Amazon Neptune Resources


Documentation links below provide an overview of Amazon Neptune with instructions on using the features in the AWS Management Console and the AWS Command Line Interface. We have published AWS Reference Architectures for Using Graph Databases to help inform your choices about graph data models and query languages as well as providing reference deployment architectures.

Feature Overview
Best Practices
Neptune Serverless
Global Databases
Neptune ML
Loading Data for Gremlin
Accessing Data via Gremlin
Loading Data for openCypher
Accessing Data via openCypher
Loading RDF Data for SPARQL
Accessing Data via SPARQL


Course - Getting Started with Amazon Neptune

(9 hours, fundamental)

In this video series you will learn how to get started with Amazon Neptune. You will learn the use cases and the basics of Neptune including creating and managing your cluster, building popular graph models Property Graph and W3C’s RDF, writing queries using Apache TinkerPop Gremlin and SPARQL, troubleshooting performance, and integrating with tools and services such as Elasticsearch and AWS Glue.

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Other courses:
Build Your First Graph Application with Amazon Neptune
Getting Started with Amazon Neptune
MLOps for Neptune ML

Blog posts

No blog posts have been found at this time. Please see the AWS Blog for other resources.

See all Amazon Neptune posts on the AWS Database Blog



Customer Stories

Accenture: Natural Language Processing and Graph Databases for the Oil and Gas Industry (6:23)
AWS re:Invent 2020: ADP’s next-generation platform powers dynamic teams with Amazon Neptune (26:02)
AWS re:Invent 2018: Data & Analysis with Amazon Neptune: A Study in Healthcare Billing (48:49)
Nike: A Social Graph at Scale with Amazon Neptune (7:00)
AWS re:Invent 2019: Real-world customer use cases with Amazon Neptune (30:25)
AWS re:Invent 2017: Amazon Neptune Overview and Customer Use Cases (1:00:56)
AWS re:Invent 2020: Building the post-cookie identity graph for marketing (30:48)
AWS re:Invent 2018: Building a Social Graph at Nike with Amazon Neptune (53:46)

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)
AWS DMS supports copying data from relational databases to Amazon Neptune (1:02:34)
AWS re:Invent 2018: How Do I Know I Need an Amazon Neptune Graph Database? (46:12)
Build Event Driven Graph Applications with AWS Purpose-Built Databases (48:03)
Amazon Neptune: Build Applications for Highly Connected Datasets (32:33)
Understanding Game Changes and Player Behavior with Graph Databases (50:21)
AWS Tel Aviv Summit 2018: How Amazon Neptune and Graph Databases Can Transform Your Business (38:39)

Customer case studies

Capital One
“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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Capital One

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).

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"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."

Aron Szanto, Co-Founder - Zerobase

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ADP logo
“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.”

Zaid Masud, Chief Architect, ADP's next gen HCM

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“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.”

Sasikala Singamaneni, Software Engineering Manager - Zeta Global

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