Study Guide

Objective

After completing the course the student can:
- describe basic solutions for data architectures and big data
- select and use suitable data architecture
- apply ETL process and tools for handling of big data

Content

Architecture and Components of Big Data Frameworks
ETL process with Big Data for batch and streaming
Practical work with suitable tools and frameworks

Materials

Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.

Teaching methods

Contact learning, practical exercises, independent study

International connections

Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.

Student workload

Contact hours 56 h
Inpendent studying 79h, including:
- Studying the course material
- Completing exercises
- Project

Content scheduling

-Introduction to data engineering
-The basic idea of data engineering methods and pipelines
-different components
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)

Further information

Itslearning and contact classes are the main communication channels used on this course.

The student is required to have a computer capable of running a simple Ubuntu virtual machine.

Evaluation scale

H-5

Assessment methods and criteria

Homework exercises returned throughout the course
Small project at the end of the course