Graph analytics solutions are analytic methods for determining the intensity and direction of connections between objects in a graph. Graph analytics, also known as network analysis, is becoming more common for analytics workloads. For social network influencer analysis, graph analysis is becoming more common. This programme is being used by marketing managers to identify potential targets for marketing campaigns by attempting to build chain reactions within social network audiences to purchase goods and services. It's also used to capture money laundering and other financial crimes. It is also used to detect fraud, such as fraudulent financial transactions and applications, government benefits fraud, insurance applications and claims fraud, and other fraudulent telecommunications activities. These techniques are used to deter violence and conduct counter-terrorism operations.
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Key Driving Factors: The market is rising due to a strong demand for low latency queries:
New efficient methods for processing large-scale heterogeneous data in real-time are in high demand. As a result, a slew of graph databases focused on low-latency graph queries have sprung up. These low-latency queries process a large number of data messages in a short period of time. Graph databases are typically used to handle graph-only low-latency query workloads. Many new technologies demand scalable, transactional data storage as well as interactive, low-latency graph queries. Graph analytic applications are also in high demand due to the increasingly increasing base of social networks and other graph datasets.
This report offers the major market player’s profiles, such as Oracle, Microsoft, AWS, Neo4j, IBM TigerGraph, DataStax, Franz, TIBCO Software, Cray, Lynx Analytics, Linkurious, Teradata, Graphistry, Dataiku, Tom Sawyer Software, Objectivity, Kineviz, and Expero.
Opportunities: In the near future, cloud deployment will expand at a rapid rate:
The majority of graph analytics vendors provide cloud-based maintenance solutions to maximise revenues and efficiently automate the equipment maintenance process. The use of cloud-based graph analytics solutions is expected to increase as a result of advantages such as simple data maintenance, cost-effectiveness, scalability, and efficient management.
Customer analytics, risk and enforcement management, recommendation engines, path optimization, fraud detection, and other applications make up the graph analytics industry (operations management and asset management). Due to the growing need for finding the fastest and safest route in verticals such as supply chain and logistics, transportation, and retail and eCommerce, the route optimization segment is expected to be the fastest-growing segment in the industry.
The Graph Analytics market report has been categorized as below
By Organizational Size
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Published Date : March-2021