At Amazon Neptune, we work backwards from our customers. One insight that we got from listening to customers is that, in many cases where they explore Neptune as a solution to their problems, it’s primarily “just about graph”: they want to use the relationships in their data to solve business problems using knowledge graphs, identity graphs, fraud graphs, and more. The choice of technology, selecting Property Graph or RDF and choosing a specific query language, is often a secondary consideration and creates friction throughout the adoption process. To reduce this friction, we are working towards a unified graph database landscape that enables interoperability between data models and query languages, which we call OneGraph. In this presentation, we discuss resulting challenges at implementation and architectural level across all layers of the database stack — from the need for a unified storage model, data model independent statistics, a unified query execution runtime that overcomes conceptual differences of the query languages, our approach towards building a unified query execution and translation layer, up to the challenges in enabling widespread graph adoption and establishing a unified customer experience via data and query level interoperability.
Michael Schmidt is a Principal Engineer with Amazon Web Services. On his mission to improve Amazon Neptune’s performance, scalability, and user experience, he is the tech lead for activities centered around graph query statistics, optimization, and execution. Prior to joining Amazon Neptune, Michael was involved in the development of the Blazegraph triple store and worked as CTO for metaphacts, building an end-user focused platform that helps customers building and utilizing Enterprise Knowledge Graphs. Michael holds a PhD from University of Freiburg and was awarded the ICDT 2020 Test of Time Award for his work on “Foundations of SPARQL query optimization”