Category: Amazon Neptune

This is a guest post written by Roberto García, Associate Professor and Deputy Vice-rector for Research & Transfer at Universitat de Lleida, Spain. In this post, we illustrate how to use the Rhizomer web application to interact with knowledge graphs available as semantic data from an Amazon Neptune instance through its SPARQL endpoint. Neptune is […]

Many AWS customers are looking to solve their business problems by storing and integrating data across a combination of purpose-built databases. The reason for that is purpose-built databases provide innovative ways to build data access patterns that would be challenging or inefficient to solve otherwise. For example, we can model highly connected geospatial data as […]

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Whether you’re creating a new graph data model and queries, or exploring an existing graph dataset, it can be useful to have an interactive query environment that allows you […]

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications with highly connected datasets. You can use Neptune to build fraud detection, entity resolution, product recommendation, and knowledge graph applications. Built on open standards, Neptune enables developers to use three popular open-source graph query languages […]

Customers have asked for a way to interact with graph datasets in Amazon Neptune using business intelligence (BI) tools such as Amazon QuickSight. Although some BI tools offer generic HTTP connectors that allow you to define a set of REST API calls to extract data from REST endpoints, you have to predefine either Gremlin or […]

Managing virtual machine images that you standardize through configuration, consistent security patching, and hardening (also called “golden images”) is a time-consuming task. System administrators and database administrators responsible for these tasks have to define the characteristics of these images (such as which software to pre-install, which versions to use, and which security configurations to apply). […]

This blog post was last reviewed or updated May, 2022. September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. In this post, we discuss the need for a metadata and data lineage tool and the problems it solves, how to rapidly deploy it in the language you prefer using […]

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Neptune’s database engine is optimized for storing billions of relationships and querying with millisecond latency. The W3C’s Resource Description Framework (RDF) model and the popular Labeled Property Graph model […]

Amazon Neptune ML provides a simple workflow for training machine learning (ML) models for graph data. With version 1.0.5.0, Neptune ML delivers additional enhancements to all the steps of this workflow to reduce cost, increase speed, and offer a more flexible modeling experience. Starting with data export and data processing, Neptune ML now provides additional […]

As an application developer building graph applications with Amazon Neptune, your graph data may be evolving on a regular basis, with new nodes and or new relationships between nodes being added to the graph to reflect the latest changes in your underlying business data. Amazon Neptune ML now supports incremental model predictions on graph data […]