AWS Database Blog

Tag: Neptune

Using SPARQL explain to understand query execution in Amazon Neptune

Customers continue to want greater visibility and control over the services they use within AWS. When it comes to our database services, customer requests typically revolve around providing greater insights into the query optimization and processing within a given database. Database developers and administrators are mostly already familiar with the idea and use of database […]

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Amazon Neptune now supports TinkerPop 3.4 features

Amazon Neptune now supports the Apache TinkerPop 3.4.1 release. In this post, you will find examples of new features in the Gremlin query and traversal language such as text predicates, changes to valueMap, nested repeat steps, named repeat steps, non-numerical comparisons, and changes to the order step. It is worth pointing out that TinkerPop 3.4 […]

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How LifeOmic’s JupiterOne simplifies security and compliance operations with Amazon Neptune

This is a guest blog post by Erkang Zheng, the CISO at LifeOmic. Most organizations take a linear, list-based approach to security operations. It’s a two-dimensional process. First, identify resources. Second, manage their configurations. Ideally the tools and technologies for management also alert security analysts about changes in the environment and help with remediation. The […]

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Introducing Gremlin query hints for Amazon Neptune

Amazon Neptune is a fast, reliable, fully managed graph database, optimized for storing and querying highly connected data. It is ideal for online applications that rely on navigating and leveraging connections in their data. Amazon Neptune supports W3C RDF graphs that can be queried using the SPARQL query language. It also supports Apache TinkerPop property […]

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Let Me Graph That For You – Part 1 – Air Routes

We’re pleased to announce the start of a multi-part series of posts for Amazon Neptune in which we explore graph application datasets and queries drawn from many different domains and problem spaces. Amazon Neptune is a fast and reliable, fully-managed graph database, optimized for storing and querying highly connected data. It is ideal for online […]

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Analyze Amazon Neptune Graphs using Amazon SageMaker Jupyter Notebooks

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 to visualize the results. In this blog post we show you how to achieve this by connecting an Amazon SageMaker notebook to an Amazon Neptune database. […]

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