Hadoop: The Ultimate Solution for Data Storage and Processing
There has been immense advancement in technology in few decades. A number of languages and applications have been developed which is completely boon for us. The researches and developers have done exceptionally well in the evolution of user friendly applications and services. We often come across the word Hadoop now a days, and if are curious to know about it in details then this article will help you out.
What is Hadoop:
Hadoop is a popular open source framework from Apache which is used to store process and analysing huge volume of data. It is written in Java and is used by a number of applications like Google, Facebook, Yahoo, LinkedIn and Twitter in batch or offline processing. Hadoop can be scaled up easily by adding nodes in cluster. It should be noted that Hadoop is not an Online Analytical Processing (OLAP).
Hadoop was introduced in 1999 and later it developed for faster distributing data and calculations. Hadoop can store structured and unstructured data for faster processing. It gives more elasticity for assortment, process and examines data in compare to RDBMS.
Modules of Hadoop:
Unlike Java, Hadoop has some basic pillars which are its modules. These modules are explained below.
- HDFS: HDFS refers to Hadoop Distributed File System and was developed as a result of GFS paper published by Google. It states that huge files will be divided into small blocks and will be stored in nodes over a distributed architecture.
- YARN: It stands for Yet Another Resource Negotiator and is used for scheduling the task and managing the cluster.
- Map Reduce: It is a framework that enables Java programs to perform parallel computation of data using a key value pair. It is used to convert data into a set which can be computed in key value pair. The output of the Map task is an intermediate data which is further processed for the desired output.
- Hadoop Common: These are Java libraries are the starter of Hadoop and are used by other modules.
Master Slave Architecture is used by Hadoop for storage and distribution of data. Some basic elements of Hadoop architecture is mentioned below.
- Name Node: It is the representation of files and directory used in the namespace.
- Data Node: It helps in managing the state of HDFS node and allows users interaction with the blocks.
- Master Node: It enables users to perform parallel processing of data through Hadoop MapReduce.
- Slave Node: These are additional machines in the cluster which is used to store data of complex calculations. It must be noted that the slave nodes come with Data Node and Task Tracker.
Advantages of Hadoop:
Knowledge of any technology is incomplete until and unless you are not aware of the advantages. In this section we will see the advantages of Hadoop.
- Quick: Data is distributed over the cluster in HDFS and are mapped for quick retrieval. The processing time is reduced as the data and the processing tool resides on the same servers. The speed of data recovery can be understood from the fact that terabytes of data can be processed in few minutes.
- Cost Effective: Open source feature of Hadoop allows users have commodity hardware to save data. Thus making it cost effective as compared to other systems of database management.
- Scalable: Hadoop clusters can be easily extended by simply adding nodes to the cluster.
- Withstand Failure: HDFS property allows data to replicate over the network so in case of any failure, a copy of data is always available for the users.
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