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Hadoop is an open-source platform and a software foundation project regarding distributed and scalable computing. It is a reliable and quick analysis associated with every structured as well as unstructured data.

The Hadoop software library enables the distributed processing of huge data across clusters of computers through simple programming models. This software is designed to scale up from a single server to several machines, each providing storage, and local computation. Instead of depending on the hardware for delivering top-availability, it is designed to detect as well as handle failures at an application layer.

What are the Modules of Hadoop?

Hadoop has four modules that are stated in our help for assignment on Apache hadoop topics as follows:

1. HDFS or Hadoop Distributed File System- It is a distributed file system, which runs on low-end or standard hardware. It offers better data throughput compared to traditional file system besides high fault tolerance as well as native support of the huge datasets.
2. MapReduce- It is a framework that aids programs do a parallel data computation. The map task gets input data and then converts it to a dataset, which can be computed in the main value pairs. The map’s output is consumed by minimizing tasks to aggregate output and getting the desired result.
3. YARN or Yet Another Resource Negotiator- It manages as well as monitors resource usage and cluster nodes. It schedules tasks and jobs.
4. Hadoop Common- It provides Java libraries, which can be used in all modules.

The Importance of Hadoop

Hadoop is considered important and the reasons are highlighted when you buy assignment case study help on Hadoop as follows:

• It has the ability to store as well as process huge amounts of data quickly. With the data volumes and its varieties increasing constantly, mainly from the IoT or the Internet of Things and social media, it is a major consideration.
• The distributed computing model of Hadoop can process big data very fast. If you use more computing nodes, you will have more processing power.
• Unlike the conventional relational databases, you do not need to preprocess data before you store it. You may store as much data you want and decide to use it subsequently. It includes unstructured data such as images, text, and videos.
• Application and data processing are offered protection against the hardware failure. When a node goes down, the jobs are redirected automatically to the other nodes for ensuring distributed computing is a success. Many copies of the data are automatically stored.
• You can grow your system easily with Hadoop to handle data by adding nodes. You need little administration.
• Its open-source framework is free and it uses commodity hardware for storing huge quantities of data.

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