Course detail

Parallel Data Processing

FEKT-MPA-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

English

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. Radim Burget, Ph.D.

Department

Department of Telecommunications (UTKO)

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.

Aims

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

Dasgupta, Nataraj. "Practical big data analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R." (2018) (EN)