Category: Amazon Neptune

This is a guest post by Ami Luttwak, CTO at Wiz, co-authored with Brad Bebee, General Manager of Amazon Neptune. Graphs are changing the way we parse and understand the world. Social graphs have had a huge impact on how we analyze social interactions across many industries. Now, in security, we can build totally new […]

This post is co-written with Bobber Cheng and Ronit Rudra from CSC Generation. CSC Generation is a company that focuses on acquiring overlooked stores and catalog-based retailers and transforming them into high-performance, digital-first brands. As we grew through multiple acquisitions, it became apparent that our legacy product information system (PIM), backed by relational databases, was […]

Amazon Neptune version 1.2.1.0 now supports the Apache TinkerPop 3.6.x release line, which offers a number of major new features and improvements to existing functionality. New features include fresh additions to the Gremlin language itself, like the P.regex predicate for filters and the mergeV() and mergeE() steps, which should help simplify complex upsert-like functionality. In this […]

Semantic reasoning is a powerful form of symbolic AI that brings meaning to data. At a high level, this is achieved by inferring new facts from existing information (or base facts) using a data model and knowledge of the domain. It can be useful for performing calculations, ensuring consistency, and detecting intricate patterns. Semantic reasoning […]

In this post we focus on data analysis as part of the modern data strategy. I cover how to generate insights from healthcare FHIR (Fast Healthcare Interoperability Resources) data with Amazon Neptune, a fast, reliable, fully managed graph database service. Using a graph database for this use case allows you to model and navigate complex […]

Each year online businesses lose tens of billions of dollars due to fraud, which can take many forms. For example, fraudsters can obtain stolen credit card details and use them for unauthorized transactions. Therefore, detecting fraud and malicious behavior at the time of a transaction, such as when a user registers a new payment method, […]

In this post, we present a design for a common technical requirement: ingest data from multiple sources to a target Resource Description Framework (RDF) graph database. Our target is Amazon Neptune, a managed graph database service. RDF is one of two graph models supported by Neptune. The other is Labeled Property Graph (LPG). Each graph […]

This is a guest post co-authored by Amr Elnaggar, Saurabh Deshpande, Mohammad Azzam, Matias Pons and Wilson Tang from Delivery Hero. Delivery Hero is available in 74 countries around the World. It operates a wide range of local brands that are united behind the shared mission Always Delivering an Amazing Experience — fast, easy, and […]

For manufacturers it’s important to track transformations and transfers of products as they travel through the supply chain. In the event of quality issues, the ability to quickly and accurately identify a defective product and gather data for root cause analysis and containment is critical. NXP Semiconductors has been working to improve its product traceability […]

Amazon Neptune, a fully managed graph database, is purpose built to work with highly connected data such as relationships between customers and products, or between pieces of equipment within a complex industrial plant. Neptune is designed to support highly concurrent online transaction processing (OLTP) over graph data models. Neptune supports both property graphs, which you […]