Hadoop Big Data Solution

Traditional Approach: The Old Way of Managing Data

In the early days, large companies used a single computer system for both storing and processing data. These systems relied on RDBMS (Relational Database Management Systems) like: Oracle, IBM DB2.

How It Worked:

  • Developers built applications to communicate with databases.
  • The databases stored structured data (organized into rows and columns).
  • These apps helped users to store, retrieve, update, and analyze the data.

Limitations:

  • Limited Capacity: Commodity hardware could only store and process small volumes of data.
  • Slow Processing: The CPU speed was not enough for massive datasets.
  • Lack of Scalability: A single server couldn’t handle growing data loads.
  • Poor Handling of Variety & Velocity: The system couldn’t cope with fast or unstructured data.

Google’s Solution: MapReduce

To solve this, Google invented MapReduce, a revolutionary way to process large data sets efficiently. It follows a master-slave architecture: The Master node breaks the task into smaller subtasks. These are assigned to Slave nodes for parallel execution. The Master then collects and combines the results into the final output.

This allows:

  • Many machines to work in parallel, speeding up processing.
  • Systems to handle massive datasets without overloading one server.

Example - Imagine organizing a library of 1 million books: Instead of one person sorting all the books, you assign 100 people to sort small piles. Later, one person (Master) collects their results and arranges everything perfectly.

Hadoop: Open-Source Big Data Solution -

Inspired by Google’s MapReduce model, Doug Cutting and Mike Cafarella created Hadoop in 2005. Hadoop is an open-source framework written in Java. It is designed to handle very large data sets across many low-cost computers. It uses: HDFS to store data across multiple machines and MapReduce to process data in parallel.

Why It’s Powerful:

  • Distributes data and computation across nodes.
  • Handles failures automatically (fault-tolerant).
  • Easily scalable—you just add more machines.
  • Cost-efficient—runs on basic hardware.