Introduction to Hadoop: Architecture and Core Components

 

Introduction 

In today’s data-driven world, organizations generate and consume massive volumes of data daily. Traditional data processing systems struggle to handle such scale and complexity. Enter Apache Hadoop, the open-source framework that revolutionized big data processing with its scalable, fault-tolerant, and distributed computing approach.

In this post, we’ll explore the Hadoop architecture and dive into its core components, helping you understand how it powers large-scale data processing across clusters of machines.



What is Hadoop?

Apache Hadoop is an open-source framework developed to process and store huge datasets in a distributed computing environment. Initially developed by Doug Cutting and Mike Cafarella, Hadoop is now a top-level project under the Apache Software Foundation.

Its key strengths lie in:

  • Scalability: Can grow by simply adding more nodes

  • Fault tolerance: Data is replicated to prevent loss

  • Cost-effectiveness: Runs on commodity hardware

  • High throughput: Handles massive data efficiently

Hadoop Architecture Overview

At a high level, the Hadoop architecture is based on a Master-Slave model and consists of two main layers:

  1. Storage Layer – Hadoop Distributed File System (HDFS)

  2. Processing Layer – MapReduce

These are managed by a set of core components, which coordinate data storage, processing, resource management, and job scheduling.

Core Components of Hadoop

1. Hadoop Distributed File System (HDFS)

HDFS is the backbone of Hadoop's storage system. It stores data in large blocks (default 128MB or 256MB) and distributes them across a cluster.

  • NameNode (Master): Maintains metadata (like file paths, block locations).

  • DataNodes (Slaves): Store the actual data blocks and serve read/write requests from clients.

Key features:

  • Block storage and replication

  • Fault tolerance via block replication (default is 3 copies)

  • Designed for streaming large files

2. MapReduce

MapReduce is Hadoop’s original processing model. It divides tasks into two stages:

  • Map Phase: Processes input data into key-value pairs

  • Reduce Phase: Aggregates and processes results from the Map phase

This model works in a distributed way, enabling large-scale data processing across nodes.

3. YARN (Yet Another Resource Negotiator)

Introduced in Hadoop 2.x, YARN manages resources and job scheduling across the cluster.

  • ResourceManager: Central authority for resource management

  • NodeManager: Manages resources on a single node

  • ApplicationMaster: Manages the lifecycle of individual applications

YARN allows multiple data processing engines (like Spark, Tez, or MapReduce) to run on Hadoop simultaneously.

4. Hadoop Common

Hadoop Common includes shared libraries, utilities, and APIs used across other Hadoop modules. It ensures smooth communication between different components.

Optional (Yet Popular) Hadoop Ecosystem Tools

Though not part of the core, the Hadoop ecosystem includes several tools that enhance its functionality:

  • Hive – SQL-like querying on top of Hadoop

  • Pig – High-level scripting language for data flow

  • HBase – NoSQL database on HDFS

  • Sqoop – Data transfer between Hadoop and RDBMS

  • Flume – Collecting and aggregating log data

  • Zookeeper – Coordination service for distributed systems

  • Oozie – Workflow scheduler for Hadoop jobs

Final Thoughts

Hadoop laid the foundation for the big data revolution. While technologies like Apache Spark and cloud-native tools have gained popularity, Hadoop remains a critical part of many enterprise data architectures.

Understanding its core components—HDFS, MapReduce, YARN, and Hadoop Common—is essential for any data engineer or big data enthusiast looking to dive deeper into the world of distributed data processing.

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