Master's degree in Computer Science and Engineering

Data-intensive computing systems

Course code
Name of lecturer
Damiano Carra
Damiano Carra
Number of ECTS credits allocated
Academic sector
Language of instruction
I semestre dal Oct 1, 2020 al Jan 29, 2021.

Lesson timetable

Go to lesson schedule

Learning outcomes

The course aims to provide the fundamental concepts of distributed computing systems that deal with very large data sets, together with the programming paradigms adopted by these systems. At the end of the course the student must demonstrate that he has acquired the necessary knowledge to evaluate the possible alternatives in the design of the analysis of large amounts of data, considering the benefits and limitations of possible alternatives. This knowledge will allow the student to: i) configure parallel data processing systems; ii) design solutions to analyze large amounts of data; iii) evaluate the solutions for data analysis with parallel systems, considering the system resources necessary for the analysis; iv) continue the studies autonomously in the development of advanced analysis of large amounts of data.


* Programming frameworks:
-- Distributed filesystems (HDFS);
-- Data and graph processing (MapReduce, Pregel);
-- SQL-like systems (Pig, Hive);
-- NoSQL systems (HBase, Cassandra).

* Algorithms:
-- Design of algorithms for text processing;
-- Indexing algorithms (inverted indexing);
-- Graph analysis (PageRank).

* Datacenter architectures:
-- Datacenter organization;
-- Datacenter networking;
-- Failure management.

Reference books
Author Title Publisher Year ISBN Note
Jimmy Lin, Chris Dyer Data-Intensive Text Processing with MapReduce (Edizione 1) Morgan & Claypool Publishers 2010 978-1608453429
Tom White Hadoop: The Definitive Guide (Edizione 3) Oreilly & Associates Inc 2012 978-1449311520

Assessment methods and criteria

Examination consists of a project and the corresponding documentation. The project aims at verifying the comprehension of course contents and the capability to apply these contents in the resolution of a problem. The project topic is agreed with the teacher and focus on specific case studies. The project includes the performance evaluation for different input sizes, and the evaluation of the implementation alternatives. After the evaluation of the project documentation, the student may give an oral exam where the details of the project are discussed.

© 2002 - 2021  Verona University
Via dell'Artigliere 8, 37129 Verona  |  P. I.V.A. 01541040232  |  C. FISCALE 93009870234