Amazon Neptune: A Look into AWS’s Fully Managed Graph Database

Amazon Neptune, if you aren't familiar, is a fully managed graph database service offered by AWS designed for storing and querying highly connected data like social networks. It is optimized for scenarios where relationships between data points is as important as the data itself.

As you will see later on, Amazon Neptune supports two major graph models: property graphs, which have nodes and edges with key-value properties, and RDF (Resource Description Framework) that contains triples of subject-predicate-object. Amazon Neptune is compatible with three major query languages, like Gremlin, openCypher, and SPARQL. All this is exciting stuff, so let’s get started.

What Is Amazon Neptune?

As I started to mention, Amazon Neptune is a purpose-built graph database for rapid querying of relationships that handles billions of nodes and edges with low latency. It supports property graphs that are used for social, recommendation, and network analysis for their flexibility, and RDF, which is standard for the semantic web, knowledge graphs, and linked data in general.

Amazon Neptune is deployed across multiple AWS regions for global reach and disaster recovery. And in case you're asking, it is safe to use as it meets key regulatory standards like HIPAA for healthcare,  PCI DSS for payments, and ISO for overall security. This makes it suitable for sensitive and regulated workloads. If you want to learn more about graph databases, read our What is A Graph Database? A Beginner's Guide blog post.

How Does Amazon Neptune Work?

Amazon Neptune’s database engine is originally based on Blazegraph and has now evolved and been enhanced by AWS for high performance and reliability. It supports multiple query languages like Gremlin, which is used for traversing property graphs (finding friends-of-friends in a community graph), openCypher for declarative SQL-like syntax for property graph queries, and SPRQL for querying RDF data and supporting semantic relationships and ontologies.

Neptune stores its data on SSD-backed cluster volumes, replicated across multiple availability zones for durability, which makes its storage particularly fast and responsive to product needs. Furthermore, it supports up to 15 read replicas to distribute read traffic and enhance availability, and it smoothly handles instance failures. This means Neptune automatically fails over to read replica.

Moreover, AWS manages hardware, software updates, and backups efficiently. So Neptune is a very good choice to ensure fault-tolerant, self-healing storage. Take our AWS Concepts course for more in-depth explanations and a deeper understanding on AWS.

Key Features of Amazon Neptune

Amazon Neptune is rich in features and benefits, which make it a great choice for the industry. In this section, I will walk you through the main features of Neptune to keep in mind.

Scalability and performance

Amazon Neptune scales really well, depending on your usage, with a promising performance tradeoff. Thereby, it automatically increases storage as your data grows up to 64TB or even more, it supports tens of thousands of queries per second with a high throughput, and gives millisecond-latency real-time responses for interactive applications, even in large, complex graphs, as it handles billions of relationships efficiently.

Security and compliance

Neptune’s performance doesn’t come at the cost of its security, as all the data is encrypted at rest using AWS KMS and in transit using TLS, it is deployed inside a VPC for private and secure networking, and its permissions are fine-grained using AWS IAM roles and policies. This is further proved by its regulatory compliance since it is certified for HIPAA, PCI DSS, and ISO, which makes it suitable for regulated industries.

AWS integration

It's no surprise that Amazon Neptune integrates well with several AWS services and the overall ecosystem. Thus, you can easily, for instance, import and export large datasets easily and efficiently with Amazon S3, monitor performance, set alarms, and analyze logs on Amazon CloudWatch, trigger serverless functions based on database events on AWS Lambda, and run applications on EC2 instances that connect with Neptune.

Additionally, Neptune is a great tool for ML tasks since it connects well with Amazon SageMaker to integrate machine learning for advanced analytics. In fact, there is a specific use of Neptune ML, for built-in machine learning for tasks like link prediction and node classification.

Applications of Amazon Neptune

Amazon Neptune is full of functionalities that can be widely used in a diverse range of tasks. In this section, I will talk more about the real-world applications of Neptune.

Identity graphs and customer data

Neptune’s ability to store and query graph data makes it a suitable tool for aggregating customer data from multiple sources to create a unified profile. It uses relationship data to customize recommendations and communications, which stands for its personalization. It is also a great use for Fraud detection, like identifying credit card fraud. It does so by identifying suspicious patterns by analyzing connections between users, accounts, and transactions. Additionally, it is a great use for targeted advertising to deliver relevant ads by analyzing user profiles, thanks to the data’s graph structure.

Recommendation engines

Recommendation engines generally rely heavily on graph data, which chooses a suitable engine a crucial step in production. Therefore, Neptune provides the perfect toolbox for recommendation systems. One application of such is E-commerce, in which the solution demands to suggest products based on user behavior and item relationships. It is also used in media platforms to recommend movies, music, or articles based on user preferences and social connections, or to suggest friends, groups, or content by analyzing user interactions in social networks, like in social media platforms.

Knowledge graphs and fraud detection

Businesses nowadays rely heavily on integrating AI into their solutions. One example of such is using agentic workflows, or AI-driven insights. This is generally done with knowledge graphs for robust solutions. It works by organizing and linking information for semantic search and AI-driven insights, such as GraphRAG, or several other mostly used techniques to enhance AI solutions. Neptune is also robust, for semantic search applications to improve search relevance by understanding relationships between entities, and financial services to detect, for example, money laundering and fraud by tracing complex transaction networks and relationships. And there are plenty of other use cases of Neptune that extend to drug discovery, network security, and supply chain management.

Getting Started with Amazon Neptune

Amazon Neptune is well integrated into the AWS ecosystem. Therefore, I will walk you through the main setups and techniques to use on Neptune.

Setting up Neptune

To get started with Neptune, you should follow these steps:

1. Open the Neptune section of the platform

Then, click on Launch Amazon Neptune to get to the creation page.

This image shows the Amazon Neptune front page.

2. Configure the database engine options

Select the provisioned engine type if you want a fixed capacity with manual scaling, or select serverless if you prefer auto-scaling based on workload demand. And you can choose the version based on your specific use case.

This image shows the parameters you can choose within the Engine options panel when creating a database.

3. Type a name for your cluster identifier

You can choose a name like “mydbcluster”. Please follow the conventions that are mentioned in the panel.

4. Set the capacity parameters

Set the boundaries of Neptune capacity units it can access during processing. More capacity units cost more money. So it’s important to keep in mind how big your application is.

5. Choose a template

You can choose production templates if you want preconfigured instances for high availability, durability, and performance. But if you are working on the development phase, I recommend using development templates for optimized cost-efficiency and quick testing with minimal resources, since you don’t need the high-availability and durability that much for the moment.

This image shows the template settings within Neptune’s database creation. 

Once you choose one of the templates, the availability and durability settings are automatically chosen. Therefore, choosing a production template toggles replica creation in different zones automatically, whereas the development template doesn’t.

6. Connectivity settings

Select the Virtual Private Cloud (VPC) that the overreaching network your Neptune cluster is deployed into. You can also click on additional connectivity configurations to configure the subnets and security groups.

This part is crucial to manage all the security protocols of your cluster.

You can make a quick connection by going to the Notebooks section by clicking on “Notebooks” on the leftmost panel on your screen:

There is a demo notebook there by default, and it has basic queries to test your database connection and other tasks. You can also switch to the Graph Explorer by clicking on actions on the right-hand side of your screen. You can also use the provided endpoint to connect from applications or development tools.

Writing queries in Amazon Neptune

In section I will introduce you to the “hello world” queries in each of the three languages for AWS Neptune:

Gremlin

A Gremlin query is a chain of operations/functions that are evaluated from left to right. Here’s an example of adding two vertices and an edge:

g.addV('Person').property('id','1').property('name','Alice')
.as('a').
addV('Person').property('id','2').property('name','Bob')
.as('b').
addE('knows').from('a').to('b');

Where .addV('Person')…property(…) tells the engine to create a vertex with label "Person" and attaches properties like "id" and "name" to it. .as('a') / .as('b') gives each new vertex a reference (“a” and “b”) so you can link them. .addE('knows').from('a’).to('b') creates an edge labeled knows from Alice to Bob.

Here’s an example of retrieval:

g.V().hasLabel('Person').valueMap('id','name');

Where g.V().hasLabel('Person').valueMap('id','name') finds all vertices with label Person and returns their id and name properties.

openCypher

A Cypher query uses ASCII-art–style patterns to create and match graph elements. Here’s how to create two nodes and a relationship:

CREATE (a:Person {id: '1', name: 'Alice'}),
(b:Person {id: '2', name: 'Bob'})
CREATE (a)-[:KNOWS]->(b);

Where (a:Person {…}) defines a node labeled Person with properties id and name. The second CREATE uses (a)-[:KNOWS]->(b) to add a directed KNOWS relationship from a to b.

Here’s how to retrieve them:

MATCH (p:Person)
RETURN p.id, p.name;

Where MATCH (p:Person) finds all nodes with the Person label, and RETURN p.id, p.name outputs each node’s id and name.

SPARQL

A SPARQL query works over RDF triples and uses PREFIX declarations plus graph patterns. Here’s how to insert two resources and their relationship:

PREFIX ex: <http://example.com/>
INSERT DATA {
ex:Alice a ex:Person ;
ex:name "Alice" .
ex:Bob a ex:Person ;
ex:name "Bob" .
ex:Alice ex:knows ex:Bob .
}

Where PREFIX ex:… defines a namespace shortcut. Within INSERT DATA, each block of triples ends with .: ex:Alice a ex:Person assigns the RDF type, ex:name "Alice" adds the name literal, and ex:Alice ex:knows ex:Bob creates the link.

Here’s how to select them:

SELECT ?person ?name WHERE {
?person a ex:Person ;
ex:name ?name .
}

Where ?person a ex:Person matches all subjects of type ex:Person and ?person ex:name ?name retrieves their ex:name into the variable ?name. 

Monitoring and optimization

Using CloudWatch correctly would allow you to track Neptune’s performance efficiently by turning on enhanced monitoring for selected insights (up to 1-second intervals) on CPU, memory, disk, and network metrics. You can also check some metrics like Engine CPU Utilization and Freeable Memory to spot CPU/memory pressure, Buffer Cache Hit Ratio to understand cache efficiency (higher is better); Query Latency and Query Throughput to monitor how long queries take and how many are served per second. You can also set dashboards and alarms with CloudWatch that respectively let you spot trends at a glance and trigger notifications or automated scaling. Additionally, you can integrate logs to enable slow-query logging in Neptune and send logs to CloudWatch Logs. 

There are, of course, many best practices to further optimize your workflows with Neptune, which I classify into two classes. If you cannot differentiate between AWS CloudTrail and AWS CloudWatch, I recommend reading our AWS CloudTrail vs AWS CloudWatch: A Beginner's Guide blog post.

Query optimization

Optimizing a good workflow comes with optimizing your queries and here are some key tips to keep in mind when querying:

• Use indexes and labels: In Gremlin/openCypher, index properties that are frequently queried. For example g.createIndex('name','vertex') in Gremlin and CREATE INDEX FOR (n:Person) ON (n.name) on openCypher. Use well-defined RDF classes and predicates to speed up pattern matching on SPARQL.

• Profile and tune queries: Use EXPLAIN on openCypher or PROFILE on Gremlin to see traversals and filter placement. You should also push filters as early as possible to reduce the data scanned like g.V().has('Person','age',gt(30)).out('knows')…

• Avoid cartesian products: In Cypher, always connect patterns rather than matching unrelated subgraphs, or the processing time will grow dramatically. For the case of SPARQL, scope your graph patterns tightly to avoid cross-joining large sets.

• Use batch writes and bulk loads: Group vertex/edge creations into fewer requests or use Neptune’s bulk loader like CSV/JSON on S3 to ingest large volumes efficiently.

Managing large datasets

Managing large datasets is different than dealing with small, limited data, because some queries require micro-controls that grow exponentially in number with the growing size of data, therefore, I will state some of the helpers in Amazon Neptune:

• Read replicas: Offload read-heavy workloads to up to 15 read replicas to route analytical queries there to keep the primary write node responsive.
• Achieve old data: Apply TTLs or regularly export and remove aged nodes/edges to keep the working graph small.
• Partition by domain: Split very large graphs into multiple clusters like ‘social’ versus ‘transactional’ and route queries in your app.
• Monitor storage growth: Always keep an eye on auto-scaling storage and set alerts so you never hit capacity surprises.
• Regularly review slow queries: Check your slow-query logs weekly and tune or address problematic patterns.

Pros and Cons of Amazon Neptune

Neptune is full of advantages and useful tips, and tricks. However, it has some limitations you should keep in mind when choosing it.

Amazon Neptune vs. Neo4j and Others

Graph databases nowadays are fighting a fast-paced competition, especially in the emergence of LLMs, RAGs, and agentic workflows. While Neptune has many strengths, there are many potential advancements going on, such as deeper AI/ML integration, improved analytics and visualization, and more query languages or interoperability.

However, there are many competitors in the landscape, such as the famous Neo4j, which offers property graphs, a strong community, but less integration with AWS. TigerGraph, on the other hand, offers a focus on high performance, but is more complex to manage. ArangoDB is also a great competitor and offers multi-model data such as graphs, documents, and key-value, but it is not as tightly integrated with AWS.

I suggest choosing Neptune if you need managed service, AWS integration, and strong compliance. However, it is plausible to consider alternatives if you need more flexibility or multi-cloud support. Amazon Neptune versus Neo4j can be an especially tough comparison, so I recommend studying up before choosing one or the other.

Conclusion

Neptune is ideal for applications needing to manage and analyze highly connected data. It is fully managed, scalable, secure, and deeply integrated with AWS services and within the AWS ecosystem.

Remember to keep learning with us. Take our AWS Concepts course if you are unfamiliar with any of what I mentioned in this article, and best of luck with Amazon Neptune!