-based ERP and CRM software, Dynamics ERP on-premises, and Dynamics CRM on-premises.
Oracle was founded in 1977 and is headquartered in California, United States. The company is a global leader in providing a wide range of products, solutions, and services geared to fulfil the needs of corporate IT environments, including platforms, applications, and infrastructure. Customers of Oracle include small and large organisations, government agencies, educational institutions, and resellers. The corporation offers its products and services both directly and indirectly through a global sales force and the Oracle Partner Network. It is a company that develops, manufactures, and sells hardware systems, databases, middleware software, and application software. It offers SaaS solutions that include upcoming technologies such as IoT, AI, ML, and blockchain. It works in more than 175 countries through three business segments: cloud and licence, hardware, and services. Graph databases that are part of Oracle’s convergent database offering eliminate the requirement to set up and move data to a separate database.
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