What is Big Data?
Big Data refers to datasets that are too large and complex for traditional systems to store and process. The main challenges of Big Data fall under three key areas, often referred to as the three Vs: volume, velocity, and variety.

Every minute, we generate enormous amounts of data—204 million emails, 1.8 million Facebook likes, 278,000 Tweets, and 200,000 photo uploads on Facebook.

Volume: Data is being generated in the range of terabytes to petabytes. Social media platforms like Facebook, which generates 500 TB of data daily, are among the largest contributors.

Velocity: Enterprises need to process data quickly. For instance, credit card fraud detection requires real-time data processing. Thus, a system that can handle high-speed data is crucial.

Variety: Data comes in various formats—text, XML, images, audio, video, etc. Big Data technologies must be capable of analyzing diverse data types.

Why Hadoop Was Invented
Let's explore the limitations of traditional methods that led to the creation of Hadoop:

Storage for Large Datasets: Conventional RDBMS systems can't store vast amounts of data, and the cost of storing large data sets is very high in traditional systems.

Handling Data in Different Formats: RDBMS works well for structured data, but real-world data is often structured, unstructured, or semi-structured.

Data Generation at High Speed: With the constant flow of data, especially in the order of terabytes to petabytes, traditional systems struggle with real-time processing.

What is Hadoop?
Hadoop solves these Big Data challenges by offering a distributed storage system and analytics platform that can store massive datasets across a cluster of inexpensive machines. Developed as an open-source project by the Apache Software Foundation, Hadoop was created by Doug Cutting, and Yahoo handed it over to the Apache Foundation in 2008.

Hadoop comes in several versions, including Cloudera, IBM BigInsight, MapR, and Hortonworks.

Prerequisites to Learn Hadoop

Basic Linux Command Knowledge: Hadoop is often set up on Linux, particularly Ubuntu. Familiarity with basic Linux commands is essential for managing files in HDFS.

Basic Java Concepts: While you can use other programming languages like Python, Perl, C, or Ruby in Hadoop via its streaming API, having knowledge of Java concepts can be useful when writing map and reduce functions.

Core Components of Hadoop
Hadoop has three primary components:

Hadoop Distributed File System (HDFS): The storage layer for Hadoop.

MapReduce: The data processing layer.

YARN: The resource management layer.

1. HDFS
HDFS provides distributed storage by dividing files into blocks and storing them across slave nodes in the cluster. The NameNode on the master machine keeps track of metadata, while DataNodes on the slave machines store the actual data.

HDFS also supports Erasure Coding (introduced in Hadoop 3.0), offering a more efficient method of data fault tolerance with lower storage overhead compared to replication.

2. MapReduce
MapReduce processes data in two stages:

Map Phase: Data is transformed into key-value pairs.

Reduce Phase: Aggregation is applied to the key-value pairs.

3. YARN (Yet Another Resource Manager)
YARN is responsible for resource management and job scheduling. The Resource Manager tracks the resources on the slave nodes, while the Node Manager on each slave manages containers for tasks. The Application Master negotiates resources for the job and oversees its execution.

Why Hadoop?
Hadoop is widely adopted because of its ability to store and process large datasets in a distributed manner, offering scalability, fault tolerance, and cost-effectiveness. Key features include:

Flexibility: Hadoop can store and process structured, semi-structured, and unstructured data without being tied to a single schema.

Scalability: It can be scaled economically using commodity hardware, making it a viable solution for growing data needs.

Fault Tolerance: Data is replicated across nodes, ensuring that if one node fails, another can take its place without data loss.

Hadoop Ecosystem
Various flavors of Hadoop are available, including:

Apache Hadoop: The vanilla version of Hadoop hosted by the Apache Software Foundation.

Cloudera: A popular enterprise Hadoop distribution.

Hortonworks: Known for its robust and reliable platform.

MapR: Features a faster version of HDFS.

IBM Big Insights: A proprietary version offering integration with IBM’s suite of products.

Hadoop's Future Scope
The demand for Big Data professionals is growing rapidly. According to a report by Forbes, 90% of global organizations are expected to invest in Big Data technology. This surge in demand for Big Data skills, particularly Hadoop, presents lucrative career opportunities. According to Alice Hills of Dice, the number of Hadoop job postings has increased by 64% from the previous year.

Conclusion
Apache Hadoop is a powerful open-source framework for storing and processing Big Data. It is scalable, fault-tolerant, and cost-effective, making it an essential tool for organizations dealing with vast amounts of data. As the demand for Big Data professionals continues to grow, mastering Hadoop can significantly boost your career in this field.