Northbrook, IL 60062 -- (SBWIRE) -- 09/07/2023 -- The global Graph Database Market size to grow from USD 2.9 billion in 2023 to USD 7.3 billion by 2028, at a Compound Annual Growth Rate (CAGR) of 20.2% during the forecast period, according to research report by MarketsandMarkets™. Increasing adoption for graph database tools and services to drive market.

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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.

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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.

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).

Key Dynamic Factors For Graph Database Market:

Data complexity and relationships: As businesses deal with more intricate and related data, graph databases are becoming more and more popular. Graph databases are indispensable in fields like social networks, recommendation systems, and fraud detection because they are excellent at handling data with complex relationships.

Rise of Knowledge Graphs: Knowledge graphs are vital for many applications, such as semantic search, AI-driven knowledge bases, and data integration. They represent data as entities and their relationships. Knowledge graph construction and querying are logical applications for graph databases.

The use of graph databases is fueled by the growth of large data and the demand for sophisticated analytics. Businesses aim to get insights from connected data, and graph databases can be used to find undiscovered patterns and connections.

Applications tailored to certain industries: Each industry has its own requirements for managing data interactions, including social media, healthcare, banking, and logistics. The creation of solutions and applications tailored to particular industries frequently increases demand for graph databases.

The development of graph query languages (such as GraphQL, Cypher) and standards (such as RDF) can have a big impact on how widely used and interoperable graph databases become. These languages facilitate compatibility between various graph database systems and make working with graph data simpler.

Competition and the Vendor Ecosystem: There are an increasing number of companies offering graph database solutions. Market competition can result in innovation, pricing competition, and improved features that are advantageous to users.

Scalability and Performance: Graph databases must exhibit excellent performance and scalability in order to handle big datasets and sophisticated queries. To satisfy the demands of modern applications, performance and scalability improvements are essential.

Cloud Adoption: The market for graph databases has been significantly impacted by the switch to cloud-based infrastructure and services. It is simpler for businesses to implement and scale graph databases in the cloud because so many cloud providers offer managed graph database services.

Data Privacy and Security: Organisations want strong solutions to safeguard sensitive data within graph databases in light of growing data privacy and security issues. The selection of graph database systems may be influenced by compliance with laws like GDPR and CCPA.

Open Source and Community Development: The existence of free and open-source graph database programmes (such as Neo4j and JanusGraph) as well as vibrant developer communities can spur market growth and innovation.

Use cases and Success Stories: As more use cases and success stories from other industries come to light, it may inspire other businesses to look into graph database solutions for their own data problems.

Integration with AI and Machine Learning: Graph databases are increasingly being used in conjunction with AI and machine learning techniques. Predictive analytics, sophisticated recommendation systems, and fraud detection are all made possible by this combination.

Global Economic variables: Businesses' propensity to invest in novel database technology, such as graph databases, can be influenced by economic variables like economic expansion or contraction.

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Competitive and Segmentation Analysis:

A dynamic interplay between established competitors and up-and-coming rivals defines the competitive landscape of the graph database industry, which is fueled by the growing acceptance of graph databases as a basic technology for handling intricate, interrelated data. Among the leading companies in this area are Neo4j, Amazon Web Services (AWS), Microsoft Azure, IBM, OrientDB (now a member of the DataStax family), and Virtuoso. Scalability, performance, usability, and integration possibilities with other data technologies are just a few of the areas in which these businesses compete.

Market segmentation for graph databases is mostly determined by use cases and sectoral industries. Applications for knowledge graphs, fraud detection, and social networking platforms are just a few examples of how graph databases are used. Graph databases are used in the healthcare industry to handle patient data and find new drugs. Graph databases are used by financial organisations for risk management and anti-money laundering purposes. They are used in the retail and e-commerce industries for supply chain optimisation and personalised product suggestions.

Additionally, there are several deployment strategies available on the market, from on-premises solutions to cloud-based services. The flexibility, scalability, and affordability provided by cloud providers like AWS, Azure, and Google Cloud have significantly accelerated the use of cloud-based graph databases.

The rise of creative businesses and open-source initiatives adds to the competitiveness as the graph database market expands. Newcomers frequently bring novel viewpoints and specialised solutions, which might further splinter the market. Additionally, as businesses look to leverage the power of interconnected data to achieve a competitive edge in their individual industries, the combination of graph databases with cutting-edge technologies like AI and machine learning is primed to open up new avenues and fuel rivalry. Data complexity, industry-specific requirements, and the continued development of standards and query languages will all have an impact on how the market develops. These factors will also continue to define the competitive environment and segmentation dynamics in the graph database market.

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