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

Exam schedules

There's no exam.

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 44 h
Independent studying 91h, including:
- Studying the course material
- Completing exercises
- Small Personal Project

Content scheduling

-The basic idea of big data engineering methods and pipelines
-different components and processes
-integration of said components (MQ systems)
-data engineering frameworks (Apache family)
-The goal of the course is to be able to build a data pipeline from start to finish and to understand both the process and the different components and their role.

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 and basic skills to work with Ubuntu command line.

Evaluation scale

H-5

Assessment methods and criteria

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

Assessment criteria, satisfactory (1-2)

Student has basic understanding of how the basic big data engineering processes work, what components the systems consist of and how they are used. The student has an idea of what can be done with big data engineering systems.

Assessment criteria, good (3-4)

Student has a good understanding of big data engineering systems and processes. He is able to install many of the components and understands how they work together in a pipeline.

Assessment criteria, excellent (5)

The student understands and is capable of designing big data engineering pipelines. He is able to install and configure the components and understands what kind of questions need to be considered when designing, deploying and implementing the system.