Big Data Systems (Wintersemester 2020/2021)
Dozent:
Prof. Dr. Tilmann Rabl
(Data Engineering Systems)
,
Dr. Pedro Silva
(Data Engineering Systems)
,
Ilin Tolovski
(Data Engineering Systems)
Website zum Kurs:
https://hpi.de/rabl/teaching/winter-term-2020-21/big-data-systems.html
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.10.-20.11.2020
- Lehrform: Vorlesung / Übung
- Belegungsart: Pflichtmodul
- Lehrsprache: Englisch
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
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- IT-Systems Engineering
- IT-Systems Engineering
Beschreibung
The amount of data that can be generated and stored in academic and industrial projects and applications is increasing rapidly. Big data analytics technologies have established themselves as a solution for big data challenges to the scalability problems of traditional database systems. The vast amounts of new data that is collected, however, usually is not as easily analyzed as curated, structured data in a data warehouse is. Typically, these data are noisy, of varying format and velocity, and need to be analyzed with techniques from statistics and machine learning rather than pure SQL-like aggregations and drill-downs. Moreover, the results of the analyses frequently are models that are used for decision making and prediction. The complete process of big data analysis is described as a pipeline, which includes data recording, cleaning, integration, modeling, and interpretation.
In this lecture, we will discuss big data systems, i.e., infrastructures that are used to handle all steps in typical big data processing pipelines.
Voraussetzungen
See course website.
Literatur
Announced in the course. General info can be found in:
- Principles of Distributed Database Systems, M. Tamer Özsu and Patrick Valduriez, 2011, 978-1441988331
- Distributed Systems, Maarten van Steen and Andrew S. Tanenbaum, 2017, 978-1543057386
- Streaming Systems, T. Akidau, S. Chernyak, R. Lax, 2018, 978-1-491-98387-4
- Designing Data-Intensive Applications, Martin Kleppmann, 2017, 978-1449373320
Lern- und Lehrformen
See course website.
Leistungserfassung
See course website.
Termine
See course website.
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