Course detail

Parallel Data Processing

FEKT-MKC-PZPAcad. year: 2023/2024

Parallelization using CPU. Parallelization using GPU (matrix operations, deep learning algorithms). Technologies: Apache Spark, Hadoop, Kafka, Cassandra. Distributed computations for operations: data transformation, aggregation, classification, regression, clustering, frequent patterns, optimization. Data streaming – basic operations, state operations, monitoring. Further technologies for distributed computations.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

final exam
The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.


The goal of the course is to introduce parallelization for data analysis with using common processors, graphic processors and distributed systems.
Students have skills of design and implementation of various forms of parallel systems to solve big data challenge. They will learn techniques for the parallelization of computations using CPU and GPU and further they will learn techniques for distributed computations. Students will control technologies Apache Spark, Kafka, Cassandra to solve distributed data processing with using data operations: data transformations, aggregation, classification, regression, clustering, frequent patterns.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Holubová, Irena, et al. Big Data a NoSQL databáze. Grada, 2015. (EN)

Recommended reading

BARLAS, Gerassimos. Multicore and gpu programming: an integrated approach. ISBN 9780124171374 (EN)

Classification of course in study plans

  • Programme MKC-TIT Master's, 2. year of study, winter semester, compulsory-optional

Type of course unit



26 hours, optionally

Teacher / Lecturer

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer


13 hours, optionally

Teacher / Lecturer