Author: Mike Havey

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.

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.

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 […]

It’s common in an enterprise for data that logically fits together to be separated into different databases. Some data is better suited for one storage than another, and it may not be feasible to locate all your data in one data store. But this data often needs to be linked back together to provide a […]

To be successful with a graph database—such as Amazon Neptune, a managed graph database service—you need a graph data model that captures the data you need and can answer your questions efficiently. Building that model is an iterative process. The earliest stage of the process, in which you are merely getting initial elements on paper […]

If you’re an architect tasked with designing a solution with complex data requirements, you may decide to adopt a multimodel approach, combining multiple data models, possibly spanning multiple database technologies. For each model, you choose the best technology for the purpose at hand. AWS offers 15+ purpose-built engines to support diverse data models, including relational, […]

A knowledge graph allows us to combine data from different sources to gain a better understanding of a specific problem domain. In this post, we use Amazon Neptune (a managed graph database service) to create a knowledge graph about technology products. In addition to the data we already have in the graph, we add the […]

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 […]