In this Blog, we are going to talk about what is Hadoop, Evolution of Hadoop, Why organizations are using it, Hadoop architecture and Modules in brief.
Let us understand what really Hadoop is.
It is an open-sourceJava-based programming framework, which helps in processing and storage of totally enormous data sets in a dispensed computing environment.
We can say in simple way like Hadoop Allows distributed processing of large datasets.
It provides enormous storage for any sort of information, huge processing and the capacity to control almost limitless concurrent duties or jobs.
Now, we will discuss the epitome of Hadoop evolution.
Apache Software Foundation designed Hadoop for reliable, scalable, distributed computing.
Hadoop was initially released in December 10, 2011 and stable version was released in August 25, 2016.
Let’s dive little deep to understand its evolution.
The first version of Hadoop 1.0 had few components like Pig, Hive, Map Reduce and HDFS.
Later in Hadoop 2.0 version, which is now stable version, many components have been added, which are RT Stream Graph,Services HBase, Tez and YARN.
Now, let’s understand where Hadoop is used?
Hadoop is quickly becoming a central store for big data in the enterprise.
A wide variety of companies and organizations use Hadoop for both research and production.
There are plenty of reasons, why companies or organizations require Hadoop.
Few of them are…
Hadoop supports Data Exploration with Full Datasets.
Hadoop can mine or sort the large datasets.
Hadoop Pre-process Large Scale Raw Data and for Data Agility.
Let’s understand in brief about hadoop architecture and its modules.
Hadoop has four modules in its architecture, they are
The entire modules in Hadoop are designed with essential assumption that hardware failures are fashioned and should be automatically dealt with the aid of the framework.
Let’s understand each in brief.
Starting with Hadoop common, it comprises libraries and utilities needed by different Hadoop modules.
While Hadoop distributed File process is a distributed file-method that stores information on commodity machines, providing very high aggregate bandwidth across the cluster.
Hadoop YARN is useful in resource-administration platform dependable for managing computing assets in clusters and utilizing them for scheduling of consumer’s applications.
And finally Hadoop Mapreduce, which is an implementation of the Map minimize programming mannequin for gigantic scale data processing.