Introduction to Hadoop: Architecture and Components
What is Hadoop?
An open-source framework for distributed storage and processing of large datasets.
Developed by Apache Software Foundation. Based on the MapReduce programming model. Handles big data across multiple nodes in a cluster. Scalable, fault-tolerant, and cost-effective.
Why Use Hadoop?
✅ Scalability – Handles petabytes of data across many machines.
✅ Fault Tolerance – Automatically recovers from failures.
✅ Cost-Effective – Uses commodity hardware.
✅ Parallel Processing – Processes data across multiple nodes simultaneously.
✅ Supports Various Data Types – Structured, semi-structured, and unstructured data.
Hadoop Architecture Overview
1.Master-Slave Architecture:
Master Node – Manages and coordinates the cluster.
Slave Nodes – Store data and perform computations.
2.Core Components:
HDFS (Hadoop Distributed File System) – Storage layer.
MapReduce – Data processing engine.
YARN (Yet Another Resource Negotiator) – Manages resources.
Common Utilities – Shared libraries for Hadoop modules.
Key Components of Hadoop
HDFS (Storage Layer) – Stores data in a distributed manner using blocks.
MapReduce (Processing Layer) – Processes data in parallel using map & reduce tasks.
YARN (Resource Management) – Allocates and manages resources dynamically.
Hadoop Common – Provides utilities for all Hadoop modules.
Hadoop Ecosystem (Additional Tools)
Hive – SQL-like querying for big data.
Pig – High-level scripting language for data transformation.
HBase – NoSQL database on top of HDFS.
Spark – Fast in-memory processing engine.
Oozie – Workflow scheduling for Hadoop jobs.
Flume & Sqoop – Data ingestion from external sources.
Conclusion
Hadoop is a powerful big data framework for distributed storage & processing.
Highly scalable, fault-tolerant, and cost-effective for large-scale data.
Key components: HDFS (storage), MapReduce (processing), and YARN (resource management).
Rich ecosystem with tools like Hive, Pig, Spark, and HBase.