Massive Scope of Graph Analytics Market | Global Key Vendors - Microsoft, IBM, AWS, Oracle, Neo4j, TigerGraph, Cray, DataStax

Graph Analytics Market Report covers industry chain analysis, latest market trends & dynamics along with cost profit analysis of major key players which focuses on expansion rate, prices, competition, size, prices and value chain analysis of those leaders in the market. The Graph Analytics Market report covers the market landscape and its development prospects over the coming years.

Global Graph Analytics Market Synopsis:

The past four years, Graph Analytics market size to maintain the average annual growth rate from 2014 to 2019, Market analysts believe that in the next few years, Graph Analytics market size will be further expanded, we expect that by 2024, The market size of the Graph Analytics will reach at Higher rank.

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Graph analytics is a rising and fascinating new area for analytics workloads and it is also called as network analysis. Its capacity to be used for the purpose of social network influencer analysis has make growth opportunities for the graph analytics market. Marketing managers are particularly interested in the identification of social network influencers as they are potential targets for social media marketing campaigns. Connecting with influencers could set off chain reactions amongst the social network communities and trigger sale of services and products.

Important drivers energizing the market growth are growing demand to analyze low-latency queries, advancements in graph analytics by integration of technologies, such as Artificial Intelligence (AI), Internet of Things (IoT), and the ability of graph analytics to uncover relationships between data in real time.

The detailed segments and sub-segment of the Graph Analytics Market are given below:

1) Product Type Segmentation:

Solutions
Software Tools
Platform
Services
Consulting
System Integration
Support and Maintenance
2) Industry Segmentation:

Customer Analytics
Risk and Compliance Management
Recommendation Engines
Route Optimization
Fraud Detection/Others (Operations Management and Asset Management)
3) Region Segmentation:

On the basis of Regional analysis, North America and Europe holds major share in global Graph Analytics market. Moreover, Asia-Pacific is expected to record higher growth rate in Graph Analytics Market during the forecast year.

North America Country (United States, Canada)
South America
Asia Country (China, Japan, India, Korea)
Europe Country (Germany, UK, France, Italy)
Other Country (Middle East, Africa, GCC)
Top Leading player in Global Graph Analytics Market:

Microsoft, IBM, AWS, Oracle, Neo4j, TigerGraph, Cray, DataStax, Lynx Analytics, NVIDIA Developer

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Industry news:

Neo4j (Feb 04, 2020)

The Future of Graph Databases Is Here: Introducing Neo4j 4.0

Graph Market Leader Delivers the Definitive Foundation for Connected Data, Built for Unlimited Scale and Developer Flexibility

The 4.0 release of the Neo4j graph database addresses the broad and complex challenges of application development in the decade to come, including unlimited scalability, intelligent data context and robust enterprise-grade security. Enterprise organizations are already using Neo4j 4.0 to build intelligent applications that leverage the increasingly dynamic, interconnected nature of data.

Emil Eifrem, CEO and Co-Founder of Neo4j, explained how businesses innovate using Neo4j 4.0 without having to negotiate trade-offs between performance, flexibility and maintenance.

"I believe Neo4j 4.0 will set the pace for all graph database technology in the next decade and beyond," Eifrem said. "With Neo4j 4.0, we made a massive, audacious engineering investment to raise the bar on the scale, performance and security that can be expected from a graph database - and from databases in general. Our customers challenge us with new use cases for graph technology which require unlimited scale, as well as development and deployment flexibility, all while maintaining security and privacy. These are hard problems to address, but Neo4j 4.0 is already delivering on this promise."

Stephen O'Grady, Principal Analyst at RedMonk, shared his observations on modern application development.

"The modern application development process puts a premium on velocity, which is why ease of use and flexibility for developers have become as critical as performance for database platforms," O'Grady said. "One of the ways this is achieved is by avoiding the need for cumbersome data abstractions to translate between business needs and relational schemas. Graph databases are a canonical example of this, and Neo4j remains one of the pioneers of the category committed to bringing the benefits of graphs to a wide variety of customer types and use cases."

Emerging Requirements for Intelligent Applications

Connected data has emerged as a key consideration for modern application development - whether organizations require well-tuned heuristics, advanced analytics or machine learning and artificial intelligence (AI). Neo4j 4.0 addresses the broad and complex concerns of today's intelligent applications, including:

Unlimited scalability: Data grows relentlessly and performant applications can't be constrained by data volumes. Applications need to scale up - and out - to handle higher volumes while maintaining performance across a growing diversity of on-premises, hybrid and cloud architectures.

Dynamic, revealed context: Applications increasingly need databases that adapt to the myriad complexities, dynamics and unpredictability of real-world data. Effective applications use the richness of data relationships to reveal context and causality in real time.

Security and data privacy: As regulations continue to evolve, individuals and governments are more conscious than ever about how and where consumer and citizen data is used. Application developers will need to build applications that meet these needs securely and rapidly.

Intelligence and learning: Operational applications are increasingly likely to be components of complex systems that incorporate machine learning and AI. For actionable AI, tomorrow's applications need to bridge data science across operational systems and leverage context in real time.

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Significant points in table of contents of Global Graph Analytics Market Report 2020:

1 Graph Analytics Product Definition

2 Global Graph Analytics Market Manufacturer Share and Market Overview

3 Manufacturers Graph Analytics Business Introduction

4 Global Graph Analytics Market Segmentation (Region Level)

5 Global Graph Analytics Market Segmentation (Product Type Level)

6 Global Graph Analytics Market Segmentation (Industry Level)

7 Global Graph Analytics Market Segmentation (Channel Level)

8 Graph Analytics Market Forecasts 2020-2024

9 Graph Analytics Segmentation Product Type

10 Graph Analytics Segmentation Industry

11 Graph Analytics Cost of Production Analysis

12 Conclusions

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