Ian Robinson

To adapt to an ever-changing threat landscape, effective cybersecurity must align policy interpretation, operational enforcement, and real-time metrics in a continuous feedback loop.

Retrieval Augmented Generation (RAG) applications use the power of generative AI to analyze private datasets. GraphRAG combines knowledge graphs with RAG to produce explainable responses that are grounded in the semantic relationships between concepts, entities, and the underlying content.

What if you could bring the connected data insights of your Amazon Neptune application to all your users—graph practitioners and non-technical users alike—while evolving your application to satisfy increasingly demanding availability, performance, and scaling requirements?

Amazon Neptune is a fast, reliable, and fully managed graph database service that makes it easier to build and run applications that work with highly connected datasets.

With the launch of Amazon Neptune Serverless, graph database workloads can automatically scale from one query to hundreds of thousands of queries. In this deep-dive session, discover how serverless addresses not only workloads with sudden spikes but also those that are unpredictable while providing customers up to 90 percent cost savings compared to provisioning for peak capacity.

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. In this session, we review some best practices for graph data modeling and how to migrate to graph from other data models.