Hadoop: Applications and Implementations

Hadoop is a software framework used for processing huge set of data across many servers at a time. As the decades passes, the scope of Hadoop is increasing day by day. It is providing feasibility in business development that has never ending market demands. Due to the emergence of Hadoop technology, large organisations are able to run their applications in efficient manner. Hadoop is feasible for both small and large organisations. Hadoop has shown a big effect in producing the analytical report of data processing which was not in earlier stages of technology development. Hence it has revolutionized the technology and significant results.

 

Advantages

It has high recommendation in business development as it deals with huge set of data and also provides solutions to related issues. It is cost-effective and advantageous in application development.  Hadoop is highly scalable, as it is liable to distribute large amount of data among different servers simultaneously. Companies are able to run applications on thousands of node in terabytes of data. It eradicates the issues that are relatively accompanied with relational database management system (RDBMS). The architecture of Hadoop is quiet affordable to store large amount of data as it offers good amount of storage and computing capabilities. To acquire valuable data, Hadoop enables businesses for easy access of source data. Even there is concept of simultaneous data processing and Hadoop is able to provide faster data processing and that too in small fraction of time. The main advantageous factor which makes Hadoop unique is its fault tolerance capabilities. As there is chances of failure while processing data, so it keeps track of another copy for future correspondence. It poses the growth of unstructured data of any sizes of organisations.

Applications

Hadoop is serving large variety of purposes such as log processing, data warehousing, system recommendations, fraud detection and so many other applications are getting served through it. Basically Hadoop serves two important applications: 1. HDFS (Hadoop Development File System) and 2. Hadoop Map.

HDFS is designed to serve large cluster of servers which are suitable for distributed storage and processing of data. It is also a status checker of server and providing the authentication and permission to access files that contain data. Namenode and Datanode are the commodity hardware which performs actions on GNU/Linux operating system. Namenode is used to manage the system file namespace while Datanode is used to create, delete and replicate the instructions of namenode. The file system is divided into different segments known as Blocks that store individual data set. HDFS is actually responsible for fault detection and recovery of data.

Hadoop Map helps in scheduling jobs based on priorities and also takes care of data processing. It shows the parallelism. It is the programming model for Hadoop development which is associated with the implementations of generating and processing huge data simultaneously. Its library functions are loaded with different sets of programming languages.

Hadoop has great market influence as it is solution focused and the growing advancements in technology is making it remarkable and sustainable.

hdfs_architecture

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