[Philly JUG] Divide, Distribute and Conquer: Stream v. Batch

0680be1c881abcf19219f09f1e8cf140?s=47 Viktor Gamov
September 13, 2017

[Philly JUG] Divide, Distribute and Conquer: Stream v. Batch

Data is flowing everywhere around us, from phones, credit cards, sensor-equipped buildings, vending machines, thermostats, trains, buses,planes, posts to social media, digital pictures and video and so on.Simple data collection is not enough anymore. Most of the current systems do data processing via nightly extract, transform, and load (ETL)operations, which is common in enterprise environments, requires decision makers to wait an entire day (or night) for reports to become available.

But businesses don’t want «Big Data» anymore. They want «Fast Data».What distinguishes a «streaming systems» from the batch systems is that the event stream is unbounded or “infinite” from a system perspective.

Decision-makers need to analyze these streaming events as a whole to make business decisions as new information arrives.In this talk, after a short introduction to common approaches and architectures (lambda, kappa), Viktor will demonstrate how to use open-source steam processing tools (Flink, Kafka Streams, Hazelcast Jet) for stream processing.

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Viktor Gamov

September 13, 2017
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