Key features of Apache Hadoop in 2019


Apache Hadoop is the most popular big data tool. Hadoop provides the world's most reliable storage layer. Some of the most important features that make this tool the most suitable for processing large volumes of data are:

Open source

According to Hadoop training in Bangalore, Apache Hadoop is an open source project which means that your code can be modified according to the business requirements.

Distributed processing

As data is stored distributed in HDFS throughout the cluster, the data is processed in parallel in a cluster of nodes.

Fault tolerance 

This is one of the most important features of Hadoop. By default, 3 replicas of each block are stored in the cluster in Hadoop and can also be changed according to the requirement. Then, if any node falls, the data from that node can be easily retrieved from other nodes with the help of this feature. Nodes or tasks failures are automatically recovered by the framework. This is how Hadoop is fault tolerant.


Reliability 

Due to the replication of data in the cluster, data is stored reliably in the machine cluster despite machine failures. If your machine shuts down, your data will also be stored reliably due to this feature of Hadoop.

High availability 

The data is highly available and accessible despite hardware failure due to multiple copies of the data. If a machine or some hardware fails, the data can be accessed from another route.

Scalability 

Hadoop is highly scalable in the way you can easily add new hardware to the nodes. This feature of Hadoop also provides horizontal scalability, which means that new nodes can be added on the fly without any downtime.

Economy 

Apache Hadoop is not very expensive, since it runs on a group of basic hardware. We do not need any specialized machine for this. Hadoop also offers great cost savings, as it is very easy to add more nodes on the fly. Therefore, if the requirement increases, you can also increase the nodes without any downtime and without requiring much prior planning.

Easy to use

It is easy to use because without the need for the client to handle distributed computing, the framework takes care of everything.

Data location 

This is a unique feature of Hadoop that made it easy to handle Big Data. Hadoop works with the principle of data locality that states that calculations move to data instead of data to calculations. When a client sends the MapReduce algorithm, this algorithm moves to the data in the cluster instead of taking the data to the location where the algorithm is sent and then processing them.

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