Data Systems Research

IF5OT1 - 6 ECTS

Overview

Learning Objectives

This course aims to provide students with an overview of research practices in data systems. It will cover the role of data systems in the broader computing landscape, highlight recent work from top-tier university labs, and host guest talks from both academic and industrial researchers.

Quoting Andy Pavlo’s excellent course description:

“This course is appropriate for students who are ready to sweat their systems programming skills.”

Additional Notes

• The course is research-oriented and hands-on
• Students are expected to be self-driven and curious
• Infrastructure for reproducibility (e.g., cloud setup) will be made available
• Biased project proposal can come from the professor or the students

SCHEDULE

Topic		Day	Date	From	To	Material	Video	Comment
Intro	📓	Monday	2025/11/24	10:00	12:00	Slides Papers
Reproducibility	🖐🖐	Wednesday	2025/11/26	14:00	18:00
Reproducibility	🖐🖐	Monday	2025/12/01	14:00	18:00
Reproducibility	🖐🖐	Wednesday	2025/12/03	14:00	18:00
Problem Definition	📓+🖐	Monday	2025/12/08	14:00	18:00
Problem Submission	🏁	Friday	2025/12/12	0:00	23:59
Validation vs Evaluation	📓+🖐	Monday	2026/01/05	15:00	19:00
Prototyping (validation)	🖐🖐	Tuesday	2026/01/06	10:00	12:00
Prototyping (validation)	🖐🖐	Wednesday	2026/01/07	8:00	12:00
Prototyping (evaluation)	🖐🖐	Tuesday	2026/01/13	8:00	12:00
Project Submission	🏁	Tuesday	2026/01/27	0:00	23:59
Poster Session	🎭	Wednesday	2026/01/28	14:00	18:00

NB: The course schedule can be subject to changes!

Practices Detail

Project 1: Reproducing Evaluation Results

Students will choose from a pool of recent research papers in the field. The objectives are:

• Deeply understand the selected paper • Reproduce the evaluation results locally • (Bonus) Reproduce the results in a distributed cloud environment (infrastructure will be provided)

Project 2: System Prototyping

Students will:

• Propose a (potentially crazy) research idea to prototype • Choose an existing data system as a base (e.g., DuckDB) • Develop a proof of concept (PoC) using strong but reasonable assumptions • Define and justify success metrics for their PoC

At the end of the semester, students will participate in a poster session to present both of their projects.