Detecting and storing complex data relationships,
AWS Launches Amazon Neptune Serverless
By Jaime Hampton in Datanami
Amazon Web Services has launched a serverless option for its Neptune graph database service used for building and running applications with highly connected datasets.
Neptune’s graph database engine is optimized for storing billions of relationships and allows low latency querying of the graph with supported languages including Apache TinkerPop Gremlin, the W3C’s SPARQL, and Neo4j’s openCypher. Use cases include recommendation engines, fraud detection, and knowledge graphs, among others.
AWS touts Amazon Neptune Serverless as a good deployment option for customers with variable or unpredictable workloads where the volume and complexity of database queries can be spiky or intermittent, leading to challenges with capacity planning. Instead of constantly monitoring and reconfiguring capacity, AWS says Neptune Serverless automatically provisions and scales graph database workloads to hundreds of thousands of queries. It also supports multiple AWS Availability Zones for high availability, read replicas for high performance, and fully managed software patching, updates, and backups.
Amazon Neptune is a fully managed database service made for building and running graph applications, according to AWS. Source: AWS
“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,” said Swami Sivasubramanian, vice president of databases, analytics, and machine learning at AWS. “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.”
AWS is continuously expanding its serverless offerings. The company unveiled serverless versions of its hosted Apache Kafka, Kinesis, Elastic MapReduce (EMR), and Redshift offerings last year, and this year brought an updated serverless database, Amazon Aurora Serverless V2, and SageMaker Serverless Inference for AI workloads. ... '
