623.500 (20W) Data Engineering
Überblick
Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
- Lehrende/r
- LV-Titel englisch Data Engineering
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Onlinelehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 26 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 08.10.2020
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The successful student will have a deeper understanding of the challenges imposed by Big Data and know state of the art data engineering methods and techniques focusing on big data applications.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The VC will be a mixture of a classical lecture, presentations of assignment solutions and student presentations. The course will be held fully online via MS Teams.
Inhalt/e
- Introduction to Big Data, Data Engineering and Data Science.
- Recap on RDBMS and common file formats.
- Managing XML and JSON in RDBMS.
- Advanced SQL queries.
- Scaling of RDBMS.
- Big Data Frameworks
- MapReduce
- Apache Spark
- SQL on Big Data Architectures
- (Big) Data Integration
- Data Provenance and Data Quality
Erwartete Vorkenntnisse
Relational Databases (Lecture "Datenbanken"), Java Programming
Literatur
Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data. Cambridge University Press New York, NY, USA ©2018 ISBN:1107186129 9781107186125
Prüfungsinformationen
Prüfungsmethode/n
- Credits for weekly assignments.
- Project and Project Presentation.
Prüfungsinhalt/e
All topics addressed by the lecture or practical parts.
Beurteilungskriterien/-maßstäbe
The final grade is given by: 50% assignment sheets, 50% project and project interview. Additional 10% may be given for your active participation in class.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
-
Fach: Vertiefung Informatik (Specialization in Informatics)
(Pflichtfach)
-
1.1 Data Engineering (
2.0h VC / 4.0 ECTS)
- 623.500 Data Engineering (2.0h VC / 4.0 ECTS) Absolvierung im 1. Semester empfohlen
-
1.1 Data Engineering (
2.0h VC / 4.0 ECTS)
-
Fach: Vertiefung Informatik (Specialization in Informatics)
(Pflichtfach)
- Masterstudium Information Management
(SKZ: 922, Version: 19W.1)
-
Fach: Informatics
(Pflichtfach)
-
1.1 Data Engineering (
0.0h VC / 4.0 ECTS)
- 623.500 Data Engineering (2.0h VC / 4.0 ECTS) Absolvierung im 1. Semester empfohlen
-
1.1 Data Engineering (
0.0h VC / 4.0 ECTS)
-
Fach: Informatics
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Sommersemester 2024
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)
-
Wintersemester 2023/24
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)
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Sommersemester 2023
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)
-
Wintersemester 2022/23
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)
-
Sommersemester 2022
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)
-
Wintersemester 2021/22
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)
-
Sommersemester 2021
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 623.501 VC Data Engineering (2.0h / 4.0ECTS)
-
Wintersemester 2019/20
- 623.500 VC Data Engineering (2.0h / 4.0ECTS)