What does the Hadoop Distributed File System (HDFS) do?

The Hadoop Distributed File System (HDFS) is fundamentally designed for storing large datasets across multiple machines in a distributed computing environment. Its architecture allows for data to be divided into blocks and distributed across different nodes. This distribution not only enhances storage capacity but also improves fault tolerance; if one node fails, the data can still be accessed from other nodes. The system is optimized for high throughput of data access, which is particularly beneficial for big data applications that need to handle vast amounts of information efficiently.

While other options, such as processing data in real-time or managing security protocols, might be relevant to data processing ecosystems, these functionalities do not describe the core purpose of HDFS. HDFS does not inherently provide real-time processing capabilities, as it is designed more for batch processing of large datasets. Similarly, security management and user authentication are handled by additional layers or tools within the Hadoop ecosystem, rather than being primary functions of HDFS itself.