Summary

The course covers fundamental principles for understanding and building systems for managing and analyzing large amounts of data.

Content

Keywords

Distributed systems, Parallel programming, Large-scale storage systems, Large-scale data management

Learning Prerequisites

Recommended courses

CS-322 Introduction to database systems

CS-323: Introduction to operating systems

CS-206 Parallelism and concurrency

Important concepts to start the course

• Algorithms and data structures.
• Scala and/or Java programming languages will be used throughout the course. Programming experience in one of these languages is strongly recommended.
• Basic knowledge or computer networking and distributed systems

Learning Outcomes

By the end of the course, the student must be able to:

• Choose systems parameters, data layouts, and application designs for database systems and applications.
• Develop data-parallel analytics programs that make use of modern clusters and cloud offerings to scale up to very large workloads.
• Analyze the trade-offs between various approaches to large-scala data management and analytics, depending on efficiency, scalability, and latency needs
• Choose the most appropriate existing systems architecture and technology for a task

Teaching methods

Lectures, exercisesand practical work

Expected student activities

During the semester, the students are expected to:

• attend the lectures in order to ask questions and interact with the professor,
• attend the exercises session to solve and discuss exercises,
• solve practical homeworks and/or finish a project during the semester,
• take the exams during the semester

Assessment methods

Homeworks, written examinations, project. Continuous control

Supervision

Resources

Bibliography

Relevant resources (textbook chapters, articles, and videos) posted on moodle page.