Hadoop Online Training ​

What is Hadoop Online Training

By taking our Big Data Hadoop training course, you will gain knowledge of the four Apache Foundation MapReduce concepts, Hadoop Distributed File System (HDFS), as well as other software components that run alongside Hadoop, such as Yet Another Resource Negotiator (YARN), Hadoop Common, Ambari, Cassandra, Flume, HBase, HCatalog, Hive, Oozie, Pig, Solr, Spark, Sqoop, and Zookeeper. Our Hadoop courses will go over all of these ideas while using real-world examples from industries including finance, social media, healthcare, retail, telecommunications, manufacturing, and the internet of things.



100% Job Support
100% Practical Training
Live Project Experience


  • Understand Big Data
  • Traditional systems’ limitations
  • EDW vs. Big Data
  • Cloud vs. Big Data
  • HPC vs. Big Data
  • Why Hadoop (Introduction) 
  • Distributed file system concepts
  • Alternatives to Hadoop Ecosystem
  • Scenarios
  • distributed file system with Hadoop (HDFS)
  • core daemons for Hadoop
  • MapReduce Framework for Hadoop
  • Handshaking of the fundamentals
  • logic for job scheduling
  • in-depth discussion of sorting, shuffling, and reducers
  • File semantics (read and write)
  • operations in bulk
  • Practice & Assignments
  • Apache Hive
  • Hive Hands-on
  • Apache Pig
  • Pig Hands-on
  • Apache HBase
  • HBase hands-on
  • Apache Scoop
  • Scoop hands-on
  • Apache Flume
  • Apache Hue
  • Apache Mahout
  • Apache Oozie
  • Apache Ambari
  • Hands-on & Assignments
  • Hive with Pig
  • Hive with HBase
  • Pig with HBase
  • Scoop with Hive
  • Scoop with HBase
  • Python integration
  • Hands-on & Assignments
  • Challenges in Hadoop 1.x
  • Name node high availability
  • HDFS Federation
  • Resource Manager – YARN
  • Node Manager
  • App Master
  • Hands-on & Assignments
  • Refresh on Unix/Linux commands
  • Planning for Hadoop cluster
  • Hardware and Software considerations
  • putting a solo system together
  • establishing a phoney distributed system
  • Hadoop daemon addresses and ports
  • Controlling Jobs
  • Several Hadoop Scheduler Types
  • setting up the scheduler and running a job
  • Monitoring and troubleshooting for clusters

The application is free and takes only 5 minutes to complete.

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