Weimo Liu
PuppyGraph CEO
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Dr. Weimo Liu serves as the CEO of PuppyGraph. He was a former software engineer within Google's F1 team and a research scientist at TigerGraph. In these capacities, he specialized in advancing query languages and engines. Dr. Liu earned his PhD degree from GWU, and his BS degree from Fudan University. Notably, he actively participates as a program committee member and reviewer for esteemed conferences like SIGSPATIAL, TKDE, and KDD. His contributions extend to publications in VLDB and ICDE. He also served on the Expert Group for GQL at the International Committee for Information Technology Standards (INCITS).
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Integrating StreamNative's Ursa Engine with PuppyGraph for Real-Time Graph Analysis
At the Pulsar Virtual Summit EMEA 2024, StreamNative unveiled the Ursa Engine—a transformative advancement in data streaming architecture. PuppyGraph is proud to be the first graph compute engine to integrate with StreamNative's Ursa Engine. This partnership marks a shift toward democratizing access to streaming data and graph analytics—delivering cost-effective solutions without requiring a dedicated graph database. Combined with Ursa Engine, PuppyGraph enables users to query streaming data using graph query languages like Gremlin and openCypher, along with built-in visualization tools, providing a seamless experience for graph-based analytics.
In this talk, we will introduce StreamNative's Ursa Engine and explore how combining its capabilities with PuppyGraph’s zero-ETL graph query engine unlocks the potential of real-time graph analytics.
Apache Iceberg: the Answer to Observability for AI Agents
Observability has long been a core challenge in distributed systems. By collecting logs, metrics, and traces, engineers gain visibility into system behavior. In traditional microservices, execution is request-driven, topology is mostly static, and sampling is commonly used to manage data volume. Observability data is typically analyzed using SQL-based analytics engines.
AI agents fundamentally change what traces represent. Instead of predefined service paths, traces capture dynamic runtime execution: model invocations, tool calls, and orchestration steps selected based on context. It is impossible to predict execution paths in advance, and each request may produce a unique, data-dependent execution graph.
This shift introduces two key challenges: rapidly increasing data scale and continuously evolving execution graphs that cannot be modeled ahead of time. We show how Apache Iceberg, combined with a graph engine, provides a flexible and cost-efficient foundation for scalable observability in AI agent–driven systems.
A Survey of Cybersecurity Data Infra and How to Simplify It with Iceberg
Modern cybersecurity teams increasingly rely on large-scale data infrastructures to detect threats and manage risk. Traditional SIEM and log pipeline architectures often involve multiple ETL layers, duplicated data across systems, and slow, disconnected workflows that make real-time threat detection and investigation difficult.
In this talk, we focus on how Apache Iceberg simplifies and strengthens cybersecurity data pipelines. By storing raw security events and telemetry in Iceberg tables, teams gain schema evolution, time travel, and partitioning capabilities, enabling scalable, low-latency queries without complex data duplication. We show how combining Iceberg with graph query engines allows cybersecurity teams to model entities—users, devices, applications, alerts—as connected graphs. This approach supports multi-hop queries for risk scoring, lateral movement detection, and investigation workflows that would be cumbersome with traditional SQL or log-based systems.
Attendees will leave with concrete guidance on using Iceberg as a foundation for real-time, connected cybersecurity data, avoiding common pitfalls of fragmented pipelines, and enabling more intuitive threat detection and investigation workflows.
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