Answer & Solution
Answer: Option D
Solution:
The correct answer is
B: HDFS.
Let's break down why:
HDFS stands for
Hadoop Distributed File System. This is the heart of Hadoop's storage capabilities. Think of it like this: if you have a huge book (your data) that's too big for one bookshelf (a single computer), HDFS is designed to cleverly split that book into many parts and store those parts across many different bookshelves (computers) in a cluster. This is what we call
distributed storage. It's designed to be very reliable and handle very large files across many machines.
Now, let's look at why the other options are not responsible for distributed storage:
A: YARN (Yet Another Resource Negotiator) is like the operating system of Hadoop. Its main job is to manage the resources (like CPU and memory) on your cluster and schedule your applications (like MapReduce jobs) to run. It doesn't store the data itself; it manages how jobs use the resources.
C: MapReduce is a programming model and processing engine. It's what Hadoop uses to process and analyze the large amounts of data stored in HDFS. So, while HDFS stores the data, MapReduce processes it. It's for computing, not for storing.
D: Hive is a data warehousing tool that sits on top of Hadoop. It lets you easily query and analyze data stored in HDFS using a language similar to SQL (called HiveQL). It's an analysis tool that *uses* the data stored in HDFS, but it's not a storage component itself.
In summary, when you hear
distributed storage in Hadoop,
HDFS is the core component that comes to mind.
