As per report "Graph Database Market with COVID-19 Impact Analysis, By Type (RDF and LPG), Application (Fraud Detection and Prevention, and Recommendation Engine), Component (Software and Services), Deployment Mode, Vertical, and Region - Global Forecast to 2026", market size to grow from USD 1.9 billion in 2021 to USD 5.1 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 22.5% during the forecast period.

Various factors such as need to incorporate real-time big data mining with visualization of results, increasing adoption for AI-based graph database tools and services to drive market, and growing demand for solutions that can process low-latency queries are expected to drive the adoption of graph database solutions and services.
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COVID-19’s global impact has shown that interconnectedness plays an important role in international cooperation. As a result, several governments started rushing toward identifying, evaluating, and procuring reliable solutions powered by AI. Graph databases and AI are invaluable to organizations managing uncertainty in real-time, but most predictive models rely on historical patterns. The use of graph database and AI has accelerated in the COVID-19 pandemic period. This has helped organizations engage customers through digital channels, manage fragile and complex supply chains, and support workers through disruption to their work and lives. New practices, such as work from home and social distancing, have led to the requirement of graph database solutions and services and the development of digital infrastructures for large-scale technology deployments. The COVID-19 pandemic in 2020 brought accelerating changes in consumer preferences and behaviors and putting pressure on brands to keep pace and provide a personalized customer experience. Enterprises have witnessed a reduction in their operational spending and are now focusing more on business continuity and sustainability.

Technology and service providers have been facing significant disruption to their businesses from COVID-19. Hence, the COVID-19 pandemic has disrupted the global financial markets and has created panic, uncertainty, and distraction in the operations of global corporations.

Based on components, the graph database market is segmented into solutions and services. The services segment has been further divided into professional and managed services. These services play a vital role in the functioning of graph database solutions, as well as ensure faster and smoother implementation that maximizes the value of the enterprise investments. The growing adoption of graph database solutions is expected to boost the adoption of professional and managed services. Professional service providers have deep knowledge related to the products and enable customers to focus on the core business, while MSPs help customers improve business operations and cut expenses.

To protect customer data, mitigate exposure to risk, and deliver the most value to shareholders, organizations are increasingly using graph databases as a fraud detection mechanism. The graph database is one of the top technologies used to detect fraud, uncover fraud rings, and identify sophisticated scams, including eCommerce fraud, corruption, and money laundering. In money laundering and financial fraud, graph database uses pattern recognition, classification, statistical analysis, and ML models to identify fraud from massive amounts of data.

The graph database market is bifurcated on the basis of RDF and LPG. RDF database is expected to hold a higher CAGR due to its capability of focusing on the information that is organized in a subject-predicate-object relationship. The healthcare and pharmaceutical companies and government statistics agencies are increasingly adopting the RDF graph databases. RDF graph database providers are offering support services for multiple graph traversal and declarative languages, such as Gremlin and Cypher.

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The graph database market has been segmented by organization size into large enterprises and SMEs. The market for SMEs is expected to register a higher CAGR during the forecast period as cloud-based solutions and services help them improve business performance and enhance productivity. Whereas the large enterprises segment is expected to hold a larger market share in the graph database market during the forecast period due to the affordability and the acceptance of emerging technologies.

The graph database market by vertical has been categorized into banking, financial services, and insurance, Retail and eCommerce, manufacturing and automotive, media and entertainment, IT and telecom, healthcare, pharmaceuticals and life sciences, energy and utilities, government and public, travel and hospitality, transportation and logistics and others (education and research, and real estate). The healthcare, pharmaceuticals and life sciences vertical is expected to witness the highest growth rate, while the BFSI vertical is expected to have the largest market size during the forecast period. The larger market size of the BFSI vertical can be attributed to the increasing regulatory scrutiny, improved customer satisfaction, and benefits, such as risk management and tailored solutions.

APAC 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. All these factors are responsible for the expeditious growth of the graph database market in the region. Companies operating in APAC continue to focus on improving customer services to drive market competitiveness and revenue growth. China, Japan, and South Korea have displayed ample growth opportunities 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), MarkLogic Corporation (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).

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