Big Data Systeme (Wintersemester 2019/2020)

Lecturer: Prof. Dr. Tilmann Rabl (Data Engineering Systems)
Course Website: https://hpi.de/rabl/teaching/next-semester/big-data-systems.html

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.10.-30.10.2019
• Teaching Form: Vorlesung / Übung
• Enrolment Type: Compulsory Module
• Course Language: English

Programs, Module Groups & Modules

Description

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.

Requirements

See course website.

Literature

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

Learning

See course website.

Examination

The grade will determined in exercises and an exam. The time and location of the exam will be anounced at least 6 weeks in advance. The prerequisite for admission to the exam is the successful completion of the exercises. In case of low participation, the exam might be replaced by an oral examination.

The grade breakdown is as follows:

5 Exercise sheets (20% of total points)

• 1 self assessment (unmarked)
• 4 graded exercises (5 points each)

Programming Exercises (15% of total points)

• November (7%)
• January (8%)

Exam (65% of total points)

Dates

See course website.

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