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Understanding Hadoop and Its Components

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What is Hadoop: Hadoop is a framework for managing big data using distributed storage and parallel processing
It consists of HDFS (storage), MapReduce (processing), and YARN (resource management)
Hadoop enables processing of structured, semi-structured, and unstructured data
How Hadoop Works: Data is divided into 128MB blocks and stored across multiple commodity nodes
Name node manages metadata and distributes data across slave nodes
MapReduce processes data in parallel using small code chunks
YARN manages cluster resources and schedules tasks
Key Components: HDFS provides distributed storage with name and data nodes
Job Tracker manages resource allocation and task tracking
Task Tracker processes tasks and updates Job Tracker status
Data is replicated across nodes for fault tolerance
Applications and Benefits: Used by major companies like British Airways, Netflix, and NSA
Enables fraud detection in financial sector
Improves healthcare data analysis and retail sales tracking
Provides scalability and cost-effectiveness through distributed architecture
Challenges: Steep learning curve for MapReduce programming
Limited scalability for different datasets
Security concerns with sensitive data handling
MapReduce limitations in real-time interactive tasks