Summary

This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students to be able to build such systems as well as read and understand recent research publications.

Content

Learning Prerequisites

Required courses

• CS-322: Introduction to database systems
• CS-10: Introduction to programming

Recommended courses

• CS-323: Introduction to operating systems
• CS-452: Foundations of software

Learning Outcomes

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

• Design big data analytics systems using state-of-the-art infrastructures for horizontal scaling, e.g., Spark
• Implement algorithms and data structures for streaming data analytics
• Decide between different storage models based on the offered optimizations enabled by each model and on the expected query workload
• Compare concurrency control algorithms, and algorithms for distributed data management
• Identify performance culprits, e.g., due to concurrency control

Teaching methods

Lectures, project, homework, exercises

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 exercise sessions to solve and discuss exercises about the recently taught material,
• work on projects, which cover the practical side of the taught material,
• take a midterm,
• take a final exam,
• read scientific papers related to the course material

Assessment methods

• 60% exams
• 40% project

Supervision

Resources

Bibliography

J. Hellerstein & M. Stonebraker, Readings in Database Systems, 4th Edition, 2005

R. Ramakrishnan & J. Gehrke: "Database Management Systems", McGraw-Hill, 3rd Edition, 2002.

A. Rajaraman & J. Ullman: "Mining of Massive Datasets", Cambridge Univ. Press, 2011.

Ressources en bibliothèque

• Database Management Systems / Ramakrishnan
• Readings in Database Systems / Hellerstein
• Mining of Massive Datasets / Rajaraman