Category: Amazon Neptune

August, 2024: This post has been updated to reflect advances in technology and new features AWS released, to help you on your generative AI journey. Generative artificial intelligence (AI) has captured our imagination and is transforming industries with its ability to answer questions, write stories, create art, and generate code. AWS customers are increasingly asking […]

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, […]

In this post, we show you common use cases for Amazon Neptune Serverless, and how you can optimize for both cost and performance by following recommended best practices. Amazon Neptune is a fully managed database service built for the cloud that makes it easier to build and run graph applications. It supports both RDF and […]

DoGet App is a mobile application that connects friends for sharing in-person moments together. Suggestions for activities to engage in with friends are presented to users in card deck format: swiping up indicates no interest in an activity, and swiping down indicates interest and prompts a follow-up on when a user is available (such as […]

Modeling chemical structures can be a complex and tedious process, even with the help of modern programs and technology. The ability to explore chemical structures at the most fundamental level of atoms and the bonds that connect them is an essential process in drug discovery, pharmaceutical research, and chemical engineering. By infusing chemical research with […]

Graph databases are uniquely designed to address query patterns focused on relationships within a given dataset. From a relational database perspective, graph traversals can be represented as a series of table joins, or recursive common table expressions (CTEs). Not only are these types of SQL query patterns computationally expensive and complex to write (especially for […]

Graph databases are uniquely designed to address query patterns focused on relationships within a given dataset. From a relational database perspective, graph traversals can be represented as a series of table joins, or recursive common table expressions (CTEs). Not only are these types of SQL query patterns computationally expensive and complex to write (especially for […]

Graph databases are uniquely designed to address query patterns focused on relationships within a given dataset. From a relational database perspective, graph traversals can be represented as a series of table joins, or recursive common table expressions (CTEs). Not only are these types of SQL query patterns computationally expensive and complex to write (especially for […]

Many global corporations are managing multiple supply chains, and they depend on those operations to not only deliver goods on time but to respond to divergent customer and supplier needs. According to a McKinsey study, it’s estimated that significant disruptions to production now occur every 3.7 years on average, adding new urgency to supply chain […]

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 […]