エピソード

  • High Impact in Databases with... Ali Dasdan
    2024/10/08

    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.

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    1 時間 3 分
  • Matt Perron | Analytical Workload Cost and Performance Stability With Elastic Pools | #57
    2024/07/22

    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



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

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    52 分
  • High Impact in Databases with... Andreas Kipf
    2024/07/15

    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.

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    53 分
  • Marvin Wyrich & Justus Bogner | How Software Engineering Research Is Discussed on LinkedIn | #56
    2024/07/08

    In this episode, we delve into the intersection of software engineering (SE) research and professional practice with experts Marvin Wyrich and Justus Bogner. As LinkedIn stands as the largest professional network globally, it serves as a critical platform for bridging the gap between SE researchers and practitioners. Marvin and Justus explore the dynamics of how research findings are shared and discussed on LinkedIn, providing both quantitative and qualitative insights into the effectiveness of these interactions. They reveal that a significant portion of SE research posts on LinkedIn are authored by individuals outside the original research team and that a majority of comments on these posts come from industry professionals, highlighting a vibrant but underutilized avenue for science communication.


    Our guests shed light on the current state of this metaphorical bridge, emphasizing the potential for LinkedIn to enhance collaboration and knowledge exchange between academia and industry. Despite the promising engagement from practitioners, the discussion reveals that only half of the SE research posts receive any comments, indicating room for improvement in fostering more interactive dialogues. Marvin and Justus offer practical advice for researchers to better engage with practitioners on LinkedIn and suggest strategies for making research dissemination more impactful. This episode provides valuable insights for anyone interested in leveraging social media for advancing software engineering knowledge and practice.


    Links:
    • ICSE'24 Paper
    • Marvin's Homepage
    • Justus's Homepage

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

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    48 分
  • High Impact in Databases with... Joe Hellerstein
    2024/07/01

    In this High Impact episode we talk to Joe Hellerstein.


    Joe is the Jim Gray Professor of Computer Science at UC Berkeley. Tune in to hear Joe'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.


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

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    53 分
  • Harry Goldstein | Property-Based Testing | #55
    2024/06/25

    In this episode, we chat with Harry Goldstein about Property-Based Testing (PBT). Harry shares insights from interviews with PBT users at Jane Street, highlighting PBT's strengths in testing complex code and boosting developer confidence. Harry also discusses the challenges of writing properties and generating random data, and the difficulties in assessing test effectiveness. He identifies key areas for future improvement, such as performance enhancements and better random input generation. This episode is essential for those interested in the latest developments in software testing and PBT's future.


    Links:
    • ICSE'24 Paper
    • Harry's website
    • X: @hgoldstein95

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

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    49 分
  • High Impact in Databases with... Raghu Ramakrishnan
    2024/06/17

    In this High Impact episode we talk to Raghu Ramakrishnan.


    Raghu is CTO for Data and a Technical Fellow at Microsoft. Tune in to hear Raghu'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.


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

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    24 分
  • Gina Yuan | In-Network Assistance With Sidekick Protocols | #54
    2024/06/10

    Join us as we chat with Gina Yuan about her pioneering work on sidekick protocols, designed to enhance the performance of encrypted transport protocols like QUIC and WebRTC. These protocols ensure privacy but limit in-network innovations. Gina explains how sidekick protocols allow intermediaries to assist endpoints without compromising encryption.


    Discover how Gina tackles the challenge of referencing opaque packets with her innovative quACK tool and learn about the real-world benefits, including improved Wi-Fi retransmissions, energy-saving proxy acknowledgments, and the PACUBIC congestion-control mechanism. This episode offers a glimpse into the future of network performance and security.


    Links:
    • NSDI'2024 Paper
    • Gina's Homepage
    • Sidekick's Github Repo





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

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    55 分