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{
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References (fun stuff to read)
1. Martin Kleppmann: “Designing data-intensive applications.” O’Reilly Media, to appear in 2015. http://dataintensive.net
2. Jay Kreps: “Why local state is a fundamental primitive in stream processing.” 31 July 2014. http://radar.oreilly.com/2014/07/why-local-
state-is-a-fundamental-primitive-in-stream-processing.html
3. Jay Kreps: “I ♥︎ Logs.” O'Reilly Media, September 2014. http://shop.oreilly.com/product/0636920034339.do
4. Nathan Marz and James Warren: “Big Data: Principles and best practices of scalable realtime data systems.” Manning MEAP, to appear
January 2015. http://manning.com/marz/
5. Jakob Homan: “Real time insights into LinkedIn's performance using Apache Samza.” 18 Aug 2014. http://engineering.linkedin.com/samza/
real-time-insights-linkedins-performance-using-apache-samza
6. Martin Kleppmann: “Moving faster with data streams: The rise of Samza at LinkedIn.” 14 July 2014. http://engineering.linkedin.com/stream-
processing/moving-faster-data-streams-rise-samza-linkedin
7. Praveen Neppalli Naga: “Real-time Analytics at Massive Scale with Pinot.” 29 Sept 2014. http://engineering.linkedin.com/analytics/real-
time-analytics-massive-scale-pinot
8. David He: “Monitor and Improve Web Performance Using RUM Data Visualization.” 19 Sept 2014. http://engineering.linkedin.com/
performance/monitor-and-improve-web-performance-using-rum-data-visualization
9. Lili Wu, Sam Shah, Sean Choi, Mitul Tiwari, and Christian Posse: “The Browsemaps: Collaborative Filtering at LinkedIn,” at 6th Workshop
on Recommender Systems and the Social Web, Oct 2014. http://ls13-www.cs.uni-dortmund.de/homepage/rsweb2014/papers/
rsweb2014_submission_3.pdf
10. Shirshanka Das, Chavdar Botev, Kapil Surlaker, et al.: “All Aboard the Databus!,” at ACM Symposium on Cloud Computing (SoCC),
October 2012. http://www.socc2012.org/s18-das.pdf
11. Apache Samza documentation. http://samza.incubator.apache.org
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