How does Hadoop work?
Hadoop operates by distributing large data sets across multiple machines in a cluster, using its two primary components: the Hadoop Distributed File System (HDFS) and MapReduce.
HDFS handles data storage by splitting files into blocks (typically 128MB or 256MB in size) and distributing them across the cluster’s nodes. It maintains high availability and fault tolerance through data replication, storing multiple copies of each data block on different nodes. This setup ensures that the system can recover quickly from a node failure.
MapRreduce, the processing component, works by breaking down processing tasks into smaller sub-tasks, distributed across the nodes. The process consists of two phases: the ‘Map’ phase, which processes and transforms the input data into intermediate key-value pairs, and the ‘Reduce’ phase, which aggregates these intermediate results to produce the final output. This model allows for efficient parallel processing of large data volumes, leveraging the distributed nature of HDFS.
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.