Open Source Data Processing (Wintersemester 2020/2021)
Dozent:
Prof. Dr. Tilmann Rabl
(Data Engineering Systems)
,
Lawrence Benson
(Data Engineering Systems)
Website zum Kurs:
https://hpi.de/rabl/teaching/winter-term-2020-21/open-source-data-processing.html
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: via email to us by Nov 6th 23:59
- Lehrform: Projektseminar
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
- Maximale Teilnehmerzahl: 9
Studiengänge, Modulgruppen & Module
- SECA: Security Analytics
- HPI-SECA-K Konzepte und Methoden
- SECA: Security Analytics
- HPI-SECA-T Techniken und Werkzeuge
- SECA: Security Analytics
- HPI-SECA-S Spezialisierung
- SCAL: Scalable Data Systems
- HPI-SCAL-K Konzepte und Methode
- SCAL: Scalable Data Systems
- HPI-SCAL-T echniken und Werkzeuge
- SCAL: Scalable Data Systems
- HPI-SCAL-S Spezialisierung
- IT-Systems Engineering
- IT-Systems Engineering
- SAMT: Software Architecture & Modeling Technology
- HPI-SAMT-K Konzepte und Methoden
- SAMT: Software Architecture & Modeling Technology
- HPI-SAMT-T Techniken und Werkzeuge
- SAMT: Software Architecture & Modeling Technology
- HPI-SAMT-S Spezialisierung
Beschreibung
The digital revolution leads to ever increasing amounts of data and a massively increased pace of data generation. In many use cases, archival of the data and later processing is either impossible or uneconomic due to the speed and amount of the data and the quick loss in value of data analysis over time. This has led to the development of stream processing engines (SPE), which can analysis large amounts of data in motion. This leads to two major challenges, the handling of time and potentially endless streams. In this course, we will focus on the SPE Apache Flink and develop code to support its ecosystem.
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