Introduction of Hadoop Big Data 

Hadoop big data is most powerful big data tool in the market because of its features. Features like Fault tolerance, Reliability, High Availability etc. It is an open source software framework that supports distributed storage and processing of huge amount of data set. 

Important Features of Hadoop Big Data 

1.Distributed Processing - It process the data in parallel on a cluster of nodes.Hadoop stores huge amount of data in a distributed manner in HDFS.

2.Scalability - Hadoop provides horizontal scalability so new node added on the fly model to the system. In Apache hadoop, applications run on more than thousands of node. Hadoop is an open-source platform. This makes it extremely scalable platform. So, new nodes can be easily added without any downtime.

3.Economic - As we are using low-cost commodity hardware, we don’t need to spend a huge amount of money for scaling out your Hadoop cluster. Hadoop is not very expensive as it runs on the cluster of commodity hardware

4.Flexibility - It deals with structured, semi-structured or unstructured.Hadoop is very flexible in terms of ability to deal with all kinds of data

5.Data Locality - This minimizes network congestion and increases the over throughput of the system. Learn more about data locality. It refers to the ability to move the computation close to where actual data resides on the node. Instead of moving data to computation. 

Conclusion 

In conclusion all these features of Big data Hadoop make it powerful for the Big data processing. Hadoop is cost efficient as it runs on a cluster of commodity hardware. Hadoop work on Data locality as moving computation is cheaper than moving data.It reliably stores huge amount of data despite hardware failure. It provides High scalability and high availability.