Hadoop online training

Big Data Training is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it involves many areas of business and technology.

Hadoop Course is an Apache open source framework written in Java that allows distributed processing of large datasets across clusters of computers using simple programming models. Its framework application works in an environment that provides distributed storage and computation across clusters of computers.

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What is Hadoop Training

Hadoop Course is an Apache open source framework written in Java that allows distributed processing of large datasets across clusters of computers using simple programming models. Its framework application works in an an environment that provides distributed storage and computation across clusters of computers. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage.

Hadoop Architecture:

Hadoop has two major layers namely:

(a)Processing/Computation layer (MapReduce), and

(b)Storage layer (Hadoop Distributed File System).

a) MapReduce

MapReduce is a parallel programming model for writing distributed applications devised at Google for efficient processing of large amounts of data (multi terabyte datasets), on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. The MapReduce program runs on Hadoop which is an Apache open-source framework.

b) Hadoop Distributed File System

The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. It is highly fault-tolerant and is designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications having large datasets.

How Does Hadoop Work?

Hadoop runs code across a cluster of computers. This process includes the following core tasks that Hadoop performs:

• Data is initially divided into directories and files. Files are divided into uniformly sized blocks of 128M and 64M (preferably 128M).

• These files are then distributed across various cluster nodes for further processing.
• HDFS, being on top of the local file system, supervises the processing.
• Blocks are replicated for handling hardware failure.
• Checking that the code was executed successfully.

Advantages of Hadoop

Hadoop framework allows the user to quickly write and test distributed

systems. It is efficient, and it automatically distributes the data and work

on the machines and in turn, utilizes the underlying parallelism of the CPU cores.

• Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather it library itself has been designed to detect and handle failures at the application layer. Servers can be added or removed from the cluster dynamically and
• Hadoop continues to operate without interruption.
• Another big advantage of Hadoop is that apart from being open source, it is compatible with all the platforms since it is Java based.