Graph relationships with Amazon Neptune


In this lesson, you build a fraud-detection service for your restaurant-rating application. When a rating comes in to your application, add the data to your fraud detection service. Then analyze the rating to see if it should be flagged for manual review or for removal. This service uses Amazon Neptune, a fully managed graph database, for its data storage.

This lesson teaches you how to use a fully managed Neptune database in an application. First, you learn why you would want to use a graph database such as Neptune. Then you walk through the steps to create a Neptune database, design your data model, and use the database in your application. At the end of this lesson, you should feel confident in your ability to use Neptune in your application.

Time to complete: 3045 minutes

Purpose-built Databases - Neptune (22:20)
Why use Neptune?

Neptune is a fully managed graph database provided by AWS. A graph database is good for highly connected data with a rich variety of relationships. Many companies use graph databases for the following use cases:

  • Recommendation engines: You can use a graph database to map users and followers in a social network or to map customers to item purchases in an ecommerce application. By analyzing the connections between similar users or customers, you can provide accurate recommendations of friends to follow or additional items to purchase.
  • Fraud detection: Payment companies use graph databases to identify rings of fraudulent transactions. By analyzing the relationships between email addresses, IP addresses, and other shared information, it is easier to flag suspicious activity.
  • Knowledge graphs: You can use graph databases to connect related pieces of information to show connections between people, places, and concepts. This can enable rich context around entities in your storefront or knowledge hub.

With Neptune, you get a fully managed graph database experience. This means you don't need to focus on instance failover, database backups and recovery, or software upgrades. You can focus on building your application and delivering value to your customers.

Lesson contents

In this lesson, you learn how to build a fraud-detection service that uses Neptune for data storage. This lesson has five steps.

In this lesson, you learned how to create and use a Neptune database in your application. First, you created a Neptune database and configured network access so that you could connect to the database. Then you learned about data modeling with a graph database and loaded your database with sample data. Finally, you saw how to query a graph database in your application to traverse relationships in your data. You can use these patterns when building applications with Neptune.