January 07, 2024

Pushing The Limits Of Scalability And User Experience For Data Processing WIth Jignesh Patel

50 minutes

Summary

Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. As the sophistication increases, so does the complexity, leading to challenges for user experience. Jignesh Patel has been researching these areas for several years in his work as a professor at Carnegie Mellon University. In this episode he illuminates the landscape of problems that we are faced with and how his research is aimed at helping to solve these problems.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management

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Your host is Tobias Macey and today I'm interviewing Jignesh Patel about the research that he is conducting on technical scalability and user experience improvements around data management

Interview

Introduction

How did you get involved in the area of data management?

Can you start by summarizing your current areas of research and the motivations behind them?

What are the open questions today in technical scalability of data engines?

What are the experimental methods that you are using to gain understanding in the opportunities and practical limits of those systems?

As you strive to push the limits of technical capacity in data systems, how does that impact the usability of the resulting systems?

When performing research and building prototypes of the projects, what is your process for incorporating user experience into the implementation of the product?

What are the main sources of tension between technical scalability and user experience/ease of comprehension?

What are some of the positive synergies that you have been able to realize between your teaching, research, and corporate activities?

In what ways do they produce conflict, whether personally or technically?

What are the most interesting, innovative, or unexpected ways that you have seen your research used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on research of the scalability limits of data systems?

What is your heuristic for when a given research project needs to be terminated or productionized?

What do you have planned for the future of your academic research?

Contact Info

Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.

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Links

Carnegie Mellon Universe

Parallel Databases

Genomics

Proteomics

Moore's Law

Dennard Scaling

Generative AI

Quantum Computing

Voltron Data

Podcast Episode

Von Neumann Architecture

Two's Complement

Ottertune

Podcast Episode

dbt

Informatica

Mozart Data

Podcast Episode

DataChat

Von Neumann Bottleneck

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA