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.