23 Jul 2014

Real-Time Analytics with Apache Storm

The above video is the recorded webinar session on the topic ‘Real-time Analytics with Apache Storm’, held on 26th July’14. Apache Storm is a  open source, distributed real-time computation system for processing fast, large streams of data. With Storm and MapReduce running together in Hadoop on YARN, a Hadoop cluster can resourcefully process a full range of workloads from...
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The above video is the recorded webinar session on the topic ‘Real-time Analytics with Apache Storm’, held on 26th July’14.

Apache Storm is a  open source, distributed real-time computation system for processing fast, large streams of data. With Storm and MapReduce running together in Hadoop on YARN, a Hadoop cluster can resourcefully process a full range of workloads from real-time to batch.

Real-Time Analytics with Apache Storm – Topics covered in the Presentation:

  • Introduction to Apache Storm & importance of Real-Time processing
  • How Apache Storm overcomes Hadoop’s shortcomings?
  • Real world applications of Apache Storm.
  • What makes Storm ideal for real-time processing?
  • Architecture of a Storm cluster.
  • How Storm and Hadoop fits together?
  • Data ingesting techniques in Storm.
  • Managing Hadoop and Storm cluster with Apache Ambari.

Presentation:

Characteristics of Storm that makes it Ideal for Real-Time Data Processing:
  • Fast – Processes one million 100 byte messages per second per node
  • Scalable – Parallel calculations that run across a cluster of machines
  • Fault-tolerant – Automatic restart when a worker or node dies.
  • Reliable – Guarantees to process each unit of data at least once or exactly once.
  • Easy to Operate – Standard configurations suitable for production from day one.

Feel free to drop us a line for any clarifications. 

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Apache Storm Use Cases

What is Apache Storm all about? 

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