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Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives (FT Press Analytics)

Master alternative Big Data technologies that can do what Hadoop can’t: real-time analytics and iterative machine learning.   When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn’t well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases s

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Storm Applied: Strategies for real-time event processing

It’s a lot harder to make sense out of data when it’s coming at full speed. Apache Storm’s efficient stream processing capabilities are relied upon by giants like Twitter and Yahoo for swiftly extracting intelligence from their Big Data streams. Fault tolerant guarantees of Storm make it an invaluable and versatile platform in the Big Data landscape. It integrates seamlessly with battle-tested message queuing systems (like Kafka) and NoSQL databases (like Cassandra). Storm is built to run on the

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Cloud computing: Hadoop, Aws and Google

Basics of cloud computing, AWS, Google cloud, Hadoop and more.

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Storm or Spark: Choose your real-time weapon
Storm, a distributed computation framework for event stream processing, began life as a project of BackType, a marketing intelligence company bought by Twitter in 2011. Twitter soon open-sourced the project and put it on GitHub, but Storm ultimately …
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The Internet Of Things' Best-Kept Secret
“Today, part of the product is hardware and part of it is software and part of the software is in the product and part of it is in the cloud and the cloud has a whole new computing architecture. You need a data center … From what Heppelmann calls a …
Read more on Forbes