AWS Neptune is AWS’s managed graph database service, offered to give customers an option to easily build and run applications that work with highly connected datasets. It was first announced at AWS re:Invent 2017, and made generally available in May 2018.

Graph databases like AWS Neptune were created to address the limitations of relational databases, and offer an efficient way to work with complex data. 

What is a graph database?

A graph database is a database optimized to store and process highly connected data – in short, it’s about relationships. The data structure for these databases consists of vertices or nodes, and direct links called edges.

Use cases for such highly-connected data include social networking, restaurant recommendations, retail fraud detection, knowledge graphs, life sciences, and network & IT ops. For a restaurant recommendations use case, for example, you may be interested in the relationships between various users, where those users live, what types of restaurants those users like, where the restaurants are located, what sort of cuisine they serve, and more. With a graph database, you can use the relationships between these data points to provide contextual restaurant recommendations to users.

Details on the AWS Neptune Offering

AWS Neptune Pricing 

The AWS Neptune cost calculation depends on a few factors:

  • On-Demand instance pricing – you’ll need to pay for the compute instances needed for read-write workloads as well as Amazon Neptune replicas. These follow the general pricing for AWS On Demand instances.
  • Database Storage & I/Os – storage is also paid per usage with no upfront commitments. Storage is billed in per GB-month increments and I/Os are billed in per million request increments. 
  • Backup storage – you are charged for the storage associated with your automated database backups and database cluster snapshots. As per usual, increasing the retention period will cost more. 
  • Data transfer – you are charged per GB for data transferred in and out of AWS Neptune.

For this, as with most AWS services, pricing is confusing and difficult to predict. 

AWS Neptune Use Cases

Use cases for the AWS graph database and other similar offerings include:

  • Machine learning, such as intelligent image recognition, speech recognition, intelligent chatbots, and recommendation engines.
  • Social networking
  • Fraud detection – flexibility at scale makes graph databases useful to work with the huge amount of transactional data needed to detect fraud. 
  • Regulatory compliance – ever-more important as HIPPA, GDPR, and other regulations pose strict regulations on the way organizations use data about customers.
  • Knowledge graphs – such as advanced results for keyword searches and complex content searches.Life sciences – graph databases are uniquely suited to store models of disease and gene interactions, protein matterns, chemical compounds, and more. 
  • Network/IT Operations to keep networks secure, including identity and access management, detection of malicious file paths, and more. 
  • Supply chain transparency – graph databases are great for modeling complex supply chains that span the globe. 

Tired of SQL?

If you’re tired of SQL, AWS Neptune may be for you. A graph database is fundamentally different from SQL. There are no tables, columns, or rows – it feels like a NoSQL database. There are only two data types: vertices and edges, both of which have properties stored as key-value pairs.

AWS Neptune is fully managed, which means that database management tasks like hardware provisioning, software patching, setup, configuration, and backups are taken care of for you.

It’s also highly available and shows up in multiple availability zones. This is very similar to Aurora, the relational database from Amazon, in its architecture and availability.

Neptune supports Property Graph and W3C’s RDF. You can use these to build your own web of data sets that you care about, and build networks across the data sets in the way that makes sense for your data, not with arbitrary presets. You can do this using the graph models’ query languages: Apache TinkerPop Gremlin and SPARQL.

AWS Neptune Visualization is not built in natively. However, data can be visualized with Amazon SageMaker Jupyter notebooks, or third-party options like Metaphactory, Tom Sawyer Software, Cambridge Intelligence/Keylines, and Arcade. 

Other Graph Database Options

There’s certainly competition in the market for other graph database solutions. Here are a few that are frequently mentioned. 

AWS Neptune vs. Neo4j

Neo4j is a graph database that has been rated most popular by mindshare and adoption. Version 1.0 was released in February 2010. Unlike AWS Neptune, Neo4j is open source. Neo4j uses the language Cypher, which it originally developed. While there are several languages available in the graph database market, Cypher is widely known by now. 

Neo4j, unlike AWS Neptune, does actually come with graph visualization, which is a huge plus for working with this kind of data, though as mentioned above, there are several ways to visualize your Neptune data.

Other

Other graph databases include: AllegroGraph, AnzoGraph, ArangoDB, DataStax Enterprise Graph, InfiniteGraph, JanusGraph, MarkLogic, Microsoft SQL Server 2017, OpenLink Virtuoso, Oracle Spatial and Graph (part of Oracle Database), OrientDB, RedisGraph, SAP HANA, Sparksee, Sqrrl Enterprise, and Teradata Aster.

AWS Neptune – Getting Started

If you’re interested in the new service, you can check out more about AWS Neptune. As you get started, the AWS Neptune docs are a great resource. Or, check out some AWS Neptune Tutorials on YouTube

Once you’re on board, make sure you have cost control as a priority. ParkMyCloud can now park Neptune databases to ensure you’re only paying for what you’re actually using. Try it out for yourself!

About Chris Parlette

Chris Parlette is the Director of Cloud Solutions at ParkMyCloud. Chris helps customers reduce their cloud waste and manage their hybrid infrastructures by drawing on his years of experience working at various software startups. From SaaS to on-prem, virtualization to cloud, monitoring tools to cloud management platforms, and small businesses to large enterprises, Chris has seen it all and loves helping drive improvements to IT management. Chris earned a BS in Computer Science from the University of Maryland. He and his wife, Megan, reside in Silver Spring, MD.

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