Hadoop and HDFS are two terms that go hand-in-hand in the world of big data, but they serve different purposes:

• Hadoop is a broader framework. It’s an open-source system that allows you to store, process, and analyze large datasets across clusters of commodity hardware. Think of it as a big data toolbox.

• HDFS (Hadoop Distributed File System) is one of the tools in that toolbox. It’s a specific file system designed to store large data sets reliably across those clusters of hardware. HDFS is like a filing cabinet within the Hadoop framework, optimized for massive amounts of data.

Here’s an analogy: Imagine Hadoop is a restaurant kitchen. The kitchen itself (Hadoop) has all the tools and resources needed to prepare a meal (process data). HDFS would be the large walk-in freezer (data storage) where the kitchen keeps all the ingredients (data) they need to cook with.

In summary, Hadoop is the entire framework for big data processing, and HDFS is a specific component within Hadoop that handles data storage.