Unified Data, Unified Insights: Meet the Graph Lakehouse

Graph workloads are becoming increasingly prevalent across domains. Security teams trace attack paths through complex networks. Financial institutions analyse transaction flows. Supply chains track dependencies across vendor ecosystems. These tasks rely on efficiently querying relationships within large datasets that continue to grow in size and complexity, often distributed across multiple systems. ​To manage this scale, many organisations adopt data lakes and lakehouses as centralised platforms for storing diverse data. While these architectures are effective for handling volume and variety, traditional query engines are not built for graph-specific operations such as traversals or multi-hop exploration. As a result, teams often maintain separate systems: a lakehouse for general analytics and a graph database for relationship-driven analysis. This split architecture introduces ongoing data movement, duplication, and pipeline maintenance, resulting in increased latency and operational overhead. What remains missing is a way to support graph analytics directly within the lakehouse, without requiring the copying or transformation of the underlying data. ​Join us as we introduce the Graph Lakehouse, built with Delta Lake and PuppyGraph. In this live event, you'll see hands-on demonstrations of how to query your Delta Lake tables as graphs, perform complex multi-hop analytics using Cypher and Gremlin, and visualize your tabular data as interconnected graph structures. Discover how to unlock relationship-driven insights without moving data, building pipelines, or maintaining separate systems.