Hadoop Architecture in Big Data: YARN, HDFS, and MapReduce

What is Hadoop? | What is Hadoop Architecture? | HDFS Architecture | YARN Architecture | MapReduce | The Takeaway

Do you want to know more about the Hadoop Architecture in Big Data? HDFS, MapReduce, and YARN are the three important concepts of Hadoop. In this tutorial, you will learn the Apache Hadoop HDFS and YARN Architecture in details. 

What is Hadoop?

Hadoop is an open source framework that allows for the distributed processing of large datasets across clusters of computers using simple programming models. It is from Apache and is used to store process and analyze data which are very huge in volume. The summary of the Hadoop framework is as follows:

In a nutshell, Hadoop provides a reliable shared storage and analysis system for big data. The Hadoop Distributed File System (HDFS) is specially designed to be highly fault-tolerant. 

Hadoop employs a NameNode and DataNode architecture to implement the HDFS, which provides high-performance access to data across highly scalable Hadoop clusters. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Hadoop Version 2.0 and above, employs YARN (Yet Another Resource Negotiator) Architecture, which allows different data processing methods like graph processing, interactive processing, stream processing as well as batch processing to run and process data stored in HDFS.… Read more..