Introduction
In today’s world, Big data and Hadoop are used as synonyms. However, Hadoop has proved itself a powerful tool for Big Data Analysis. With its enormous popularity, every professional wants to make their career in Big Data Hadoop. As a result, to gain experience in this domain, it is necessary to undergo Hadoop Online Training. Such training aims to prepare students and aspirants with in-depth knowledge of the technology and help them with their career goals.
Meaning of Hadoop
It is an open-source software framework that can manage several processes or jobs at once and store a large volume of data. Furthermore, it comprises two Hadoop cluster components, namely HDFS and YARN. These parts are as follows:
Hadoop Distributed File System
HDFS is in charge of distributed storage. It employs a Master-Slave environment, with the Master being high-end computers and the Slaves being a low-cost system. The Master in the Hadoop environment should be placed on powerful hardware as it is the hub of the Hadoop cluster. Furthermore, HDFS divides Big Data into blocks and stores them on a network of slave nodes.
Yet Another Resource Negotiator (YARN)
YARN handles Hadoop data processing. However, the basic idea behind YARN was to separate the tasks of resource management and job scheduling. Moreover, it comprises two components; Resource and Node Manager. The Resource Manager is in charge of resolving resource disputes across all applications. The Node Manager, on the other hand, is the pre-machine framework agent responsible for monitoring resource usage and reporting to the Resource Manager or Scheduler.
Benefits of Hadoop
Hadoop is scalable, offers an easy-to-use interface, and is cost-effective. Besides, it provides several benefits. These benefits are as follows:
Open Source Platform
It is an open-source platform, which means the source code is readily available. You may change the source code according to your business needs.
Scalability
It works on horizontal scalability, which states that we must add the entire system to the cluster of nodes rather than changing the system’s configuration, such as adding disk, RAM, and more, known as vertical scalability.
Accepts Data from Varied Sources
It accepts data from varied sources. However, data can be organized or unorganized and can extract from different sources such as email conversations, social media, etc. Moreover, It can take value from a wide range of data. It can read data from text files, XML files, pictures, CSV files, and other formats.
Fault-Tolerant
It is the core feature of Hadoop. However, each block in HDFS has a replication factor of three by default. Moreover, HDFS duplicates each data block and stores it in several locations across the cluster. If a block goes missing due to machine failure, you can replace it with two similar blocks. Fault Tolerance in Hadoop gets implemented in this manner.
Compatability
Most developing Big Data technologies are compatible with Hadoop. They have processing engines that use Hadoop as a backend, which means that you can use Hadoop as a data storage platform.
Support Multiple Languages
Hadoop can support multiple languages. As a result, developers can code various languages like C++, Python, C, Groovy, Ruby, and Perl.
Conclusion
Finally, the Big Data Hadoop has dominated the analytical industry by proving to be the powerful software in the market. As a result, to become a professional in this field, you must have proper training. However, you need to enrol yourself in the best Big Data Hadoop Training Institute in Noida. Such training institutes help you gain extensive knowledge and assist you with high-paid jobs. In addition, you must have a critical approach to planning and executing complex projects and become an expert in this domain.
