Patterns for real-time stream processing
A talk at
Budapest, Hungary, 30 Oct 2015
You have some streams of data, such as user activity on a website, or sensor readings from devices.
Now you want to process the data and make it useful with low latency: for example, generating
real-time recommendations, detecting abuse, filtering spam or predicting demand. And you want it to
Perhaps you’ve heard of distributed stream processing frameworks such as Samza, Storm or Spark
Streaming, which may do what you want, but you’re not sure how to use them most effectively.
This talk will introduce some common design patterns for working with high-volume, real-time data
streams. We will look at things like joining, enriching, filtering and aggregating streaming data,
and we’ll explore how you might break down an application into streaming operators that do what you
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“The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale,
Unbounded, Out-of-Order Data Processing,”
Proceedings of the VLDB Endowment, volume 8, number 12, pages 1792–1803, August 2015.
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14 July 2014.
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31 July 2014.
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