Hadoop Tutorial provides a introduction into working with big data in Hadoop via the Hortonworks Sandbox, HCatalog, Pig and Hive. Learn How to handle Big Data

The Importance of Having Hadoop Tutorial for Supporting Your Company


Hadoop can be called as a free Java software framework that supports data intensive distributed applications as we understand it. Hadoop distribute large amounts of work across a set of machines it is efficiently designed to do it.It is a large-scale distributed batch processing infrastructure. Its true power lies in its ability to scale to hundreds or thousands of computers, each with several processor cores, while it can be used on a single machine.The important aspect to be considered regarding Hadoop is the fact that renowned organizations around the world are moving their attention toward this open source software framework and are attentively looking out for individuals who poses the required knowledge to make use of Hadoop.

Hadoop ecosystem is a massive innovation which is designed to process web scale data of hundreds of gigabytes or terabytes or petabytes. To make this possible, Hadoop uses a distributed file system which breaks up input data and sends fraction of original data to several machines. This results into processing the data effectively, in parallel using all the machines present in the network. This also helps in bringing the output more efficiently. But this system faces lot of challenges in doing so. It is not at all an easy task to perform large scale data. Handling such a huge amount of data require some handling parts which can ease the process and can distribute the data in multiple machines in parallel.

Considering the fact that eighty percent of the data which an organization has is in an unstructured form, it is important for an organization to ensure that it formats the data in a structured manner such that make subsequent analysis and data mining suitable. Thus, taking under consideration the importance placed upon the big data management, a Hadoop tutorial will enlighten you about the dynamics of this open source software platform and define in detail how does it help in making useful analytics out of the present data.


The importance of taking a Hadoop tutorial is that it will run you through the complete dynamics of the software and teach you the skills which are necessary in you becoming a Hadoop expert. It is important that you equip yourself with this growing trend and stay afoot of the competitors. However, before opting for a Hadoop tutorial it is important that you closely go through the content of the course and ensure that it is thorough enough to impart the actual scope of this trending open source software framework.In a distributed environment, however, partial failures are very common and are well accepted. Usually, the network faces such problems if the switches and the routers break down. Due to network congestion, the data doesn't reach the destination on time. Individual compute nodes may overheat, crash, run out of memory or experience hard drive failures. In such a case the data may get corrupted, or maliciously or improperly transmitted, which is quite a risk.If you will use Hadoop ecosystem, most of your difficulties would get sorted out.

How does master slave architecture in the Hadoop?

The MapReduce framework consists of a single master JobTracker and multiple slaves, each cluster-node will have one TaskskTracker.
The master is responsible for scheduling the jobs' component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves execute the tasks as directed by the master.

What is MapReduce ?

 Map reduce is an algorithm or concept to process Huge amount of data in a faster way. As per its name you can divide it Map and Reduce.
· The main MapReduce job usually splits the input data-set into independent chunks. (Big data sets in the multiple small datasets)
· MapTask: will process these chunks in a completely parallel manner (One node can process one or more chunks).
· The framework sorts the outputs of the maps.
· Reduce Task : And the above output will be the input for the reducetasks, produces the final result.

Your business logic would be written in the MappedTask and ReducedTask.
Typically both the input and the output of the job are stored in a file-system (Not database). The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks. 

What is MapReduce ?



Map reduce is an algorithm or concept to process large amount of data in a faster way. As per its name you can divide it Map and Reduce.

The main MapReduce job usually splits the input data-set into independent chunks. (it is called, Big data sets in the multiple small datasets)

MapTask: will process these chunks in a completely parallel manner (One node can process one or more chunks).

The framework sorts the outputs of the maps.

Reduce Task : And the above output will be the input for the reducetasks, produces the final result.

Your business logic would be written in the MappedTask and ReducedTask. Typically both the input and the output of the job are stored in a file-system (Not database). The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.  

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