Why We Learn Hadoop?
The requirement for processing zettabytes of unstructured big data is generating demand for professionals with Hadoop skills to work with unstructured data which is quite complex and challenging to learn Hadoop. Professionals must learn Hadoop to ramp up on the big data technology as Hadoop is soon going to be identified as a must skill by all big data companies.
What is Hadoop?
Hadoop is a programming framework based on java that offers a distributed file system for processing of very large data sets on computer clusters which would be built from commodity hardware.
Scalable – New data in forms of nodes can be added to the cluster without changing the data formats, loading process etc.
Cost effective – Hadoop can store enormous amount of data on enormous cluster of computers and allows operating data in parallel. The result is a sizeable decrease in the cost per terabyte of storage, which in turn makes it affordable to model all your data.
Flexible – Data from multiple sources in any format and type can be joined and aggregated on Hadoop which enables deeper analysis provided by any one system. It does not require any predefined schematic for analyzing the data.
Fault tolerant – When a node is loosed, the system redirects work to another location of the data and continues processing without missing a beat.
Advantages of Using Hadoop
Allows user to quick write and test distributed systems. It is efficient, and automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library is designed to detect and handle failures at the application layer.
Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
It is compatible on all the platforms since it is Java based.