Big Data

Big Data

This Learning Object is part of the Master degree in Precision Agriculture, a Czech existing Master developed within AGRITECH EU project through the creation of e-learning modules under LMS Moodle and the development of an IoT laboratory with high quality sensors, IoT equipment, transfer data possibilities and installation of sensors on fields

Module details

The course aims to familiarize students with the development of technologies for processing big data (Big Data), their specific properties, and their practical applications. Students will learn to navigate current trends and capabilities of these systems, which they will then apply to their own project. They will present it in seminars during the semester and defend its final form in an exam. After graduation, they should be able not only to describe the Big Data ecosystem theoretically, but also to design and defend a specific technical solution.


Identification code: ETEW6E

Proposer: Czech University of Life Sciences Prague

Module designer: Authors`s collective of DIT CZU Prague

Organization: Czech University of Life Sciences Prague

Duration: 22h 30m

ECTS: 4

Shortcut access code: No

Year of pubblication: 2026

Topics

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Subject areas:

Delivery methods: E-Learning Asynchronous, In Person

Teaching methods: Lectures, Seminar, Working Group, Project Work

Languages: ,

Learning objectives
  • To familiarize students with the development of big data technologies, their characteristics and capabilities
  • The output will be a project that will be presented during seminars and defended during the exam (seminar paper)
     
  • Introduction, definitions, properties, types of data, classification

    Core concepts of Big Data, the 5 V's (Volume, Velocity, Variety, Veracity, Value), and classification of structured, semi-structured, and unstructured data


    Description: Lecture providing a foundational overview of Big Data ecosystems. Students will learn to distinguish between traditional data processing and Big Data challenges, understand data properties, and explore how different types of data are classified for large-scale analysis.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Systems for cluster computing

    Overview of distributed computing architectures, cluster management, parallel processing principles, and introduction to selected frameworks


    Description: Lecture on the infrastructure behind Big Data processing. Students will explore how clusters of computers work together to handle massive datasets, focusing on resource scheduling, fault tolerance, and the scalability of distributed systems.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Hardware resources, cluster architecture

    Physical and virtual hardware components of computing clusters, networking requirements, storage solutions, and the hierarchical structure of nodes


    Description: Lecture on the physical foundation of high-performance computing. Students will learn how CPU, RAM, and high-speed interconnects are organized into racks and clusters, focusing on resource abstraction and the architectural design of modern data centers.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • File systems and distributed data storage

    Principles of distributed file systems, data replication, fault tolerance mechanisms


    Description: Lecture on how Big Data is stored across multiple physical machines. Students will learn about the architecture of distributed storage, how systems handle hardware failures without data loss, and the difference between block storage and traditional file systems.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Hadoop framework

    In-depth exploration of the Apache Hadoop ecosystem, including YARN for resource management, MapReduce for distributed processing, and common ecosystem tools


    Description: Lecture on the industry-standard framework for Big Data. Students will learn how Hadoop enables the processing of massive datasets across clusters, understanding the workflow from data ingestion to parallel computation and resource orchestration.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Big data platforms

    Architecture of modern Big Data platforms, integration of storage and computing layers, and overview of distribution models (On-premise vs. Cloud-based)


    Description: Lecture on the ecosystem of platforms used to manage Big Data. Students will learn how different software components are integrated into a single functional platform, explore major vendors and open-source solutions, and understand the criteria for choosing the right platform for specific data workloads.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Programming for Big data

    Introduction to programming paradigms for distributed systems, focus on functional programming principles, and an overview of languages such as Python, Scala, and Java in the context of Big Data frameworks


    Description: Lecture on the development side of Big Data. Students will learn about the logic behind distributed code execution, the importance of immutability and transformations, and how to write efficient algorithms for parallel processing using modern APIs
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Database technologies

    Comparison of relational (RDBMS) and non-relational (NoSQL) database systems, CAP theorem, and an overview of specialized databases for Big Data (Document, Key-Value, Columnar, and Graph)


    Description: Lecture on modern data storage technologies. Students will learn about the evolution of databases from traditional SQL to distributed NoSQL architectures, focusing on scalability, data consistency models, and choosing the appropriate storage engine for large-scale applications.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Data visualization and presentation

    Principles of effective data storytelling, overview of visualization techniques for large-scale datasets, and an introduction to tools like Tableau, Power BI, and D3.js


    Description: Lecture on the final stage of the Big Data pipeline. Students will learn how to transform complex analytical results into clear, visual formats, focusing on choosing the right charts for different data types and the cognitive principles of data perception.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Public data sources and open data

    Overview of global and local open data portals, licensing models (Creative Commons), data formats for public sharing (CSV, JSON, RDF), and APIs for automated data retrieval


    Description: Lecture on the ecosystem of publicly available data. Students will learn how to identify reliable data sources, understand the legal and ethical aspects of using open data, and explore techniques for integrating public datasets into their own analytical projects or applications.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Lectures

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  • Introduction and requirements: semester schedule and organisation, assessment, seminar papers, setting up work teams

    Course logistics, detailed breakdown of the grading system, selection of seminar paper topics, and formation of collaborative student groups for semester projects.


    Description: Practical introductory session focused on course management. Students will be briefed on the semester timeline and deadlines, finalize their project teams, and choose specific research or technical topics for their seminar papers to ensure a clear path to successful assessment.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Seminar, Working Group, Project Work

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  • Consultation and review: seminar papers

    Interactive review sessions, individual and group consultations on project progress, and peer-to-peer feedback on seminar paper drafts


    Description: Seminar dedicated to refining student projects. This session provides a platform for detailed consultations with the instructor, troubleshooting technical or analytical challenges in seminar papers, and performing final reviews before submission to ensure high-quality outputs.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Seminar, Working Group, Project Work

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  • Seminar 1 - presentation and defense of the seminar paper

    Formal presentation of the final semester project, technical demonstration of results, and defending methodology and findings against questions from the audience and instructor


    Description: Practical evaluation session where students showcase their semester work. Each student or team presents their seminar paper, demonstrates any implemented technical solutions (Big Data), and engages in a professional discussion to prove their mastery of the chosen topic and its practical application.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Seminar, Working Group, Project Work

    •  
  • Seminar 2 - presentation and defense of the seminar paper

    Formal presentation of the final semester project, technical demonstration of results, and defending methodology and findings against questions from the audience and instructor.


    Description: Practical evaluation session where students showcase their semester work. Each student or team presents their seminar paper, demonstrates any implemented technical solutions (Big Data), and engages in a professional discussion to prove their mastery of the chosen topic and its practical application.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Seminar, Working Group, Project Work

    •  
  • Seminar 3 - presentation and defense of the seminar paper

    Formal presentation of the final semester project, technical demonstration of results, and defending methodology and findings against questions from the audience and instructor


    Description: Practical evaluation session where students showcase their semester work. Each student or team presents their seminar paper, demonstrates any implemented technical solutions (Big Data), and engages in a professional discussion to prove their mastery of the chosen topic and its practical application.
    Duration: 1h 30m
    Teacher: Jan Masner
    Delivery method: E-Learning Asynchronous, In Person
    Teaching method: Seminar, Working Group, Project Work

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