Training and Research

Credits

3

Language

English

Class attendance

Free Choice

Location

VERONA

Learning objectives

The course offers an overview of the fundamental concepts of distributed computing systems that deal with very large datasets, together with the programming paradigms adopted by these systems. In particular, it will discuss the MapReduce paradigm, and its implementation in Spark. In addition, the system aspects of the distributed computation will be presented, including the data center architectures, and the solutions for storing such large datasets.

Prerequisites and basic notions

Python knowledge

Program

- Introduction to the course
- The MapReduce programming paradigm
- Apache Hadoop and Apache Spark
- Non-relational databases for Big Data
- Datacenter architectures

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

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Learning assessment procedures

The exam consists in carrying out a project in which the principles presented in class are applied.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Assessment

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Criteria for the composition of the final grade

-

PhD school courses/classes - 2022/2023

Course lessons
PhD Schools lessons

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Guidelines for PhD students

Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.

Documents

Title Info File
File pdf Dottorandi: linee guida generali (2024/2025) pdf, it, 104 KB, 29/10/24
File pdf PhD students: general guidelines (2024/2025) pdf, en, 107 KB, 29/10/24