OVERVIEW

When the size of structured and unstructured data exceeds the capacity of conventional database management systems,  advanced tools and methods are required for capturing, storing and managing data. Such huge amounts of data are usually stored in large-scale distributed environments, processed using specific advanced data processing environments, may be already available or arrive as a stream at processing time, and specific tools for their management are usually required.

AIMS AND CONTENT

LEARNING OUTCOMES

Students will be provided with a sound grounding on theoretical, methodological, and technological fundamentals concerning data management for advanced data processing architectures, with a specific reference to large-scale distributed environments. Students will learn key elements of NoSQL and stream-based systems as well as basic issues in parallel and distributed query processing, multi-query processing, and high-throughput transactional systems. Students will be involved in project activities.

TEACHING METHODS

Class, project and outside preparation

SYLLABUS/CONTENT

Introduction to data management in distributed systems

Introduction to Big Data
Introduction to distributed archtectures
Principles of large scale data management
Architectural approaches for large scale data management

Environments for large scale data processing (data-intensive computing)

Batch processing and MapReduce paradigm
From (Hadoop) MapReduce to Spark
High level languages for large scale data processing

Systems for large-scale data management

Introduction to NoSQL systems
NoSQL data models
Column-family data stores
Graph-based data stores

Stream-based data management

Introduction to stream data management
Models and languages for stream-data management
Large-scale stream data management

RECOMMENDED READING/BIBLIOGRAPHY

Serge Abiteboul, Ioana Manolescu, Philippe Rigaux, Marie-Christine Rousset, Pierre Senellart. Web Data Management. Cambridge University Press, 2011.

Martin Kleppmann. Designing Data-Intensive Applications. O'Reilly, 2017.
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Material and references provided by the instructors.

TEACHERS AND EXAM BOARD

Exam Board

BARBARA CATANIA (President)

LAURA DI ROCCO

GIOVANNA GUERRINI

ELENA ZUCCA

LESSONS

LESSONS START

Tuesday, October 17th 2017

Class schedule

ADVANCED DATA MANAGEMENT

EXAMS

EXAM DESCRIPTION

Written examination, oral examination (including project discussion).

ASSESSMENT METHODS

Details on how to prepare for the examination and the required degree of knowledge for each topic will be provided during the lessons.

During the semester, we will propose some groupworks as well as a project, whose development should be delivered just before the written examination.

In case of positive rate of the exercizes:

• the written exam consists of a set of questions and exercizes on basic topics of the course; the goal of this test is to verify the understanding of the main issues addressed during the lessons;
• the oral exam consists of: (i) in-depth discussion of the solutions developed by the student for the given project, in order to assess not only whether the student has reached an appropriate level of knowledge, but also whether she/he has acquired the ability to critically analyze issues related to data management in large scale environments.

In case of negative rate of the exercizes:

• the written exam consists of a set of questions and exercises on the basic topics of the course; the goal of this test is to verify the understanding of the main issues addressed during the lessons;
• the oral exam consists of: (i) in-depth discussion of the solutions developed by the student for the given project, in order to assess not only whether the student has reached an appropriate level of knowledge, but also whether she/he has acquired the ability to critically analyze issues related to data management in large scale environments; (Ii) theoretical questions and / or practices of the arguments in teaching, with particular reference to matters for which deficiencies were highlighted in the written test or in the project development.

Exam schedule