Hadoop is a software framework used for storing and processing large datasets across clusters of computers. It is designed to handle big data, which refers to extremely large and complex datasets that traditional databases cannot manage efficiently.

The four main components of Hadoop are −

• Hadoop Distributed File System (HDFS) − This is a storage system that breaks large files into smaller pieces and distributes them across multiple computers in a cluster. It ensures data reliability and enables parallel processing of data across the cluster.

• MapReduce − This is a programming model used for processing and analyzing large datasets in parallel across the cluster. It consists of two main tasks: Map, which processes and transforms input data into intermediate key-value pairs, and Reduce, which aggregates and summarizes the intermediate data to produce the final output.

• YARN (Yet Another Resource Negotiator) − YARN is a resource management and job scheduling component of Hadoop. It allocates resources (CPU, memory) to various applications running on the cluster and manages their execution efficiently.

• Hadoop Common − This includes libraries and utilities used by other Hadoop components. It provides tools and infrastructure for the entire Hadoop ecosystem, such as authentication, configuration, and logging.

Hadoop doesn't have a full form in the traditional sense. It is not an acronym like many other technologies. Instead, the name "Hadoop" comes from a toy elephant that belonged to the son of one of its creators, Doug Cutting. The toy elephant was named Hadoop, and the project was named after it.

The time it takes to learn Hadoop can vary depending on factors like your prior experience with programming and databases, the amount of time you dedicate to learning, and the depth of understanding you aim to achieve. Learning the basics of Hadoop, such as understanding its components, setting up a Hadoop cluster, and writing simple MapReduce programs, could take a few weeks to a few months.

However, becoming proficient in Hadoop, including mastering advanced concepts like HDFS optimization, YARN resource management, and using Hadoop ecosystem tools, may take several months to a year or more of consistent learning and practice.

Hadoop is not a data warehouse because they serve different purposes and have different architectures. Hadoop is a framework for storing and processing large volumes of unstructured and semi-structured data across distributed clusters of computers. It is designed for handling big data and supports batch processing of large datasets using technologies like HDFS and MapReduce.

On the other hand, a data warehouse is a centralized repository of structured data that is optimized for querying and analysis. It is generally used for storing structured data from various sources, organizing it into a schema, and providing fast access for reporting and analysis purposes.

The biggest advantage of Hadoop is its ability to handle and process large volumes of data efficiently. Hadoop is designed to distribute data and processing tasks across multiple computers in a cluster, allowing it to scale easily to handle massive datasets that traditional databases or processing systems struggle to manage. This enables organizations to store, process, and analyze huge amounts of data, gaining valuable insights and making informed decisions that would not be possible with conventional technologies.

Hadoop uses several software components to manage and process big data. Hadoop Distributed File System (HDFS) stores large datasets across a cluster of computers, breaking them into smaller pieces for efficient storage and retrieval. MapReduce is the processing engine that divides data processing tasks into smaller parts and executes them in parallel across the cluster. YARN manages computing resources across the cluster, allocating resources to different applications and ensuring efficient execution. Together, these software components enable Hadoop to store, process, and analyze massive amounts of data effectively.

Hadoop was created by Doug Cutting and Mike Cafarella. Doug Cutting, inspired by Google's MapReduce and Google File System papers, started working on an open-source project to create a framework for distributed storage and processing of big data. He named the project after his son's toy elephant, Hadoop. Later, Mike Cafarella joined him in developing the framework, and together they released it as an open-source project in 2005.

Learning Hadoop requires understanding programming languages like Java, Python, or Scala, along with basic knowledge of databases and SQL. Familiarity with the Linux command line and big data concepts such as distributed computing and scalability is also important. Problem-solving skills are crucial for troubleshooting issues, while effective communication skills facilitate collaboration with team members. By mastering these skills and continuously practicing, one can become proficient in working with Hadoop for big data processing and analysis.

The latest stable version of Apache Hadoop is 3.3.1, released in September 2021. This version includes various improvements, bug fixes, and new features to enhance the performance, scalability, and reliability of the Hadoop framework. It is always a good idea to check the official Apache Hadoop website or repository for the most up-to-date information on the latest version and its features.