HDFS stands for Hadoop Distributed File System, which is a distributed file system designed to run on commodity hardware and handle large data sets running on commodity hardware. It is a key component of many Hadoop systems, as it provides a means for managing big data and supporting big data analytics. HDFS is intended more for batch processing versus interactive use, so the emphasis in the design is for high data throughput rates, which accommodate streaming access to data sets. HDFS is fault-tolerant and designed to be deployed on low-cost, commodity hardware. It provides high throughput data access to application data and is suitable for applications that have large data sets and enables streaming access to file system data in Apache Hadoop.

HDFS has three main components: NameNode, DataNode, and HDFS-API. The NameNode is the centerpiece of an HDFS file system and manages the file system namespace and regulates access to files by clients. The DataNode manages storage attached to the nodes that they run on and serves read and write requests from the file systems clients. The HDFS-API is the interface that applications use to interact with HDFS.

HDFS is designed to be highly alert and can detect faults quickly. It is intended for batch processing rather than interactive use, so the emphasis in the design is for high data throughput rates, which accommodate streaming access to data sets. HDFS accommodates applications that have data sets typically gigabytes to terabytes in size.

In summary, HDFS is a distributed file system that is designed to handle large data sets running on commodity hardware. It is a key component of many Hadoop systems and provides a means for managing big data and supporting big data analytics. HDFS is fault-tolerant, designed to be deployed on low-cost, commodity hardware, and provides high throughput data access to application data.