Hadoop Distributed File System (HDFS)
A distributed file system called HDFS offers application data high-throughput access. It is intended to reliably and fault-tolerantly store big files on several machines. A single NameNode controls the file system namespace, while DataNodes holds the real data in HDFS’s master-slave design.
Characteristics of HDFS
For handling massive volumes of data, the Hadoop Distributed File System (HDFS) provides a strong and dependable distributed file system. The key attributes of HDFS are as follows:
- Distributed Storage: Every data block that HDFS has divided into smaller pieces is stored in duplicate on a large number of nodes inside a Hadoop cluster. This distributed storage ensures fault tolerance and excellent data availability even if some nodes fail.
- Scalability: Because HDFS is designed to scale horizontally, enterprises may easily add more nodes to their Hadoop clusters to accommodate increasing volumes of data.
- Data Replication: To ensure the accuracy of the data, HDFS replicates data blocks between nodes. By default, each block is stored in three copies: two on the cluster’s other nodes and one on the node where the data is written.
- Data Compression: HDFS supports data compression, which reduces the amount of storage required.
The master node, also known as the ResourceManager in YARN or the NameNode in Hadoop’s HDFS, is the fundamental component of the distributed system. Sometimes referred to as a DataNode in Hadoop’s HDFS or a NodeManager in YARN, a slave node is a worker node in a distributed system.
Hadoop : Components, Functionality, and Challenges in Big Data
The technical explosion of data from digital media has led to the proliferation of modern Big Data technologies worldwide in the system. An open-source framework called Hadoop has emerged as a leading real-world solution for the distributed storage and processing of big data. Nevertheless, Apache Hadoop was the first to demonstrate this wave of innovation. In the era of big data processing, businesses across various industries need to manage and analyze internal large volumes of data efficiently and strategically.
In this article, we’ll explore the significance and overview of Hadoop and its components step-by-step.