What is a common storage solution for handling Big Data?

Distributed file systems like HDFS (Hadoop Distributed File System) are specifically designed to handle the vast amounts of data typical in Big Data applications. HDFS allows for the storage of large files across multiple machines, meaning that data can be stored in a way that is both scalable and fault-tolerant. This is essential for Big Data because single machines often cannot handle the volume, velocity, and variety of data generated in modern applications.

One of the key features of HDFS is its ability to split files into smaller blocks and distribute them across a cluster of machines. This enables parallel processing, which is crucial for efficiently analyzing and processing Big Data. Additionally, HDFS is built to work well with other components of the Hadoop ecosystem, allowing for seamless integration with tools for data processing, analysis, and machine learning.

In contrast, traditional SQL databases are often limited in their ability to scale horizontally and handle unstructured data, local hard drives restrict the amount of data one can effectively manage, and cloud services alone, while they can provide scalability and flexibility, do not inherently solve the issues associated with big data storage without a suitable architecture like HDFS or similar distributed file systems.