Advanced Data Systems
In this course, you will both learn state-of-the-art techniques that power state-of-the-art data-intensive applications and systems running on modern hardware and get to apply these techniques on a modern data-intensive system.
To transform the sheer amount of complex data into timely discoveries that influence the society, data-intensive systems (including database systems and machine learning platforms) must utilize the full processing power offered by modern processor and storage technologies.
In this course, you will learn the state-of-the-art techniques for data management and processing on modern hardware (multicores, hardware accelerators, microsecond-scale storage, and 100 GBE).
In parallel, you will apply
some of these techniques on widely-used open-source data-intensive systems.
As a result, you will get hands-on experience with how to design, implement, and evaluate new components of an open-source data-intensive system.
Computer Systems Performance class.
Intended learning outcomes
After the course, the student should be able to:
- Analyze the functional and performance requirements of a data-intensive system (e.g., database system or machine learning platform)
- Navigate the codebase of complex production-grade open-source software
- Design and implement components in the context of a production-grade data system
- * Evaluate the performance characteristics of a software system
- Reflect upon the evolution of the hardware (processors, storage, networks) and its impact on the landscape of data-intensive systems and applications
- Reflect upon research papers published by others and present research work to a broad technical audience
The course is composed of lectures and exercise sessions.
- The lectures will have presentations and discussions of recent research papers (done by both the lecturers and students) that focus on data systems on modern hardware.
- Each week, we will have a specific topic to focus on in terms of papers.
- The exercise sessions will focus on project assignments.
The course literature is published on the course page on LearnIT.
There will be research papers to read each week on that week's topic.
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 15%
- Lectures: 25%
- Exercises: 25%
- Project work, supervision included: 25%
- Exam with preparation: 10%
Ordinary examExam type:
D: Submission of written work with following oral, Internal (7-point scale)
D22: Submission with following oral exam supplemented by the submission.
Students submit a report based on the project assignments. This report is submitted for the exam.