If you know that what is Big Data then understanding Hadoop is not difficult for you. Hadoop is Open-Source software which developed by Apache Corporation. It can store and analyze structured and unstructured large data sets called “Big Data”. The Apache Hadoop is baked with immense power to process Big Data. A Hadoop Developer should have knowledge of major programming languages like Java, and SQL.
Hadoop allows distributed processing of huge data sets over clusters of computers more effectively than the conventional enterprise data warehouse. The core part of Apache Hadoop is composed of a storage which is recognized as Hadoop Distributed File System (HDFS). Hadoop breaks files into extended blocks and distributes them over nodes in a cluster. It then shifts packaged code into nodes to process the data concurrently.
“Did you know the name “Hadoop” was given upon the toy elephant of Doug Cutting’s son.” Doug chooses the name Hadoop so anyone can easily remember and search it on Google.”
The Hadoop framework is comprised of the following modules:
Hadoop Common: – It includes libraries and utilities required by different Hadoop modules.
Hadoop MapReduce: – MapReduce is a programming model for processing large data sets.
Hadoop Distributed File System (HDFS) : – It is a distributed file-system that stores data on stock machines.
Hadoop YARN: – A resource-management platform helps to manage computing resources in clusters.
Main Features of Hadoop
Scalability: – It is the biggest advantage of using Hadoop for Data Processing.
You can easily improve your system to manage larger data by joining extra nodes.
Flexible Data Processing: – Handling and processing structured or unstructured Data were very complex for the organizations before the advent of Hadoop. Hadoop has the capabilities to manage data both structured or unstructured data. Hadoop brings the value to the table where unstructured data can be useful in the decision-making process.
Fault Tolerant: – Hadoop has distributed file system which stores data on multiple nodes to ensure the data safety.
Powerful: – A distributed computing model allows you to add more required nodes (PC’s) to processes big data fast. You just need to use more computing nodes to larger data sets.
Cost Effective: – It provides cost effective storage facility to manage rapidly growing large data sets.