Systems for data management and data science

Summary

This course is intended for students who want to understand modern large-scale data analysis systems and database systems. The course covers fundamental principles for understanding and building systems for managing and analyzing large amounts of data. It covers a wide range of topics and technologi

Content

Learning Prerequisites

Required courses

• CS-107 Introduction to programming
• CS-214 Software construction
• CS-300 Data-Intensive Systems
• CS-202 Computer systems

or equivalent courses

Important concepts to start the course

• Knowledge of 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 recommanded.
• Basic knowledge or computer networking and distributed systems.

Learning Outcomes

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

• Understand in detail the 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
• Understand the advantage and disavantages of different storage models for a given workload, based on the offered optimization enabled by each model and the workload characteristics
• Compare concurrency control algorithms, and algorithms for distributed data management
• Configure systems parameters, data layouts, and application designs for database systems
• Develop data-parallel analytics programs that make us of modern clusters and cloud offerings to scale up to very large workloads
• Analyze the trade-offs between various approaches to large-scale data management and analytics, depending on efficiency, scalability, and latency needs

Teaching methods

Lectures, project, homework, exercises and practical work

Expected student activities

• Attend lectures and participate in class
• Complete a project as per the guidelines posted by the teaching team

Assessment methods

• Project
• Midterm (as needed)
• Final exam

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

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

Moodle Link

• https://go.epfl.ch/CS-460