Durée
25h Th, 10h Pr, 45h Proj.
Nombre de crédits
Enseignant
Langue(s) de l'unité d'enseignement
Langue anglaise
Organisation et évaluation
Enseignement au premier quadrimestre, examen en janvier
Horaire
Unités d'enseignement prérequises et corequises
Les unités prérequises ou corequises sont présentées au sein de chaque programme
Contenus de l'unité d'enseignement
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 concepts 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:
- Data deluge
- Basic distributed abstractions
- Reliable broadcast
- Shared memory
- Consensus
- Blockchain
- Distributed hash tables
- Cloud computing
- Distributed file systems
Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement
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.
Savoirs et compétences prérequis
Programming experience. Basic knowledge in computer networks.
Activités d'apprentissage prévues et méthodes d'enseignement
- Theoretical lectures
- Exercise sessions and programming tutorials
- Reading assignment
- Programming project (e.g., implement a simple data system).
Mode d'enseignement (présentiel ; enseignement à distance)
Lectures will taught face-to-face. Projects will be carried out remotely.
Lectures recommandées ou obligatoires et notes de cours
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.
Modalités d'évaluation et critères
- Exam (60%)
- Reading assignment (10%)
- Programming project (30%)
Stage(s)
Remarques organisationnelles
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)