Prof. Dr. Tilmann Rabl, Prof. Dr. Felix Naumann
The Lecture Series on Practical Data Engineering will feature presentations by distinguished speakers from industry and academia on the topic of data engineering. We will cover topics on the application of data engineering in industrial setups as well as system level advancements in data engineering research.
The agenda will be finalized until the start of the semester.
|15.10.||Introduction||Tilmann Rabl, Felix Naumann|
|22.10.||Recommending Tourist Activities - Data Science Challenges And The Needs for Data Pipelines||Maximilian Jenders, GetYourGuide|
|29.10.||20 Years HPI Conference - no lecture|| |
|05.11.||Towards Interactive Data Analytics||Carsten Binnig, TU Darmstadt|
|12.11.||File Metadata Management at Snowflake||Max Heimel, Martin Hentschel, Snowflake|
|19.11.||Introduction to Apache Flink||Arvid Heise, Ververica|
|26.11.||Deep Earth Query: Advances in Satellite Image Indexing from Massive Archives||Begüm Demir, TU Berlin|
|03.12.||Scale-In, then Scale-Out - New Database Scaling Options with FPGAs and Hardware Acceleration||Yana Krasteva, Thomas Richter, Swarm64|
|10.12.||Dimensions of Hardware Parallelism and Exploiting Them for Data-Intensive Systems||Pinar Tözün, IT University of Copenhagen|
|10.12.||SAP HANA Software Development Process||Alexander Böhm, SAP|
|07.01.||A Programming Language and Compiler View on Data Management and Machine Learning Systems||Tiark Rompf, Purdue University|
|14.01.||Applications of AI in the Credit Information Business||Gjergji Kasneci, Uni Tübingen|
|21.01.||Ethical Data Engineering||Birgit Beck, TU Berlin|
|28.01.||Data Cleaning||Ziawasch Abedjan, TU Berlin|
|04.02.||Poster Presentations||Tilmann Rabl, Felix Naumann|
Time and Location: Tuesdays 5:00 - 6:30 pm, HS 2
The course is graded and has 3 ECTS.
You have to write a summary for one lecture. Depending on the number of participants, this will be done in groups. Each lecture will be assigned to one person or group.
All summaries will be published on the course website. The summary should give a quick and engaging overview of the talk (10-15 min reading time). We will ask the presenters to review your summary and help you with questions.
Individual Poster Project
You have to prepare an A1 poster, with one of the following contents. You will be able to present your poster at the final session.
For Bachelor Students
Based on one or more of the lectures, you can either prepare a technology landscape or explain a data science or data engineering process. Both should be as detailed as possible.
For Master Students
Extending technology or methodology presented in one or multiple of the lectures, you should prepare a research project proposal. The poster should highlight goal, problem, and solution of your proposal and the connection to the lecture.
- 50% Lecture summary
- 50% Individual poster project