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Hadoop Tutorial – Accessing streaming data with Apache Storm


Apache Storm is in charge for analyzing streaming data in Hadoop. Storm is extremely powerful when analyzing streaming data and is capable of working near real-time. Storm was initially developed by Twitter to power their streaming API. At present, Storm is capable of processing 1 million tuples per node and second. The nice thing about Storm is that it scales linearly.

The Storm architecture is similar to other Hadoop projects. However, Storm comes with different challenges. First, there is Nimbus. Nimbus is the controller for Storm, which is similar to the JobTracker in Hadoop. Apache Storm also utilizes ZooKeeper. The Supervisor is on each instance and takes care of the tuples once they come in. The following figure shows this.

Storm Topology
Storm Topology

Major concepts in Apache Storm are 4 elements: streams, spouts, bolts and topologies.

Storm Tuples
Storm Tuples

Streams are an unbound sequence of Tuples, a Spout is a source of streams, Bolts process input streams and create new output streams and a topology is a network of Bolts and Spouts.

I lead a team of Senior Experts in Data & Data Science as Head of Data & Analytics and AI at A1 Telekom Austria Group. I also teach this topic at various universities and frequently speak at various Conferences. In 2010 I wrote a book about Cloud Computing, which is often used at German & Austrian Universities. In my home country (Austria) I am part of several organisations on Big Data & Data Science.

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