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  4. SpringOne2GX 2014 Replay: Scalable Big Data stream processing with Storm and Groovy

SpringOne2GX 2014 Replay: Scalable Big Data stream processing with Storm and Groovy

Pieter Humphrey user avatar by
Pieter Humphrey
CORE ·
Feb. 20, 15 · Interview
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Speaker: Eugene Dvorkin

More Groovy Track

Slides: http://www.slideshare.net/SpringCentral/storm-twtterwebmd

With advances in distributed computing and creation of frameworks like Storm and Spark, building real-time, fault-tolerant, and scalable solutions to process huge volume of data in real-time has become easy. Storm is one of the most popular framework to develop real-time analytics and event processing applications. Storm enables to tackle real-time Big Data challenges the same way Hadoop enables batch processing of Big Data. One of the use cases of Storm is processing feeds from social networks in real-time. Social networks like Twitter, Facebook, LinkedIn, Google+ became part of our life. By analyzing social networks, companies can process important information about their product, services, and provide real-time information to customers. In this talk, Eugene will provide introduction to Storm framework, explain how to build real-time applications on top of Storm with Groovy, how to process data from Twitter in real-time and architectural decision behind WebMD MedPulse mobile application.

Big data Stream processing Data stream Groovy (programming language)

Published at DZone with permission of Pieter Humphrey, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

Related

  • Four Ways To Ingest Streaming Data in AWS Using Kinesis
  • Superior Stream Processing: Apache Flink's Impact on Data Lakehouse Architecture
  • Lambda Architecture: A Powerful Approach to Data Engineering
  • Microsoft Azure Event Hubs

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