We have published AWS Reference Architectures using Amazon Neptune to help inform your choices about graph data models and query languages as well as providing reference deployment architectures.
Accenture: Natural Language Processing and Graph Databases for the Oil and Gas Industry (6:23)
Nike: A Social Graph at Scale with Amazon Neptune (7:00)
AWS re:Invent 2020: Building the post-cookie identity graph for marketing (30:48)
AWS re:Invent 2020: ADP’s next-generation platform powers dynamic teams with Amazon Neptune (26:02)
AWS re:Invent 2019: Real-world customer use cases with Amazon Neptune (30:25)
AWS re:Invent 2018: Building a Social Graph at Nike with Amazon Neptune (53:46)
AWS re:Invent 2018: Data & Analysis with Amazon Neptune: A Study in Healthcare Billing (48:49)
AWS re:Invent 2017: Amazon Neptune Overview and Customer Use Cases (1:00:56)
AWS re:Invent 2022
AWS re:Invent 2022 - Deep dive into Amazon Neptune Serverless (53:04)
AWS Summit SF 2022 - Amazon Neptune: Using graphs to gain security insights (56:43)
AWS re:Invent 2021 - Real-world use cases with graph databases (31:25)
AWS re:Invent 2020
AWS re:Invent 2020: Deep dive on Amazon Neptune (29:50)
AWS re:Invent 2020: New capabilities to build graph apps quickly with Amazon Neptune (26:54)
AWS Tech Talks
AWS on Air 2020: AWS What’s Next ft. Amazon Neptune ML (24:05)
Build Event Driven Graph Applications with AWS Purpose-Built Databases (48:03)
Understanding Game Changes and Player Behavior with Graph Databases (50:21)
AWS DMS supports copying data from relational databases to Amazon Neptune (1:02:34)
Amazon Neptune: Build Applications for Highly Connected Datasets (32:33)
AWS Tel Aviv Summit 2018: How Amazon Neptune and Graph Databases Can Transform Your Business (38:39)
AWS re:Invent 2018: How Do I Know I Need an Amazon Neptune Graph Database? (46:12)
Customer case studies
Audible for Business
A graph database gives us more flexibility than the relational systems. We might need to do a lot of joins on our tables [in a relational model], and that would have caused high latency of a lot of our business logic. A graph database is optimized for our use case. Amazon Neptune solved what we were trying to solve.
Mayank Gupta, Software Engineer - Audible for Business
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Siemens
metaphactory and Amazon Neptune enabled Siemens Energy to build a Turbine Knowledge Graph and visualize the connections between similar parts across the entire fleet of gas turbines. Amazon Neptune, a managed graph database service, fits perfectly into the cloud-first strategy driven by Siemens Energy IT, which focuses on reliability, scalability, reduction of maintenance and integration with their existing platform on Amazon Web Services (AWS).
We chose Neptune because it is a powerful graph database that is secure, performant, and analytics-friendly. In our [contact tracing] model, each user node is connected to a device node. When a device checks in to a location, an edge forms between that device and a scannable (a QR code), which is associated with a particular site (a physical store) and linked organization (a corporate entity). Neptune allows us to store these rich relationships between users, check-ins, and locations to derive insight about the spread of the virus.
We like app-level encryption in addition to database-level encryption. When we use Amazon Neptune, the data is already encrypted before it gets to the database, and then it’s encrypted again at rest.
By leveraging [Amazon] Neptune and other AWS services, we are able to achieve a cost-efficient data platform, at scale, in a very short period of time.
