Why Learn Hadoop for a Career

664px-Hadoop_logo.svgApache Hadoop is a reliable open source software project that aids in the distributed processing of huge data sets across clusters of computers making use of simple programming models. It is a scalable and distributed computing system that upgrades from one server to thousands of machines, each of them rendering storage and local computation. With Hadoop you do not need to rely on high-end hardware for delivering high availability as the software has high resiliency and has been designed in a way to detect as well as handle failures at the application layer which demonstrates its high degree of tolerance of faults and its ability to deliver first grade services.

The modules of Hadoop have been designed (Create and promote Hadoop course) fundamentally assuming that sudden failure of hardware is inevitable and hence require automatic handling through software by framework. The framework consists of the following modules:

  • Hadoop Common – This module contains libraries as well as utilities that are required by other Hadoop modules
  • Hadoop Distributed File System (HDFS) – It is a distributed file system that offers high throughput access to the application data. Moreover, it stores data on the commodity machines and provides high collective bandwidth across the cluster.
  • Hadoop YARN – It is a cluster resource management platform that is liable for managing of compute resources and then using them for scheduling of users’ applications. In other words, it is a module for job scheduling.
  • Hadoop MapReduce – It is a programming model or framework used for large-scale data processing. It has the ability to understand as well as assign work to the nodes in a cluster. It’s a YARN based system that aids in parallel processing of large sets of data.

Besides, Hadoop has been aided by an ecosystem of Apache projects like Hive, Pig and Zookeeper. It extends Hadoop’s value Hadoop, thereby improving its usability.

The Advantages of Hadoop

To our surprise almost 80% of the data that is captured today is all unstructured like the sensors that gather social media post in its sites, digital videos and pictures, the GPS signals, climate information and several others. All these unstructured data is known as Big Data which includes high volume and variety of information. These require cost effective and a unique form of information processing module that would help in handling these.

Hadoop is the answer and solution to handling these several unstructured information. Apache Hadoop is the registered trademark of the Apache Software Foundation. Created in 2005 by Doug Cutting and Mike Cafarella, the framework was initially made for Nutch search engine project.

In the present scenario, building huge servers is no longer essential for solving large scale issues. Instead, it’s preferable and quite popular to merge several low end machines together as a single functional distributed system. Of the some advantageous spheres of Hadoop are:

  • Hadoop is very scalable and has high fault tolerance
  • It is a platform that offers both Distributed storage & Computational capabilities
  • One of the vital components of Hadoop is HDFS which is the storage component that is optimized
  • The distinctive features of HDFS are scalability and availability that offers data replication and the fault acceptance capability
  • For a specific number of times HDFS can replicate files for a specific number of times that is tolerant towards hardware and software failure. It can also re-replicate the data blocks on the failed nodes automatically
  • The MapReduce framework used by Hadoop is batch based, distributed computing framework.
  • Hadoop makes use of distributed storage & transferring code in place of data which helps in avoiding the costly steps of transmission while working for large data sets
  • It is easier to create any programs with Hadoop as it makes use of MapReduce framework.
  • HDFS makes use of large block sizes that eventually works the best during manipulation of large files like gigabytes, petabytes and others.
  • Its functionality helps in changing the economics as well as the dynamics of large-scale computing

The Key Features that Define Hadoop

The following key features or attributes of Hadoop have made it distinctive and essential software that is now availed by top companies.

  • Accessible: Hadoop functions on cloud computing services and runs on huge clusters of commodity machines that make it easily accessible for different companies or users to use.
  • Robust: As mentioned, Hadoop has been developed to run on commodity hardware and has been created in way to offer support to the recurrent hardware break down that is quiet usual in the day to day usage. It is thus, complimented to handle almost all failures and is highly fault tolerant.  In case of failures when you have lost a node, the system then automatically redirects the work to some other location of the data and goes on processing so that there is no interruption in the operation.
  • Scalable: Hadoop offers the facility to add new modes as per requirement even without changing the data formats. The module scales linearly for handling of larger data by just adding additional nodes to the cluster.
  • Simple and easy: Hadoop permits its users to rapidly write effective parallel code. This easy and hassle free accessibility and simple process has offered it a distinct position and the choice over having to write and run large distributed programs.
  • Flexible– Hadoop has the ability to accept all types of data whether structured or unstructured and it is schema-less. The data is absorbed from any number of sources.
  • Cost effective– Hadoop has brought parallel computing option to commodity servers. It is possible to run varied applications on systems having thousands of nodes which include thousands of terabyte. This has resulted in the sizeable reduction in the cost of storage of per terabyte. This makes it an affordable option to model all the data.

The Range of Hadoop Users

Varied types of companies and organizations make use of Hadoop for both research work and production. As per the statistical report on 2008, Yahoo! Inc was the world’s largest Hadoop production application.

On 2010 Facebook was the largest Hadoop cluster in the world, boasting of 21 PB of storage. As per 2013 adoption of Hadoop has grown widespread. More than half of the Fortune 50 is said to be using Hadoop. These companies use Hadoop for mainly applications that involve search engines and advertising. The best preferred operating systems for Hadoop are Windows and Linux but it can also work with BSD and OS X.

With several advantages and an essential for daily operations, an understanding of Hadoop might prove to be beneficial and important for your career growth.

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