Staying agile in the face of the data deluge

Slides from my talk at Span conference, London on 28 October 2014. http://london-2014.spanconf.io/martin-kleppmann/

As our applications need to process ever more data in ever shorter time, it's difficult to stay sane. The architecture of our applications quickly becomes a monstrosity of different databases, queues and servers held together by string and sellotape. That may work at first, but soon gets ugly. If something goes wrong, it's hard to recover. If features of the application need to change, it's hard to adapt.

Stream processing gives us a route towards building data systems that are scalable, robust, and easy to adapt to changing requirements. In this talk, we will discuss how you can bring sanity to your own application architecture with Apache Samza, an open source framework for distributed stream processing applications.

Apache Samza is used in production at LinkedIn, building upon years of hard-won experience in building large-scale data systems. Even if you're not processing millions of messages per second, like LinkedIn is, you can still pick up useful tips on how to structure your data processing for scale and agility.

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51. References (fun stuff to read)
    
    1.  Jay Kreps: “I ♥︎ Logs.” O'Reilly Media, September 2014. http://shop.oreilly.com/product/0636920034339.do
    
    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.  Martin Kleppmann: “Designing data-intensive applications.” O’Reilly Media, to appear in 2015. http://dataintensive.net
    
    4.  Martin Kleppmann: “Rethinking caching in web apps.” 1 October 2012. http://martin.kleppmann.com/2012/10/01/
    rethinking-caching-in-web-apps.html
    
    5.  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
    
    6.  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/
    
    7.  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
    
    8.  Mahesh Balakrishnan, Dahlia Malkhi, Ted Wobber, et al.: “Tango: Distributed Data Structures over a Shared Log,” at
    24th ACM Symposium on Operating Systems Principles (SOSP), pages 325–340, November 2013. http://
    research.microsoft.com/pubs/199947/Tango.pdf
    
    9.  Roshan Sumbaly, Jay Kreps, and Sam Shah: “The ‘Big Data’ Ecosystem at LinkedIn,” at ACM International Conference
    on Management of Data (SIGMOD), July 2013. http://www.slideshare.net/s_shah/the-big-data-ecosystem-at-
    linkedin-23512853
    
    10.  Apache Samza documentation. http://samza.incubator.apache.org
    
    

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