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Free apache hadoop Tutorial with tipcircle.com

apache hadoop

Learn free online apache hadoop 

The apache hadoop  software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

The project includes these modules:

  • Hadoop Common: The common utilities that support the other Hadoop modules.
  • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.

  • Hadoop YARN: A framework for job scheduling and cluster resource management.
  • Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Prerequisites for apache hadoop tutorial

Before moving forward we assume you have prior experience to following:

  1. Core Java.
  2. Database concepts, and
  3. Any of the Linux operating system flavors.

Who Uses apache hadoop?

A wide variety of companies and organizations use Hadoop for both research and production.

Latest Version of Hadoop as per hadoop website

08 October, 2016: Release 2.6.5 available

A point release for the 2.6 line.

Please see the Hadoop 2.6.5 Release Notes for the list of 79 critical bug fixes and since the previous release 2.6.4.

NOTE:

  • Minimum required Java version increased from Java 7 to Java 8.
  • Apache hadoop now supports Microsoft Azure Data Lake filesystem connector.
  • Intra-datanode balancer is introduced for balancing functionality.
  • The Hadoop shell scripts have been rewritten to fix many long-standing bugs.
  • Support for erasure encoding in HDFS.
  • Default ports of multiple services have been changed.
  • A series of changes have been made to heap management for Hadoop daemons as well as MapReduce tasks.