Learn Hadoop Online: A Brief Summary of Apache Hadoop
Introduction To Apache Hadoop
A type of open-source software based on a Java programming framework, a Hadoop system is programmed to address any hurdle it faces. Using a skilled framework distributes unclear and large amounts of data in multiple programming systems.
Apache Hadoop provides increased effectiveness and efficiency with high productivity. Though it is a single server, it extends to various networks further connected to machines.
Components
Apache Hadoop has several components which play a critical role in bringing out the responsibilities of Hadoop. They are as follows:
- Library deals with all the complications and discrepancies at the top layer of an application.
- The Hadoop Distributed File System is the Storage unit that helps process stored data in various chunks and is efficiently transmitted with the help of cluster nodes.
- MapReduce is the processing unit.
- Yet Another Resource Navigator (YARN) is the resource management unit.
Role of Apache Hadoop In Big Data
In the era of digitization, all information has to be stored digitally, and it is a huge challenge to control and manage all the data. It thereby creates a need to develop a system that can control, manage and handle an overabundance of such data. Thanks to Apache Hadoop, one can store and manage big data. Various roles played by it are:
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It stores data at a lower cost:
Apache Hadoop is designed in such a way that it can store data at much lower costs as compared to other systems available.
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Velocity and variety:
Apache Hadoop processes data and gives information to enterprises on time. It uses different techniques and tools to structure the data into valuable outcomes.
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Provides security to big data:
Apache Hadoop is also used to detect cyber attacks on the system as it uses different tools and techniques. It is also helpful in recognizing the attackers that try to attack and want to gain access.
Advantages of Apache Hadoop
Some of the advantages of Apache Hadoop are mentioned below.
- Flexibility: In Apache Hadoop, data can be stored in semi-structured and unstructured formats. It also enables enterprises to access new data sources easily.
- Scalable: Traditional systems have limited data storage capacity. On the other hand, Hadoop is highly scalable as it is distributed and stored across several servers.
- Resilient: This system is fault resilient as it stores data in several nodes, and there is another copy to use in the event of a contingency.
- Fast: The storage method used by Hadoop is rooted in a distributed file system that manages clustered data. The tools used for processing data are often located on the server where data is placed, resulting in faster data processing.
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