When dealing with big data, traditional databases often hit their limits. Whether it’s millions of e-commerce transactions or large-scale healthcare records, you need a framework that can store and process this data efficiently. That’s where Hadoop architecture comes in.
Hadoop is an open-source framework designed to scale from one machine to thousands. It allows distributed storage and parallel processing, making it a backbone for big data systems.
Core Components of Hadoop
Hadoop works through different layers, each handling a specific task:
HDFS (Hadoop Distributed File System): Splits files into blocks and stores them across multiple nodes.
MapReduce: Processes data in parallel, improving speed and efficiency.
YARN: Manages and allocates resources for tasks.
Ecosystem Tools (Hive, Pig, Spark): Provide ways to query, analyse, and transform data.
Master-Slave Architecture
Hadoop clusters are built using a master-slave model:
Master Node: Runs NameNode and ResourceManager, managing metadata and resources.
Slave Nodes: Run DataNodes and NodeManagers, storing blocks and executing tasks.
This design also provides fault tolerance through replication, meaning your data stays safe even if one node fails.
Why Developers Should Learn Hadoop
Handles structured, semi-structured, and unstructured data.
Works on commodity hardware, reducing costs.
Supports petabyte-scale data processing.
Widely used in industries like banking, telecom, healthcare, and e-commerce.
If you are starting your journey in data engineering or data science, exploring Hadoop architecture in big data will give you a strong understanding of how large-scale systems manage information.
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