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Building a Distributed Data Pipeline

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Spark, Akka, MLlib, Kafka, Spray
Presentation & demo for http://www.daysofcode.nl/ @daysofcode

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Building a Distributed Data Pipeline

  1. 1. BUILDING A DISTRIBUTED MACHINE LEARNING AT SCALE
  2. 2. BACKGROUND DATA ▸Data is everywhere ▸Data, unapplied, is useless ▸How can we turn high volume & velocity data into value?
  3. 3. BACKGROUND PIPELINE ▸Process the data continuously ▸Apply several processing steps COLLECT MODEL DEPLOY INTEGRA TE
  4. 4. SOLUTION ANALYSE THE STOCK MARKET YAHOO.C OM YAHOO.C OM (PREFETCHED) COLLECTO R MESSAGE BROKER STREAMIN G STORAGE MODEL MACHINE LEARNING MLlibWEBSERVI CE USER / CLIENTS
  5. 5. DEMO DEMO (FINGERS CROSSED)
  6. 6. DONE QUESTIONS? ▸?

Spark, Akka, MLlib, Kafka, Spray Presentation & demo for http://www.daysofcode.nl/ @daysofcode

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