Apache Storm training in pune

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    1 on 1 sessions

 


    Free cloud access

 


    24*7 call support

 

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    VM for practice

 


    Free cloud storage

 


    IZO 147

 

 

 

 

    Video Lectures

 


    Complete modules

 


    Multiple batches

 

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Syllabus


Introduction

  • Introduction Apache Storm
  • Course Objectives
  • Course Overview
  • Target Audience
  • Prerequesites for the course

Big data overview

  • Objectives 
  • Bigdata
  • 3 vs of Bigdata
  • Data Volume
  • Data size
  • Data evolution
  • Features of Bigdata
  • Industry example
  • Bigdata analysis
  • Technology Comparison
  • HDFC
  • MapReduce
  • Real time Bigdata tools

Introduction to Storm

  • Itroduction to storm
  • Apache storm
  • Uses of Storm
  • What is a Stream
  • Industry Use cases of Storm
  • STORM Data model
  • STORM Architecture
  • STORM Processes
  • STORM Spout/Bolt/Topology

Installation & Configuration

  • Objectives
  • Storm version
  • OS Selection
  • Machine selection
  • Install storm Demo
  • setting of Multi-node storm cluster

Storm advance concept

  • Objectives
  • Types of Spout
  • Stucture of Spout/Bolt
  • Stream grouping
  • Relaible processing in storm
  • Anchoring
  • Data Ingestion in storm

Storm interface

  • Java Interface to Storm
  • Spout interface
  • IReachSpout Method
  • BaseReachSpout method
  • OutputFieldDeclailer interface
  • Bolt interface
  • Irichbolt method
  • BaseRichBolt Method
  • IBasicBolt method
  • StormSubmitter method
  • Kafka interface to Storm
  • Kafka-Storm-Classandra

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Apache Storm Training In Pune

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language, and is a lot of fun to use! Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Storm integrates with the queueing and database technologies you already use. A Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed. Read more in the tutorial.

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20th Oct - Demo Session - P.Saudagar - Hadoop : 10.00 am | Informatica : 11.00 am | Digital Marketing : 10.00 am | Nal Stop - Hadoop : 1.30 pm | Data Science : 10.45 am | Python : 2.00 pm | Kharadi - Tableau : 11.00 am | Data Science : 3.00 pm | Python : 12.00 pm