AWS' new serverless option for Amazon Neptune allows you to run and instantly scale graph workloads

Amazon Web Services (AWS) has announced a new serverless option for Amazon Neptune that allows you to run and instantly scale graph workloads, without the need to manage and optimize database capacity.

For the unversed, Amazon Neptune is a fully managed service that makes it easy to build and run applications that need a graph database to efficiently store and query complex and highly connected datasets. The new serverless option includes the service's advanced capabilities for high availability, performance, and resiliency.

With Amazon Neptune Serverless, you can create a graph database for your workload using the three most popular graph query languages: Apache TinkerPop Gremlin, openCypher, and SPARQL. It automatically determines and provisions the compute and memory resources to run the graph database, and scales capacity up and down based on the workload’s changing requirements to maintain consistent performance - you can begin with a small graph and scale your database instantly as your graph and workload grows.

With Amazon Neptune Serverless, you only pay for the database resources your applications use, resulting in savings of up to 90% compared to provisioning for peak capacity.

Amazon Neptune Serverless is generally available to customers running Amazon Neptune in US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Tokyo), Europe (Ireland), and Europe (London), with availability in additional AWS Regions coming soon.

"Customers tell us that they appreciate the ability to use Amazon Neptune to understand complex relationships among highly connected data points. They have also asked us to take care of the heavy lifting associated with managing capacity and optimizing for cost and performance. Now, with Amazon Neptune Serverless, customers have a graph database that automatically provisions and seamlessly scales clusters to provide just the right amount of capacity to meet demand, allowing them to build and run applications for even the most variable and unpredictable workloads without having to worry about provisioning capacity, scaling clusters, or incurring costs for unused resources," said Swami Sivasubramanian, vice president of Databases, Analytics, and Machine Learning at AWS.