Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

JUSTIFICATION
In this module, students acquire knowledge about models, techniques, and systems for storing, managing, and processing Big Data, including multidimensional data. Upon completion of the module, students will be able to model multidimensional data and design appropriate schemas and/or storage formats. They will be able to transform data from various sources into an integrated analytical data warehouse. They will be able to formulate analytical queries over large datasets and implement scalable solutions using common Big Data platforms. Finally, for a given Big Data problem, they will be able to make informed choices of models, techniques, and systems.

Learning objectives

Knowledge

Throughout the course, students will gain knowledge of theories, methods, techniques, and tools in the following areas:

Principles of Big Data scaling, including

Technologies and tools for Big Data scaling, including

Data Warehousing, including

Multidimensional databases, including

On-line Analytical Processing (OLAP), including

Students must be able to critically and reflectively engage with these theoretical topics.

Skills

After completing the course, students should be able to apply theories, methods, and models from the aforementioned areas to identify, analyze, evaluate, and propose solutions to specific practical problems. They should be able to argue for the relevance of the chosen theories, methods, and models as well as for the proposed solution. Additionally, they should be able to reflect on the significance of the context in which the solution is applied.

Specifically, it is expected that after completing the course, students will be able to:

• Model an analytical data warehouse using basic multidimensional modeling
• Design and implement appropriate schemas and/or storage formats for analytical data warehouses, e.g., a data warehouse
• Integrate and transform data from multiple different data sources, including using Extract-Transform-Load tools
• Analyze data using On-Line Analytical Processing (OLAP) tools
• Design and implement a scalable solution on a common Big Data system

Competences

After completing the course, the goal is for students to have acquired the competencies to:

• make informed choices about models, techniques, and systems for Big Data
• design, develop, and apply an appropriate Big Data solution for a realistic problem

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams

Additional information

Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854