CS-E4640 - Big Data Platforms D, 13.01.2021-07.04.2021

Overview

We will study stream processing for big data and its relation to big data platforms.

• Slides of the lecture
• Common features in existing frameworks
  

Reading List

•  Martin Hirzel, Guillaume Baudart, Angela Bonifati, Emanuele Della Valle, Sherif Sakr, and Akrivi Akrivi Vlachou. 2018. Stream Processing Languages in the Big Data Era. SIGMOD Rec. 47, 2 (December 2018), 29-40. DOI: https://doi.org/10.1145/3299887.3299892
•  Tyler Akidau, Streaming 101: The world beyond batch A high-level tour of modern data-processing concepts. August 5, 2015. [Link](https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-101)
• Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael Fernández-Moctezuma, Reuven Lax, Sam McVeety, Daniel Mills, Frances Perry, Eric Schmidt, Sam Whittle: The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing. Proc. VLDB Endow. 8(12): 1792-1803 (2015), http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf
  
•   Ellen Friedman and Kostas Tzoumas, Introduction to Apache Flink, [Link](https://mapr.com/introduction-to-apache-flink/assets/introduction-to-apache-flink.pdf)
•  Matei Zaharia, Tathagata Das, Haoyuan Li, Scott Shenker, and Ion Stoica. 2012. Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters. In Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing (HotCloud'12). USENIX Association, Berkeley, CA, USA, 10-10. [Link](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)