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Neo4j
Overview
HQ Location
United States
Year Founded
2007
Company Type
Private
Revenue
$10-100m
Employees
201 - 1,000
Website
Twitter Handle
Company Description
Neo4j is the graph company behind the #1 platform for connected data. The Neo4j graph platform helps organizations make sense of their data by revealing how people, processes and digital systems are interrelated. This connections-first approach powers intelligent applications tackling challenges such as Artificial Intelligence, fraud detection, real-time recommendations and master data. The company boasts the world's largest dedicated investment in native graph technology, has amassed more than ten million downloads, and has a huge developer community deploying graph applications around the globe. More than 250 commercial customers, including global enterprises like Walmart, Comcast, Cisco, eBay and UBS use Neo4j to create a competitive advantage from connections in their data. Neo4j is privately held and funded by Eight Roads Ventures (an investment arm of Fidelity International Limited), Sunstone Capital, Conor Venture Partners, Creandum, Dawn Capital and Greenbridge Investment Partners. Headquartered in San Mateo, Calif., Neo4j has regional offices in Sweden, Germany and the UK. For more information, please visit Neo4j.com and @Neo4j. All trademarks and registered trademarks, including Neo4j? are the property of their respective owners.
IoT Solutions
What is Neo4j
Neo4j is a highly scalable native graph database, purpose-built to leverage not only data but also data relationships.
Using Neo4j, developers build intelligent applications that traverse today's large, interconnected datasets in real time. Powered by a native graph storage and processing engine, Neo4j delivers an intuitive, flexible and secure database for unique, actionable insights.
Neo4j is a highly scalable native graph database, purpose-built to leverage not only data but also data relationships.
Using Neo4j, developers build intelligent applications that traverse today's large, interconnected datasets in real time. Powered by a native graph storage and processing engine, Neo4j delivers an intuitive, flexible and secure database for unique, actionable insights.
Key Customers
Vanguard, Airbnb, Microsoft, Hp, Cisco, IBM, Lockhead Martin, Bayer, Vanguard
IoT Snapshot
Neo4j is a provider of Industrial IoT infrastructure as a service (iaas), platform as a service (paas), application infrastructure and middleware, analytics and modeling, functional applications, and cybersecurity and privacy technologies, and also active in the finance and insurance, and healthcare and hospitals industries.
Technologies
Use Cases
Functional Areas
Industries
Services
Technology Stack
Neo4j’s Technology Stack maps Neo4j’s participation in the infrastructure as a service (iaas), platform as a service (paas), application infrastructure and middleware, analytics and modeling, functional applications, and cybersecurity and privacy IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
Case Studies.
Case Study
UBS Enhances Risk Management and Compliance with Neo4j Data Lineage Tool
UBS, a multinational investment bank and financial services company, was faced with the challenge of complying with regulations put in place to strengthen systems for risk data aggregation and internal risk reporting following the 2007 global financial crisis. Specifically, UBS needed to comply with the Basel Committee on Banking Supervision issued standard 239 (BCBS 239). This regulation required banks to provide transparency into the data flows that feed their risk reporting, necessitating broad data governance and detailed data lineage. Data lineage, which involves tracking the entire lifecycle of information, is a crucial component of risk management. UBS initially built an application called Group Data Dictionary (GDD) as its data lineage and data governance tool on Oracle. However, they soon discovered limitations with an RDBMS approach, which relies on JOINS to connect data across tables. UBS realized it needed a better solution for creating real-time data lineage visualizations and exporting lineage information for ad-hoc analysis via Excel.
Case Study
Novartis Leverages IoT for Enhanced Drug Discovery
Novartis, a global healthcare company, was faced with the challenge of managing and making sense of a vast amount of data. The company had decades of data on how various compounds affect protein targets, with about a billion data points in total. This historical data was critical but sparse compared to the granular data currently being collected. Novartis uses an automated process that captures high-content image data showing how a particular compound has affected an entire cell culture, generating terabytes of phenotypic data. The challenge was to combine this historical data with the burgeoning phenotypic data and place it within the larger context of ongoing medical research from around the world. The team also wanted to combine its data with medical information from NIH’s PubMed, which contains about 25 million abstracts from some 5,600 scientific journals.
Case Study
Leveraging Graph Technology for Enhanced Cybersecurity: A Case Study on MITRE's CyGraph
MITRE, a federally-funded, not-for-profit company that manages seven national research and development laboratories in the United States, was grappling with the challenge of managing an influx of cybersecurity data. The constant changes in network environments were impacting the security posture of U.S. government agencies. Intrusion alerts, anti-virus warnings, and seemingly benign events like logins, service connections, and file share access were all potentially associated with adversary activity. The cybersecurity researchers at MITRE needed to go beyond rudimentary assessments of security posture and attack response. This required merging isolated data into higher-level knowledge of network-wide attack vulnerabilities and mission readiness. The challenge was not the lack of information, but the ability to assemble disparate pieces of information into an overall analytic picture for situational awareness, optimal courses of action, and maintaining mission readiness. The team also struggled with fully comprehending a given security environment and mapping all known vulnerabilities.
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