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.
Speakers
Weimo Liu, Cofounder & CEO, Puppygraph
Bio: Weimo Liu is the CEO and co-founder of PuppyGraph, bringing his expertise in databases and query engines from his time at Google, where he worked on the F1 team developing the unified SQL analytic engine that supports most data formats/sources and serves billions of queries per day. Before his tenure at Google, he excelled as a research scientist at TigerGraph, creating a query language for parallel distributed graph databases and a compiler to translate queries into executable C++ code. With a PhD in Computer Science from George Washington University and a role as a program committee member and reviewer for top conferences and journals in the database area (r.g., TKDE, KDD, and SIGSPATIAL), Weimo is recognized as a distinguished expert in his field.
Jaz Ku, Solutions Architect, Puppygraph
Bio: Jaz Ku is a Solution Architect with a background in Computer Science and an interest in technical writing. She earned her Bachelor's degree from the University of San Francisco, where she did research involving Rust’s compiler infrastructure. Jaz enjoys the challenge of explaining complex ideas in a clear and straightforward way.
Robert Pack, Staff Developer Advocate, Databricks
Bio: Robert Pack is a data and AI systems expert with foundations in process systems engineering and numerical optimization, holding degrees from RWTH Aachen University and Imperial College London. He began his career in process R&D within a multinational chemical company, where he designed and led transformative cloud and data initiatives that shaped the organization's digital landscape. As Chief Technology Lead, he was responsible for global data and AI platform engineering.
Robert’s open-source journey began with deep contributions to Delta Lake and Delta-RS and later expanded into the broader Rust and Apache Arrow data ecosystems. Today, as part of Databricks, he serves as a Developer Advocate dedicated to fostering a modern, high-performance data ecosystem for all. Through his work on projects such as Delta Kernel, Delta Lake, Unity Catalog, and Apache Iceberg, Robert remains committed to advancing the Open Lakehouse vision and facilitating the next generation of data infrastructure.