Technologies for Big Data Management

Teacher:

• Prof. Massimo Callisto De Donato
• Webex room: https://unicam.webex.com/meet/massimo.callisto
• Email: massimo.callisto[at]unicam.it

ESSE3 Link

• Technologies for Big Data Management - AY 2025/26

Scheduling of Lectures:

• Scheduling is available at the following link

Degrees:

• MSc in Computer Science (LM-18)

Curricula:

• Methodologies and Technologies for Digital Communication

• knowledge about business data analysis.
• Understanding of main differences between classical methods in data analysis and new modern scenarios.
• Knowledge and expertise on Big Data, basic principles and concepts, techniques that enable data analysis and management at scale.
• Knowledge of the main Big Data frameworks and their application in real case studies.

• Introduction to enterprise and data management.
• Analysis of scenarios and contexts of data generation: from the inter-organizational model to the Internet of things.
• Introduction to the classic data analysis techniques: ETL (Extract, Transform, Load), Business Intelligence, Reporting Tools.
• Introduction to the Big Data model, concepts, principles and technological frameworks.
• Data Analytics methodologies and techniques: batch analysis models, streaming computation.

Course Slides

• Slides link

• Github link

• Webex recordings link

• Projects (TBD)

• TBDM A.A.2024/25

• Reference materials
  • Slides course.
  • Material provided by the teacher.
  • Examples
    • …
  • Other materials
    • …

Exam Dates A.Y. 2024/2025 (tentative)

• 02/02/2026
• 23/02/2026
• 22/06/2026
• 06/07/2026
• 27/07/2026
• 07/09/2026
• 29/09/2026
• 01/02/2027
• 15/02/2027

Exam rules:

• Writing Examination
• Project lab (max 3 member per team)

For the discussion students have to publish the final code to a public github repository (or similar) along with the necessary documnatation (pre-requisite, installation, configuration, usage, etc). Moreover a set of slides have to be presented in that day along with a practical demonstration of the running code. Slides should cover the following points:

1. Project description / objectives
2. Methodology and technology being used
3. Technical implementation
4. Achieved results
5. future improvements

Exam Results

• See news section