Instructors
Prof. Dr. Tilmann Rabl
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.
Announcements
- Course management will be done using the HPI Moodle
- 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 lecture will take place Tuesdays (HS 3) and Thursdays (HS 2) at 11:00 AM at Campus I.
| Date | Topic |
|---|
| TU 15.10. | Introduction |
| TH 17.10. | cancelled - Retreat Research School |
| TU 22.10. | DBS Recap |
| TH 24.10. | DBS Recap II |
| TU 29.10. | cancelled - 20 Years HPI Celebration |
| TH 31.10. | Reformation Day |
| TU 05.11. | Big Data Stack |
| TH 07.11. | Solution Quiz I |
| TU 12.11. | Benchmarking & Measurement |
| TH 14.11. | Cloud/Container |
| TU 19.11. | Modern Hardware |
| TH 21.11. | File Systems |
| TU 26.11. | Map/Reduce |
| TH 28.11. | Solution Quiz II |
| TU 03.12. | KV-Stores |
| TH 05.12. | Consistency |
| TU 10.12. | Stream Processing |
| TH 12.12. | Windows |
| TU 17.12. | Tables and State |
| TH 19.12. | Solution Quiz III |
| TU 07.01. | Stream Optimizations |
| TH 09.01. | Solution Quiz IV |
| TU 14.01. | ML Systems |
| TH 16.01. | ML Exec Strategies |
| TU 21.01. | ML Lifecycle |
| TH 23.01. | Graph Processing |
| TU 28.01. | Graph Processing II |
| TH 30.01. | Solution Quiz V |
| TU 04.02. | Q&A |
| TH 06.02. | Final Exam at 11 AM in HS 1 |
Grading
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)
