Category: Amazon Neptune

In this post, we discuss a design for a highly searchable movie content graph database built on Amazon Neptune, a managed graph database service. We demonstrate how to build a list of relevant movies matching a user’s search criteria through the powerful combination of lexical, semantic, and graphical similarity methods using Neptune, Amazon OpenSearch Service, and Neptune Machine Learning. To match, we compare movies with similar text as well as similar vector embeddings. We use both sentence and graph neural network (GNN) models to build these embeddings.

Starting with the Amazon Neptune engine version 1.3.2.X, openCypher query performance is up to 9 times faster and provides up to 10 times higher throughput than previous engine releases. You can create a new cluster or upgrade to this release for faster query performance, more open source features, and additional benefits.

In this post, we explore the key factors to consider when selecting a database for your generative AI applications. We focus on high-level considerations and service characteristics that are relevant to fully managed databases with vector search capabilities currently available on AWS. We examine how these databases differ in terms of their behavior and performance, and provide guidance on how to make an informed decision based on your specific requirements.

In this post, we explore the key considerations and best practices for implementing effective DR strategies for your Amazon Neptune database deployments.

In a previous post, we described how to connect Amazon Neptune to metaphactory, securely, and then how to explore and search the Neptune graph data using metaphactory. In this post, we show how you can use metaphactory to build an end user application using its dynamic model driven components, driven by SPARQL queries.

Amazon Neptune 1.3.2.0 now supports the Apache TinkerPop 3.7.x release line, introducing many major new features and improvements. In this post, we highlight the features that have the greatest impact on Gremlin developers using Neptune, to help you understand the implications of upgrading to these versions of Neptune and TinkerPop.

In this post, I build upon the approach of the previous post and show how you can use TinkerGraph to unit test your transactional workloads. Additionally, I show how to use TinkerGraph in embedded mode. Embedded mode requires the use of Java, but it simplifies the test environment considerably as there is no need to run the server as a separate process.

We often want to take an inventory of the types of data in our database. What is our schema? This is most useful in DEV or TEST databases whose content is created by several users or teams, is often experimental, or has multiple versions. Even in controlled environments like PROD, where the application validates data […]

A knowledge graph combines data from many sources and links related entities. Because a knowledge graph is a gathering place for connected data, we expect many of its entities to be similar. When we find that two entities are similar to each other, we can materialize that fact as a relationship between them. In this […]

A knowledge graph combines data from many sources and links related entities. Because a knowledge graph is a gathering place for connected data, we expect many of its entities to be similar. When we find that two entities are similar to each other, we can materialize that fact as a relationship between them. In this […]