Distributed Data Analytics (Wintersemester 2017/2018)

Lecturer: Dr. Thorsten Papenbrock (Information Systems) , Prof. Dr. Felix Naumann (Information Systems)
Course Website: https://hpi.de/naumann/teaching/teaching/ws-1718/distributed-data-analytics-vl-master.html

General Information

• Weekly Hours: 2
• Credits: 3
• Graded: yes
• Enrolment Deadline: 27.10.2017
• Teaching Form: v
• Enrolment Type: Compulsory Elective Module

Programs, Module Groups & Modules

Description

The free lunch is over! Computer systems up until the turn of the century became constantly faster without any particular effort simply because the hardware they were running on increased its clock speed with every new release. This trend has changed and today's CPUs stall at around 3 GHz. The size of modern computer systems in terms of contained transistors (cores in CPUs/GPUs, CPUs/GPUs in compute nodes, compute nodes in clusters), however, still increases constantly. This caused a paradigm shift in writing software: instead of optimizing code for a single thread, applications now need to solve their given tasks in parallel in order to expect noticeable performance gains. Distributed computing, i.e., the distribution of work on (potentially) physically isolated compute nodes is the most extreme method of parallelization.

Big Data Analytics is a multi-million dollar market that grows constantly! Data and the ability to control and use it is the most valuable ability of today's computer systems. Because data volumes grow so rapidly and with them the complexity of questions they should answer, data analytics, i.e., the ability of extracting any kind of information from the data becomes increasingly difficult. As data analytics systems cannot hope for their hardware getting any faster to cope with performance problems, they need to embrace new software trends that let their performance scale with the still increasing number of processing elements.

In this lecture, we take a look a various technologies involved in building distributed, data-intensive systems. We discuss theoretical concepts (data models, encoding, replication, ...) as well as some of their practical implementations (Akka, MapReduce, Spark, ...). Since workload distribution is a concept which is useful for many applications, we focus in particular on data analytics.

The topics of the lecture include:

1. Foundations
2. Data Models and Query Languages
3. Storage and Retrieval
4. Encoding and Evolution
5. Replication
6. Partitioning
7. Transactions
8. Distributed Systems
9. Consistency
10. Batch Processing
11. Stream Processing

Requirements

• Interest in data, parallelization, and analytics
• A little background in database systems
  (e.g. DBS I lecture)
• Very basic knowledge of the Java programming language
  (because most hands-ons and exercises are in Java)

Literature

Course book:

• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017, 978-1449373320

Further reading:

• Distributed Systems, Maarten van Steen and Andrew S. Tanenbaum, 2017, 978-1543057386
• Web-Scale Data Management for the Cloud, Wolfgang Lehner and Kai-Uwe Sattler, 2013, 1489997717
• Introduction to Parallel Computing, Zbigniew J. Czech, 2017, 978-1107174399
• Principles of Distributed Database Systems, M. Tamer Özsu and Patrick Valduriez, 2011, 978-1441988331
• Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services, Brendan Burns, 2017, 978-1491983645
• Spark: Big Data Cluster Computing in Production, Ilya Ganelin and Ema Orhian and Kai Sasaki and Brennon York, 2016, 978-1119254010
• Reactive Messaging Patterns with the Actor Model, Vaughn Vernon, 2015, 978-0133846836
• Mining Massive Datasets, Jure Leskovec and Anand Rajaraman and Jeffrey David Ullman, 2014, 978-1107077232
• Algorithmische Geometrie, Rolf Klein, 2005, 978-3540209560

Learning

• lecture
• hands-ons
• exercises

Examination

• A (written) exam determines the final grade
• Prerequisite to the exam is ...
  • attending the lectures
  • participation in exercises
  • completion of exercise tasks

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