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Fiche de Cours: Systems for data management and data science

Fiche de cours

Systems for data management and data science

CS-460

Fiche de cours

Enseignant(s) :

Ailamaki Anastasia
Kermarrec Anne-Marie

Langue:

English

Summary

This is a course 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 technologies.

Content

Topics include large-scale data systems design and implementation, and specifically :

Distributed data management systems
Data management : locality, accesses, partitioning, replication
Modern storage hierarchies
Query optimization, database tuning
Transaction management
Data structures : File systems, Key-value stores, DBMS
Consistency models
Large-scale data analytics infrastructures
Parallel Processing
Data stream and graph processing

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

Office hours	Yes
Assistant.e.s	Yes
Forum	Yes

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

Moodle Link

https://go.epfl.ch/CS-460

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Semaine de référence

	Lu	Ma	Me	Je	Ve
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22

En construction

Cours

Exercice, TP

Projet, autre

légende

Semestre d'automne
Session d'hiver
Semestre de printemps
Session d'été

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