AWS Database Blog
Category: Amazon Neptune
Create a 360-degree master data management patient view solution using Amazon Neptune and generative AI
In this post, we explore how you can achieve a patient 360-degree view using Amazon Neptune and generative AI, and use it to strengthen your organization’s research and breakthroughs. By consolidating information from multiple sources such as electronic health records (EHRs), lab reports, prescriptions, and medical histories into a single location, healthcare providers can gain a better understanding of a patient’s health.
Introducing the GraphRAG Toolkit
Amazon Neptune recently released the GraphRAG Toolkit, an open source Python library that makes it straightforward to build graph-enhanced Retrieval Augmented Generation (RAG) workflows. In this post, we describe how you can get started with the toolkit. We begin by looking at the benefits of adding a graph to your RAG application. Then we show you how to set up a quick start environment and install the toolkit. Lastly, we discuss some of the design considerations that led to the toolkit’s graph model and its approach to content retrieval.
Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 2: Enhancing fraud detection with Parquet and CSV import and export
In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. In Part 1, we introduced the import and export functionalities, and walked you through how to quickly get started with them. In this post, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.
Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 1: Introducing Parquet and CSV import and export
In this two-part series, we show how you can import and export using Parquet and CSV to quickly gather insights from your existing graph data. Part 1 introduces the import and export functionalities, and walks you through how to quickly get started with them. In Part 2, we show how you can use the new data mobility improvements in Neptune Analytics to enhance fraud detection.
How Orca Security optimized their Amazon Neptune database performance
Orca Security, an AWS Partner, is an independent cybersecurity software provider whose patented agentless-first cloud security platform is trusted by hundreds of enterprises globally. At Orca Security, we use a variety of metrics to assess the significance of security alerts on cloud assets. Our Amazon Neptune database plays a critical role in calculating the exposure of individual assets within a customer’s cloud environment. By building a graph that maps assets and their connectivity between one another and to the broader internet, the Orca Cloud Security Platform can evaluate both how an asset is exposed as well as how an attacker could potentially move laterally within an account. In this post, we explore some of the key strategies we’ve adopted to maximize the performance of our Amazon Neptune database.
How Coinbase provides trustworthy financial experiences through real-time user clustering with Amazon Neptune
In this post, we discuss how Coinbase migrated their user clustering system to Amazon Neptune Database, enabling them to solve complex and interconnected data challenges at scale.
How Apollo Tyres built their tyre genealogy solution using Amazon Neptune and Amazon Bedrock
This is a joint post co-authored with Shailender Gupta, Global Head of Data Engineering, Reporting and Analytics at Apollo Tyres Apollo Tyres, headquartered in Gurgaon, India, is a prominent global tyre manufacturer with production facilities in India and Europe. The company has a widespread presence, selling tyres to consumers and industrial customers across over 100 […]
How Amazon stores deliver trustworthy shopping and seller experiences using Amazon Neptune
Nearly three decades ago, Amazon set out to be Earth’s most customer-centric company, where people can discover and purchase the widest possible selection of safe and authentic goods. When a customer makes a purchase in our store, they trust they will receive an authentic product, whether the item is sold by Amazon Retail or by one […]
How Prisma Cloud built Infinity Graph using Amazon Neptune and Amazon OpenSearch Service
Palo Alto Network’s Prisma Cloud is a leading cloud security platform protecting enterprise cloud adoption from code to cloud workflows. Palo Alto Networks chose Amazon Neptune Database and Amazon OpenSearch Service as the core services to power its Infinity Graph. In this post, we discuss the scale Palo Alto Networks requires from these core services and how we were able to design a solution to meet these needs. We focus on the Neptune design decisions and benefits, and explain how OpenSearch Service fits into the design without diving into implementation details.
Triple your knowledge graph speed with RDF linked data and openCypher using Amazon Neptune Analytics
There are numerous publicly available Resource Description Framework (RDF) datasets that cover a wide range of fields, including geography, life sciences, cultural heritage, and government data. Many of these public datasets can be linked together by loading them into an RDF-compatible database. In this post, we demonstrate how to build knowledge graphs with RDF linked data and openCypher using Amazon Neptune Analytics.