What technology underpins distributed storage and processing in Big Data environments?BlockchainHadoop Distributed File System (HDFS)RESTful APIsContent Delivery Network (CDN)

The technology that underpins distributed storage and processing in Big Data environments is the Hadoop Distributed File System (HDFS).

Here's a step-by-step explanation:

1. HDFS is a distributed file system that is designed to run on commodity hardware. It is part of the Apache Hadoop project, which is an open-source software framework for storage and large-scale processing of data sets.

2. HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, which is a master server that manages the file system namespace and regulates access to files by clients. The cluster also includes a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on.

3. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. This allows the dataset to be processed in parallel, improving performance.

4. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes.

5. The DataNodes are responsible for serving read and write requests from the file system’s clients, and they also perform block creation, deletion, and replication upon instruction from the NameNode.

6. The distribution of data across multiple nodes and the replication of data blocks provides high availability and fault tolerance, which are key requirements in Big Data environments.

So, HDFS is the technology that underpins distributed storage and processing in Big Data environments.