Category: Amazon Neptune

Cloud computing has changed almost every business and industry by changing the delivery and consumption model. With the Cloud, businesses no longer need to plan for and procure servers and other IT infrastructure weeks or months in advance. This allows more flexibility and reliability, increased performance and efficiency, and helps to lower IT costs. The […]

Time to Live (TTL) is a mechanism that helps determine the longevity or lifespan of data, files, infrastructure, or even entire environments. When working with data, it could represent the amount of time a leaderboard expires in memory before being reloaded from storage, or how long a file must be kept for regulatory or compliance […]

Amazon Neptune is a fully managed graph database service built for the cloud that makes it easier to build and run graph applications that work with highly connected datasets. Neptune provides built-in security, continuous backups, serverless compute, and integrations with other AWS services. Neptune supports in-place upgrades of cluster and database instances. Upgrade of a Neptune cluster can be done either manually or automatically (during the database maintenance window).

In this post, we describe all the features that have been released as part of the recent 1.2.1.0 engine update to Amazon Neptune. Amazon Neptune is a fast, reliable, and fully managed graph database service for building and running applications with highly connected datasets, such as knowledge graphs, fraud graphs, identity graphs, and security graphs. […]

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 […]