Dr. Denise Gosnell, Principal Product Manager for Amazon Neptune, discusses the new Neptune Analytics engine and how it helps customers gain insights from large graph databases. The podcast covers various use cases for Amazon Neptune, including fraud detection and targeted content recommendation, as well as the benefits of using Neptune Analytics in generative AI applications. Listeners can also learn how to get started with graph databases and analytics using open source formats and Jupiter notebooks.
Amazon Neptune Analytics helps customers analyze graph data from a data lake like Amazon S3 to find insights 80x faster.
Neptune Analytics supports graph algorithms and vector similarity search, making it useful for complex problems like drug discovery.
Deep dives
Graph Databases: Representing Relationships and Connections
A graph database represents the relationships and connections between data, making it ideal for building applications like social networks. Unlike traditional rows and columns, a graph database is designed to model these relationships, allowing for easier tracking and analysis. Popular use cases for graph databases include knowledge graphs, identity graphs, fraud detection, and security graphs.
Introducing Amazon Neptune: A Managed Graph Database
Amazon Neptune is a managed graph database that provides developers and application builders with a fast and reliable solution for storing and querying graph data. With the capability to handle billions of edges and support thousands of interactive queries with low latency, Neptune is particularly suited for small-scale graph data processing, such as finding friends of friends in a social network app.
Unleashing Insights with Neptune Analytics
Neptune Analytics is a new analytics engine for Amazon Neptune that enables data scientists and application developers to gain valuable insights from their graph data. Designed for large-scale graph analysis, Neptune Analytics supports graph algorithms like PageRank and connected components, as well as vector similarity search. People are using Neptune Analytics for ephemeral workloads, augmenting feature stores, and combining vector search with knowledge graphs to tackle complex problems like drug discovery and identifying pirated material.
We continue to dive deep into the recent announcements from AWS re:Invent, with the release of Amazon Neptune Analytics. Discover how Neptune Analytics helps customers find insights 80x faster by analyzing their existing Neptune graph database or graph data from a data lake such as Amazon S3. Jillian Forde is joined by Dr. Denise Gosnell (Principal Product Manager for Amazon Neptune) to learn more about Amazon Neptune Analytics. We discuss how speed is of the essence for gaining insights from large graph databases with data, such as the friendships within a social network, targeted content recommendation, fraud detection, and network threat detection then you will want to check out the new Amazon Neptune Analytics database engine.
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