Even a pep talk about big data and its coming of age is incomplete without ever mentioning Hadoop. What once used to be a Yahoo invention is now an open source software platform which is used to manage large chunks of data with the help of its various tools. With the boom in the internet market, it started to get difficult for bigger companies like Yahoo and Google to manage such large scale data with the older traditional methods and that too in a cost effective manner. And Hadoop proved to be a saving grace for many organisations for managing their big data needs. This is the reason why many big companies are using Hadoop for managing the large scale data and employing professionals and individuals who have a Hadoop certification in their data analyst profile.
Hadoop was originally founded by a Yahoo engineer, Doug Cutting, as a measure to manage large amounts of data by breaking it down in various clusters that could be processed in parallel.
Components of Hadoop
There are two components of Hadoop, which gives it the efficiency to manage the variety, volume, and velocity of big data effortlessly.
1. HDFS (Hadoop Distributed File System)
It serves as a storage unit for Hadoop and collects data that has been spread over multiple systems.
2. MapReduce Engine
It serves as a processing unit for Hadoop by filtering results of various data inputs.
You can easily imagine HDFS as a warehouse where you can safely store your data until the time you need it and that too without the actual warehouse costs. Once the data is stored in the servers, it can be used or read multiple times with great efficiency and speed and that works as a great advantage for people using Hadoop for their data computing,
How does it work?
There are two different nodes in the HDFS; the main Name Node and multiple Data Nodes. The Name node does all the smart work by giving us access to different files and clusters of data and also allows us to replicate data along with liberties of reading, writing, and deleting data distributed in the form of various blocks in different data nodes.
The data nodes are used to store data in the form of blocks and these blocks are distributed all over the nodes for timely processing and replication. The name node is always in a state of constant communication with multiple data nodes and therefore knows the status and location of all the different data blocks located in data nodes. And this makes sure that if the assigned task is not completed in time by a data node then, Name node can assign the same task to a different data node which has the same data block. The data blocks are also in constant communication with each other to make it all operational in the machine cluster.
The data blocks are uniformly distributed in multiple lifeless data nodes in clusters all over the HDFS ecosystem and the Name node can manage them by locating them in working clusters or deleting them from the preposition. For suppose, a data node is not functioning properly and giving the active signal to Namenode then the name node can pretty easily separate it from the cluster of working data nodes and can even add a different data node to the working cluster based on its active status and functioning. This provides a self-healing and easy replicating interface to the whole HDFS ecosystem. The name node can adapt to the system needs and shuffle the data nodes according to the requirement of various tasks and can activate or deactivate the various data nodes in a cluster and this does not put unnecessary pressure on the system and make it operate and process large scale data at lightning speed. The replication of uniformly distributed data blocks which are stored in different clusters of data nodes all over the system make sure that even if one server is down, the file is not corrupted ever.
The MapReduce algorithm or software serves as a processing unit for Hadoop. The general idea of this unit is to first locate the data arranged in various clusters and breaking it down into small and finer pieces and then converting them into a form so that they can be processed in parallel.