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How AI Is Built

Graphs aren't just for specialists anymore. They are one import away | S2 E27

Feb 28, 2025
Semih Salihoğlu, a key contributor to the Kuzu project, dives into the future of graph databases. He elaborates on Kuzu's columnar storage design, emphasizing its efficiency over traditional row-based systems. Discussion highlights include innovative vectorized query processing that boosts performance and enhances analytics. Salihoğlu also explains the challenge of many-to-many relationships and Kuzu's unique approaches to join algorithms, making complex queries faster and less resource-intensive. Overall, this conversation unveils exciting advancements in data management for modern applications.
01:03:35

Podcast summary created with Snipd AI

Quick takeaways

  • Kuzu's architecture utilizes columnar storage to enhance data scan performance by reducing unnecessary I/O during queries.
  • The embedded nature of Kuzu facilitates seamless integration into various environments, allowing for flexibility in deploying temporary graphs.

Deep dives

Kuzu's High-Performance Capabilities

Kuzu is an embedded graph database designed to address the scalability challenges typically faced by traditional graph databases, especially under large datasets and complex, multi-hop queries. By implementing modern database techniques such as columnar storage and smart indexing, Kuzu enhances data processing speed and efficiency significantly. Its in-memory processing capability allows for quick analytics and temporary graph creation without needing to persist data, providing flexibility for developers in scenarios like quick experiments or serverless applications. This innovative architecture enables Kuzu to maintain high performance even when handling complex analytical tasks, thus making it suitable for modern application needs.

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