t.BA.XX.IE2.14HS (Information Engineering 2) 
Module: Information Engineering 2
This information was generated on: 27 April 2024
No.
t.BA.XX.IE2.14HS
Title
Information Engineering 2
Organised by
T InIT
Credits
4

Description

Version: 2.0 start 01 February 2019

Module coordinator:

 Kurt Stockinger (stog)

Learning objectives:
Objectives Competences Taxonomy levels
How to process and model structured data, and make it available for analysis.

- You know the defining characteristics and the composition, as well as the function of DWH- and Big Data systems		 F K1
- You can develop the architecture and design of a scalable DWH system F, M K2, K3
- You understand the typical models and patterns in this domain and are enabled to implement suitable models F, M K2, K3
- You know the technologies and building blocks of DWH- and Big Data systems and are capable to use these blocks for an exemplary implementation F, M K2, K3
- You know how to asses and evaluate DWH and Big Data systems F, M K6
- You are capable of carrying out a DWH project with an arbitrary amount of data F, M K3, K5
- You have gathered hands-on experience with state of the art tools such as Pentaho, Hadoop and Spark through practical exercises F, M K2, K3

Module contents: 

We live in a world where the collection, processing and exploitation of information and data is becoming increasingly crucial. The term "Information Engineering" denotes for us the methods and approaches to design and implement knowledge-intensive information systems. These are core components with regards to the emerging area of "Data Science", i.e., the area that studies prcoessing and analysis of data and information of any characteristic(including Big Data).

The "Information Engineering 2" course covers the processing and modelling of structured data as well as its provision for analysis. The course gives an overview over the basic concepts and best practices in the areas of "Data Warehousing" (DWH) and "Big Data".

- Introduction to Decision Support Systems: definition, differentiation, comparison OLTP (transaction-based systems) and OLAP (analytical systems)
- architecture and modelling: DWH composition, data modelling for analysis
- ETL process: coupling of the OLTP and business intelligence (BI) worlds, automated loading
- data quality: error detection and correction, iterative approach to DWH modelling
- big data overview: concepts for big and unstructured data in a DWH, NoSQL
- scalable queries and analyses with Pentaho, Hadoop, Spark

Literature:

 Set of slides from lectures
 

Supplementary literature:

 - W. H. Inmon: "Building the Data Warehouse", 3rd Edition, Wiley & Sons, 2002.
- Andy Konwinski, Holden Karau, Matei Zaharia, and Patrick Wendell, Learning Spark: Lightning-Fast Big Data Analysis, O'Reilly 2015

 

Prerequisites:

 Subjects of Information Engineering 1 are NOT required

Teaching language:

 German

Module structure:
Form of instruction: Number of lessons per week:
Lecture: 14*2
Labs: 14*2
Block course:  

Exams:

 The regulation on graded class assignments is binding. However, it may be waived if a formal, written request is made by the lecturer in the first week of the semester.
Designation Type Form Scope Grade Weighting
Graded assignments during teaching semester 2 Mini projects implementation project   30%
End-of-semester exam End-of-semester exam written 90 minutes   70%

Remarks:

 Part of the larger consecutive module "Information Engineering"

Note

Course: Information Engineering 2 - Praktikum
No.
t.BA.XX.IE2.14HS.P
Title
Information Engineering 2 - Praktikum

Note

  • No module description is available in the system for the cut-off date of 27 April 2024.
Course: Information Engineering 2 - Vorlesung
No.
t.BA.XX.IE2.14HS.V
Title
Information Engineering 2 - Vorlesung

Note

  • No module description is available in the system for the cut-off date of 27 April 2024.