In this High Impact episode we talk to Ali Dasdan, CTO at Zoominfo. Tune in to hear Ali's story and learn about some of his most impactful work such as his work on "Map-Reduce-Merge".

The podcast is proudly sponsored by Pometry the developers behind Raphtory, the open source temporal graph analytics engine for Python and Rust.

Materials mentioned on this episode:

• Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters (SIGMOD'07)
• The Art of Doing Science and Engineering: Learning to Learn, Richard Hamming
• How to Solve It, George Polya
• Systems Architecting: Creating & Building Complex Systems, Eberhardt Rechtin

You can find Ali on:

• Twitter
• LinkedIn

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

In this episode, we dive deep into the complexities of managing analytical query workloads with our guest, Matt Perron. Matt explains how the rapid and unpredictable fluctuations in resource demands present a significant challenge for provisioning. Traditional methods often lead to either over-provisioning, resulting in excessive costs, or under-provisioning, which causes poor query latency during demand spikes. However, there's a promising solution on the horizon. Matt shares insights from recent research that showcases the viability of using cloud functions to dynamically match compute supply with workload demand without the need for prior resource provisioning. While effective for low query volumes, this approach becomes cost-prohibitive as query volumes increase, highlighting the need for a more balanced strategy.

Matt introduces us to a novel strategy that combines the best of both worlds: the rapid scalability of cloud functions and the cost-effectiveness of virtual machines. This innovative approach leverages the fast but expensive cloud functions alongside slow-starting yet inexpensive virtual machines to provide elasticity without sacrificing cost efficiency. He elaborates on how their implementation, called Cackle, achieves consistent performance and cost savings across a wide range of workloads and conditions. Tune in to learn how Cackle avoids the pitfalls of traditional approaches, delivering stable query performance and minimizing costs even as demand fluctuates wildly.

Links:

• Cackle: Analytical Workload Cost and Performance Stability With Elastic Pools [SIGMOD'24]
• Matt's Homepage

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In this High Impact episode we talk to Andreas Kipf about his work on "Learned Cardinalities".

Andreas is the Professor of Data Systems at Technische Universität Nürnberg (UTN). Tune in to hear Andreas's story and learn about some of his most impactful work.

The podcast is proudly sponsored by Pometry the developers behind Raphtory, the open source temporal graph analytics engine for Python and Rust.

Papers mentioned on this episode:

• Learned Cardinalities: Estimating Correlated Joins with Deep Learning CIDR'19
• The Case for Learned Index Structures SIGMOD'18
• Adaptive Optimization of Very Large Join Queries SIGMOD'18

You can find Andreas on:

• Twitter
• LinkedIn
• Google Scholar
• Data Systems Lab @ UTN

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