Duration
25h Th, 10h Pr, 45h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
In the modern data landscape, large-scale data systems have become a critical component of the data science analysis pipeline. They are of primary importance for the reliable storage, but also for the analysis, of the increasingly larger volumes of data encountered in web-based applications, cloud computing centers or in networks of connected objects.
However, large-scale data systems remain notoriously difficult to build because they need to scale to hundreds or thousands of machines, they must be tolerant to crashes, they have to cope with concurrent execution and they need to ensure consistency of the data they store.
In this context, the course will cover elements of systems for data science in a bottom-up fashion. We will first cover the foundational abstractions that are the core of distributed systems, including basic abstractions and system assumptions, reliable broadcast, shared memory and consensus. We will then study data computing systems that are built on top of those components, including MapReduce and computational graph systems (Spark). Similarly, we will study distributed storage systems, including distributed file systems, distributed key-value stores and block chains.
Topics to be covered (tentative and subject to change):
- Data deluge
- Basic distributed abstractions
- Reliable broadcast
- Shared memory
- Consensus
- Blockchain
- Distributed hash tables
- Cloud computing
- Distributed file systems
Learning outcomes of the learning unit
At the end of the course, the student will have understood the core building blocks of reliable distributed systems. He/she will also have acquainted with industrial data systems and their inner workings. Finally, he/she will have developed a critical thinking regarding the benefits and limitations of these systems in the context of data science needs.
Prerequisite knowledge and skills
Programming experience. Basic knowledge in computer networks.
Planned learning activities and teaching methods
- Theoretical lectures
- Exercise sessions
- Reading assignment
- Programming project (e.g., implement a simple data system).
Mode of delivery (face to face, distance learning, hybrid learning)
Lectures will taught face-to-face. Projects will be carried out remotely.
Organisational adjustments related to the current health context
For the January exam session:
- If in-person exams are possible, then the evaluation will be based on an in-person oral exam.
- Otherwise, the evaluation will be based on a remote oral exam.
Recommended or required readings
Slides will be made publicly available on GitHub during the semester.
Part of the course will be based on "Introduction to Reliable and Secure Distributed Programming", Christian Cachin, Rachid Guerraoui, Luis Rodrigues, Springer. This book is recommended.
Assessment methods and criteria
Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.
The evaluation is divided into the following units:
- Oral exam (50%)
- Reading assignment (10%)
- Programming project (40%)
Work placement(s)
Organizational remarks
The website for the course is https://github.com/glouppe/info8002-large-scale-database-systems
Contacts
- Teacher: Prof. Gilles Louppe (g.louppe@uliege.be)
- Assistant: Joeri Hermans (joeri.hermans@uliege.be)