In this episode of Disseminate: The Computer Science Research Podcast, host Jack Waudby sits down with Xiangyao Yu (UW–Madison), one of the leading voices shaping the next generation of cloud-native databases.

We dive deep into disaggregation — the architectural shift transforming how modern data systems are built. Xiangyao breaks down:

• Why traditional shared-nothing databases struggle in cloud environments
• How separating compute and storage unlocks elasticity, scalability, and cost efficiency
• The evolution of disaggregated systems, from Aurora and Snowflake through to advanced pushdown processing and new modular services
• His team's research on reinventing core protocols like 2-phase commit for cloud-native environments
• Real-time analytics, HTAP challenges, and the Hermes architecture
• Where disaggregation goes next — indexing, query optimizers, materialized views, multi-cloud architectures, and more

Whether you're a database engineer, researcher, or a practitioner building scalable cloud systems, this episode gives a clear, accessible look into the architecture that’s rapidly becoming the default for modern data platforms.

Links:

• Xiangyao Yu's Homepage
• Disaggregation: A New Architecture for Cloud Databases [VLDB'25]

Hosted on Acast. See acast.com/privacy for more information.

In this episode of Disseminate: The Computer Science Research Podcast, Jack sits down with Navid Eslami, PhD researcher at the University of Toronto, to discuss his award-winning paper “DIVA: Dynamic Range Filter for Variable Length Keys and Queries”, which earned Best Research Paper at VLDB.

Navid breaks down how range filters extend the power of traditional filters for modern databases and storage systems, enabling faster queries, better scalability, and theoretical guarantees. We dive into:

• How DIVA overcomes the limitations of existing range filters
• What makes it the “holy grail” of filtering for dynamic data
• Real-world integration in WiredTiger (the MongoDB storage engine)
• Future challenges in data distribution smoothing and hybrid filtering

Whether you're a database engineer, systems researcher, or student exploring data structures, this episode reveals how cutting-edge research can transform how we query, filter, and scale modern data systems.

Links:

• Diva: Dynamic Range Filter for Var-Length Keys and Queries [VLDB'25]
• Diva on GitHub
• Navid's LinkedIn

Hosted on Acast. See acast.com/privacy for more information.

In this episode of the DuckDB in Research series, host Jack Waudby sits down with Paul Groß, PhD student at CWI Amsterdam, to explore his work on adaptive factorization and worst-case optimal joins - techniques that push the boundaries of analytical query performance.

Paul shares insights from his CIDR'25 paper “Adaptive Factorization Using Linear Chained Hash Tables”, revealing how decades of database theory meet modern, practical system design in DuckDB. From hash table internals to adaptive query planning, this episode uncovers how research innovations are becoming part of real-world systems.

Whether you’re a database researcher, engineer, or curious student, you’ll come away with a deeper understanding of query optimization and the realities of systems engineering.

Links:

• Adaptive Factorization Using Linear-Chained Hash Tables

Hosted on Acast. See acast.com/privacy for more information.