‫Now, let's talk about Amazon Neptune.
‫Neptune is a fully-managed graph database.
‫So an example of what a graph dataset would be,
‫would be, for example, something we all know
‫which is a social network.
‫So, if we look at a social network,
‫people are friends, they like, they connect,
‫they read, they comment and so on.
‫So users have friends, posts will have comments,
‫comments have likes from users,
‫users shares and like posts
‫and so, all these things are interconnected
‫and so, they create a graph.
‫And so, this is why Neptune is a great choice
‫of database when it comes to graph datasets.
‫So, Neptune has replication across 3 AZ,
‫up to 15 read replicas.
‫It's used to build and run applications
‫that are gonna be with highly connected datasets,
‫so like a social network,
‫and because Neptune is optimized to run queries
‫that are complex and hard on top of these graph datasets.
‫You can store up to billions of relations on the database
‫and query the graph with milliseconds latency.
‫It's highly available with application
‫across multiple Availability Zones.
‫And it's also great for storing knowledge graphs.
‫For example, the Wikipedia database is a knowledge graph
‫because all the Wikipedia articles are interconnected
‫with each other, fraud detection,
‫recommendations engine and social networking.
‫So, coming from an exam perspective,
‫anytime you see anything related to graph databases,
‫think no more than Neptune.
‫That's it.
‫I hope you liked it and I will see you in the next lecture.