HADOOP TRAINING IN HYDERABAD

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What is Hadoop?

Hadoop (the ApacheTM Hadoop®) is an open-source framework that was 

designed to make it easier to work with big data. It is a multi-clustered 

computing, process-based, and managed resource that is involved. "Hadoop" 

commonly refers to the core technology which consists of the core 

components described below, but is also frequently used in reference to the 

entire ecosystem of supporting technologies and applications.

"Hadoop" also is often used interchangeably with "big data," but it should 

not be. Hadoop is a framework for working with big data. It is part of the big 

data ecosystem, which consists of much more than Hadoop itself.

Hadoop is a distributed framework that makes it easier to process large data 

sets that reside in clusters of computers. Because it is a framework, Hadoop 

is not a single technology or product. Instead, Hadoop is made up of four 

core modules that are supported by a broad ecosystem of supporting 

technologies and products. The modules are:

 Hadoop Introduction

Hadoop Distributed File System (HDFSTM) - Provides access to application 

data. Hadoop can also work with other file systems, including FTP, Amazon 

S3 and Windows Azure Storage Blobs (WASB), among others.

Hadoop YARN - Provides the framework to schedule the jobs and manage 

resources across the cluster

Hadoop MapReduce - A YARN-based parallel processing system for large data 

sets.

Hadoop Common - A set of utilities that supports the three other core 

modules.

Some of the well-known Hadoop ecosystem components include Oozie, 

Spark, Sqoop, Hive and Pig.

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What Hadoop is not

In this tutorial for beginners, it 's helpful to understand what Hadoop is by 

knowing what it is not.

Hadoop is not "big data" - the terms are sometimes used interchangeably, 

but they should not be. Hadoop is a framework for processing big data.

Hadoop is not an operating system (OS) or packaged software application.

Hadoop is not a brand name. It is an open source project, although "Hadoop" 

may be used as part of registered brand names.

What's with the name?

Hadoop was originally developed by Doug Cutting and Mike Cafarella. 

According to lore, Cutting named the software after his son's toy elephant. 

An image of an elephant remains the symbol for Hadoop.

Core elements of Hadoop

There are four basic elements to Hadoop: HDFS; MapReduce; YARN; 

Common.

HDFS

Hadoop works across clusters of commodity servers. Therefore there is a 

need to coordinate across the hardware. The Hadoop Distributed File System 

is the primary means for doing so and is the heart of Hadoop technology. 

HDFS manages how the files are divided and stored across the cluster. Data is 

divided into blocks, and each server in the cluster contains data from 

different blocks. There is also some built-in redundancy.

YARN

Oozie, tuple and Sqoop are common, of course it's not that simple. YARN is 

an acronym for Yet Another Resource Negotiator. As the full name implies, 

YARN helps manage resources across the cluster environment. It breaks up 

resource management, job scheduling, and job management tasks into 

separate daemons. Key elements include the ResourceManager (RM), the 

NodeManager (NM) and the ApplicationMaster (AM).

Think of the ResourceManager as the final authority for all applications in the 

system. The NodeManagers are agents that manage resources (e.g. CPU, 

memory, network, etc.) on each machine. NodeManagers report to the 

ResourceManager. ApplicationMaster serves as a library that sits between the 

two. It negotiates resources with ResourceManager and works with one or 

more NodeManagers to execute tasks for which resources were allocated.

MapReduce

MapReduce provides a method for parallel processing on distributed servers. 

Before processing data, MapReduce converts that large blocks into smaller 

data sets called tuples. Tuples, in turn, can be organized and processed 

according to their key-value pairs. When MapReduce processing is complete, 

HDFS takes over and manages storage and distribution for the output. The 

shorthand version of MapReduce is that it breaks big data blocks in smaller 

chunks that are easier to work with.

The "Map" in MapReduce refers to the Map Tasks function. Map Tasks is the 

process of formatting data into key-value pairs and assigning them to nodes 

for the "Reduce" function, which is executed by Reduce Tasks, where data is 

reduced to tuples.