Hasso-Plattner-Institut
Prof. Dr. Tilmann Rabl
 

Instructors

Prof. Dr. Tilmann Rabl, Nils Straßenburg, Ricardo Salazar

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 analyzes 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,

In this lecture, we will discuss big data systems, ie, infrastructures that are used to handle all steps in typical big data processing pipelines.

Literature

  • Principles of Distributed Database Systems , M. Tamer Özsu and P. 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 , M. Kleppmann, 2017, 978-1449373320

Announcements

  • Course management will be done using the HPI Moodle
  • The lectures will be held on-site at HPI
  • Non-HPI participants : please send us an email to get access to the Moodle
  • All lectures are recorded and available in Tele-Task

Schedule (tentative) 

The lectures will be held on Tuesdays (L-03) and Thursdays (L-03) at 11:00 o'clock.

DateTopic
18.10.2022Introduction
20.10.2022TBD
25.10.2022 
27.10.2022 
01.11.2022 
03.11.2022 
08.11.2022 
10.11.2022 
15.11.2022 
17.11.2022 
22.11.2022 
24.11.2022 
29.11.2022 
01.12.2022 
06.12.2022 
08.12.2022 
13.12.2022 
15.12.2022 
20.12.2022 
22.12.2022 
27.12.2022 
29.12.2022 
03.01.2022 
05.01.2022 
10.01.2022 
12.01.2022 
17.01.2022 
19.01.2022 
24.01.2022 
26.01.2022 
31.01.2022 
02.02.2022 
07.02.2022 
09.02.2022 

Grading

The grade will be 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:

  • 3 graded programming exercies sheets (> 50% in each exercise for exam eligibility)
  • Final Exam (100% of the grade)