Hadoop Storage & Data Processing Guide

Hadoop is a distributed storage and data processing framework that can help address storage and data processing issues in the following ways:

1. Storage: Hadoop utilizes the Hadoop Distributed File System (HDFS) to store data. HDFS divides data into multiple blocks, each of which is replicated across different nodes to achieve redundant backups and high availability. This distributed storage method can handle large-scale data and withstand node failures.
2. Data processing: Hadoop utilizes MapReduce to process data stored in HDFS. MapReduce is a parallel computing model that divides data into multiple parts for parallel processing across multiple nodes, and then aggregates the results. This method of data processing can improve processing speed and efficiency, making it suitable for large-scale data processing tasks.

In general, Hadoop effectively addresses the challenges of large-scale data storage and processing through distributed storage and data processing, thus enhancing data reliability, scalability, and processing efficiency.