If you get your hands on Hadoop Ecosystem, you will notice that it is made up of two essential services. One of them is an extremely reliable distributed file system which is called as HDFS (Hadoop Distributed File System).
The second one is tremendously top-notch performing parallel data processing engine which is called as Hadoop MapReduce.
It can be tough to select a name for a product maybe that is why the creator of this system Doug Cutting surprisingly decided to name it after a toy of his son which was an elephant.
When Hadoop Distributed File System and MapReduce are fused together to work as one, then they make a software framework which can process enormous amounts of data and the process is exceptionally reliable and fault tolerating as well.
It does not matter if datasets comprise tens of terabytes to petabytes in size the Hadoop which a generic processing framework is specifically designed to carry out the queries and batch read operations.
People love software which can perform analysis of data with flexibility and with a high price to performance ratio that is why the Hadoop has become very popular in the last couple of years.
The specialty of Hadoop system is that it has characteristics of flexible data analysis which can apply to various forms of data and that data can range from structureless data (unprocessed text, partially organized data for example logs) to data which is well structured and have fixed outline.
This system has gained essential usefulness in fields in which substantial server farms are being used to gather raw data from various sources.
Hadoop makes use of only one single server farm to analyze and process parallel queries and behind the scene batch jobs.
The benefit of this excellent service for the user is clear as Hadoop system saves them some money which they would have spent on purchasing separate hardware for processing the data through the traditional database system.
As you can do the processing of data within the Hadoop system so in another way, it also saves you some extra time by not making you transfer that data to some other method.
The Hadoop systems also have some tools up in its sleeves which can be used to fulfill your requirements. It has a Hive which is a SQL dialect plus the Pig which can be defined as a data flow language and it can cover the boredom of doing MapReduce works for making higher-level generalizations suitable for user aims.
A word about MapReduce, Pig, and Hive:
Distributed data analytics make use of MapReduce because it is all in one computing setup and runtime system and now it is being used by many organizations.
Whether you are going to make scalable foundations for analytics or regular reporting to get algorithms for new machines, the MapReduce gives you a base which is flexible and scalable for analytics. The MapReduce works in this way.
First, it breaks down the given job into much smaller units of tasks and then the tasks are disseminated throughout the cluster to parallelize plus balancing the given burden to every possible extent.
A program which is used to produce or make data flow from extraction, transformation and then processing and analyzing of enormous datasets is called is Pig.
Hadoop have a system which is based on SQL, and it helps, to sum up, the data and all of the queries is known as Hive.
It is the Java API which is exceptionally upper level, well known and popular that system that can overcome many ramifications of MapReduce.