Faculty of Technical Sciences

Subject: Big Data Architectures (17.RVP04)

Native organizations units: No data
General information:
 
Category Professional-applicative
Scientific or art field Applied Computer Science and Informatics
Interdisciplinary No
ECTS 6
Educational goal:

Understanding of concepts and methods in computer systems for Big Data processing and learning techniques for problem solving in this domain.

Educational outcome:

Students acquire advanced knowledge about development, architectures, and applications of Big Data systems. Acquired knowledge is used in practice and follow-on courses HPC in Scientific Computing and HPC in Data Science.

Course content:

Concepts and methods in Big Data processing. Computer systems and algorithms for Big Data processing. Layers in Big Data systems (batch, serving, and speed). Fundamentals of Hadoop. Hadoop components - MapReduce system for data processing, HDFS file system, and YARN cluster resource management system. Effiecient searching through large datasets (Elasticsearch). Fundamentals of Big Data applications in scientific computing and data science.

Teaching methods:

Teaching is performed through lessons, oral, and computer exercises (in the computer classroom), as well as consultations. Through the teaching process, students are constantly motivated to an intensive discussion, problem oriented reasoning, independent study work, and active participation in the whole lecturing process. The prerequisite to enter final exam is to complete all the pre-exam assignments by earning at least 30 points.

Literature:
Authors Title Year Publisher Language
White, T. Hadoop: The Definitive Guide 2015 OReilly Media English
Marz, N., Warren, J. Big Data : Principles and best practices of scalable realtime data systems 2015 Manning Publications, New York English
Knowledge evaluation:
Course activity Pre-examination Obligations Number of points
Test Yes Yes 10.00
Test Yes Yes 10.00
Theoretical part of the exam No Yes 30.00
Complex exercises Yes Yes 30.00
Test Yes Yes 10.00
Test Yes Yes 10.00
Lecturers:
API Image

vanr. prof. dr Dimitrieski Vladimir

Associate Professor

Lectures

vanr. prof. dr Kordić Slavica

Associate Professor

Lectures

Asistent Ivković Vladimir

Assistant - Master

Computational classes

Asistent Todorović Nikola

Assistant - Master

Computational classes

Faculty of Technical Sciences

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© 2024. Faculty of Technical Sciences.