AWS Database Blog

Category: Amazon Neptune

Populating your graph in Amazon Neptune from a relational database using AWS Database Migration Service (DMS) – Part 2: Designing the property graph model

In this four-part series, we cover how to translate a relational data model to a graph data model using a small dataset containing airports and the air routes that connect them. Part one discussed the source data model and the motivation for moving to a graph model. In this post, we explore mapping our relational data model to a labeled property graph model. You may wish to refer to part one of the series to review the source relational data model. Part three covers the Resource Description Framework (RDF) data model. In part four, we show how to use AWS DMS to copy data from a relational database to Neptune for both graph data models.

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Populating your graph in Amazon Neptune from a relational database using AWS Database Migration Service (DMS) – Part 1: Setting the stage

In this four-part series, we cover how to translate a relational data model to a graph data model using a small dataset containing airports and the air routes that connect them. Part one discusses the source data model and the motivation for moving to a graph model. We discuss this for the labeled property graph in part two and for the Resource Description Framework (RDF) data model in part three. In part four, we show how to use AWS DMS to copy data from a relational database to Neptune for both graph data models.

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Write a cascading delete in SPARQL

Customers often manage tree structures in their graph applications. Typical examples include categories of topics in a knowledge graph, relationships between people in an identity graph, or transaction networks in a financial application. Often, the structures are actually forests (collections of trees) with shared subtrees. In these applications, you frequently need to traverse a tree, […]

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Graph your AWS resources with Amazon Neptune

In this post, we walk through an example we released for Neptune with integration with Altimeter. Altimeter is an open-source project (MIT License) from Tableau Software, LLC that scans AWS resources and links these resources into a graph. You can store, query, and visualize the data in Neptune. You can query the graph to examine the AWS resources and their relationships in an account. For example, you can query for resources or pathways that expose a cluster with a public IP address to check for security and compliance.

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Complement Commercial Intelligence by Building a Knowledge Graph out of a Data Warehouse with Amazon Neptune

This is a guest post from Shahria Hossain, Software Engineer, and Mikael Graindorge, Sales Operations Leader at Thermo Fisher Scientific. The continuous expansion of data volume is a growing challenge for businesses to produce strategic solutions for their customers. Thanks to innovative approaches, these challenges have become simpler to solve with the rise of new […]

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Cox Automotive scales digital personalization using an identity graph powered by Amazon Neptune

Neptune is a fully managed graph database service that makes it easy to build and run applications using highly connected datasets. Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports both the Property Graph and the Resource Description Framework (RDF) standard.

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Load balance graph queries using the Amazon Neptune Gremlin Client

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Graph applications built using Neptune use read replicas to horizontally scale read throughput. These applications use the Neptune reader endpoint to distribute connections across the replicas in the cluster. […]

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Using collaborative filtering on Yelp data to build a recommendation system in Amazon Neptune

“I’m hungry. Where should I go to eat?” It’s one of the most common questions we ask ourselves every day, and when you’re going out to spend money somewhere, you don’t want to simply pick a random place and try it—you want some sort of assurance that the restaurant you choose matches what you’re looking […]

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Visualize query results using the Amazon Neptune workbench

In this post, we look at the new visualization features recently added to the Amazon Neptune workbench and released on August 12, 2020. These additional capabilities allow you to produce an interactive graph diagram representing the results of your Gremlin and SPARQL queries. We look at some Gremlin-specific features and then do the same for SPARQL. Finally, we look at some of the more advanced ways you can modify the visualizations. As a sidenote, this entire post was produced using the workbench.

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