Category: 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.

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 […]

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 […]

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.

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.

In this post, we explore approaches that address operating Amazon Neptune in a multi-tenant SaaS environment, as well as the considerations that may influence how and when to apply these strategies depending on your tenant needs.

Amazon Neptune Analytics now supports openCypher queries over RDF graphs. When you build an application that uses a graph database such as Amazon Neptune, you’re typically faced with a technology choice at the start: There are two different types of graphs, Resource Description Framework (RDF) graphs and labeled property graphs (LPGs), and your choice of […]

To query a Neptune database in Athena, you can use the Amazon Athena Neptune connector, an AWS Lambda function that connects to the Neptune cluster and queries the graph on behalf of Athena. In this post, we provide a step-by-step implementation guide to integrate the new version of the Athena Neptune connector and query a Neptune cluster using Gremlin and SPARQL queries.

Retrieval Augmented Generation (RAG) is an innovative approach that combines the power of large language models with external knowledge sources, enabling more accurate and informative generation of content. Using knowledge graphs as sources for RAG (GraphRAG) yields numerous advantages. These knowledge bases encapsulate a vast wealth of curated and interconnected information, enabling the generation of responses that are grounded in factual knowledge. In this post, we show you how to build GraphRAG applications using Amazon Bedrock and Amazon Neptune with LlamaIndex framework.

In this post, we show how you can reduce your cost by up to 75% when getting started with graph analytics workloads using the new 32 and 64 m-NCU capacities for Neptune Analytics. Many commonly used sample datasets can fit on 32 or 64 m-NCU, allowing you to work with the same data but at a lower cost. We also discuss how to monitor the graph size and resize m-NCUs without downtime.