Goal of the Hadoop

The primary goal of the Hadoop Distributed File System (HDFS) is to provide a reliable, fault-tolerant, and scalable platform for storing and processing large amounts of data across a cluster of commodity hardware. Some of the key goals of HDFS are:

Scalability: 

HDFS is designed to scale horizontally by adding more nodes to the cluster. This allows it to store and process large amounts of data, making it ideal for big data applications.

Fault Tolerance: 

HDFS is designed to be fault-tolerant, which means that it can continue to operate even if one or more nodes in the cluster fail. It achieves fault tolerance by replicating data across multiple nodes in the cluster.

High Throughput: 

HDFS is optimized for storing and processing large files, which means that it can provide high throughput for data-intensive applications.

Low-Cost Hardware: 

HDFS is designed to work with commodity hardware, which is much cheaper than specialized hardware. This makes it cost-effective to build large-scale data storage and processing systems.

Data Locality: 

HDFS is designed to co-locate data and compute resources, which means that it can minimize data movement across the network by moving the computation to the data. This feature can significantly improve the performance of data-intensive applications.