didattica:ay2526:big:main [Computer Science @ Unicam]

====== Technologies for Big Data Management ======
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===== News =====
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  * <wrap em>**Exam results**</wrap>: the results of the exam of 22 June 2026 are available at the following  [[https://drive.google.com/file/d/14MQnmDVYIpKx1HX0JEEvCrd9DH45RJsi/view | link ]].
  * <wrap em>**Exam results**</wrap>: the results of the exam of 23 February 2026 are available at the following  [[https://drive.google.com/file/d/1Mdd7YjFlI5FIbmMBEVlICoaZRB5vcX6m/view | link ]].
  * <wrap em>**Exam results**</wrap>: the results of the exam of 10 February 2026 are available at the following  [[https://drive.google.com/file/d/1YU8seZSQZrrvuZKP5D7ZRffw1VwB1UcP/view | link ]].
  * <wrap em>**No lesson on 1 December**</wrap>: no lesson on this day.
  * <wrap em>**Lesson update**</wrap>: The lessons planned for 6, 10 and 13 November 2025 will take place in LA1.
  * <wrap em>**No lesson on 20 October**</wrap>: no lesson on this day.
  * <wrap em>**No lesson on 16 October**</wrap>: no lesson on this day.
  * <wrap em>**Webex recordings**</wrap>: recordings are available at the following [[https://docs.google.com/document/d/10WH7KwDXP0v2-inC2M8x8qf2175xLWD3uYqNdaJYWOk/view | link]].
  * <wrap em>**Course start**</wrap>: the course is planned to start the **6th of October** - AB2 - Polo Lodovici A.
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===== News =====
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There is not any news.
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===== General Info =====
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**Teacher**: 
  * [[https://docenti.unicam.it/pdett.aspx?ids=N&tv=d&UteId=1056|Prof. Massimo Callisto De Donato]]
  * Webex room: https://unicam.webex.com/meet/massimo.callisto
  * Email: massimo.callisto[at]unicam.it

**ESSE3 Link**
  * [[https://unicam.coursecatalogue.cineca.it/insegnamenti/2025/11166/2025/5/10025?coorte=2025&schemaid=4380|Technologies for Big Data Management - AY 2025/26]]

**Scheduling of Lectures**:
  * Scheduling is available at the following [[:didattica:orario_en|link]]
  
**Degrees**:
  * [[didattica:mscs|MSc in Computer Science (LM-18)]]



**Curricula**:
  * Methodologies and Technologies for Digital Communication


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===== Course Objectives =====

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  * 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.
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===== Syllabus =====
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  *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.
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===== Study material =====
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**Course Slides**
  * Slides {{ https://drive.google.com/drive/folders/1l6kooACXRzrm5j6DZohjNbtK2otsiVcI?usp=drive_link | link}}

  * Github {{ https://github.com/massimocallisto | link }}
  
  * Webex recordings {{ https://docs.google.com/document/d/10WH7KwDXP0v2-inC2M8x8qf2175xLWD3uYqNdaJYWOk/edit?usp=sharing | link}}

  * Projects (TBD)

  * TBDM [[http://didattica.cs.unicam.it/doku.php?id=didattica:ay2425:tbdm:main | A.A.2024/25]]

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

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===== Exams =====
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**Exam Dates A.Y. 2024/2025 (tentative)**
  * 09/02/2026
  * 23/02/2026
  * 22/06/2026
  * 07/07/2026
  * 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:
  - Project description / objectives
  - Methodology and technology being used
  - Technical implementation
  - Achieved results
  - future improvements

** Exam Results **
  * See news section

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