Hadoop is an open-source software platform for distributed storage and processing of large volumes of data (Big Data). The project was created by Doug Cutting and Mike Cafarella and is maintained by the Apache Software Foundation community. Hadoop enables the processing of terabytes and petabytes of information using clusters of servers combined into a single system.
Principle of Operation
The Hadoop architecture is based on two key components:
- HDFS (Hadoop Distributed File System) – a distributed file system that stores data across multiple nodes with automatic replication and fault tolerance.
- MapReduce – a parallel computing model that divides tasks into two stages: Map (data distribution and processing) and Reduce (aggregation of results).
These components allow Hadoop to efficiently utilize cluster resources and perform large-scale data analysis even on low-cost hardware.
Over time, the Hadoop ecosystem has been expanded with additional tools such as YARN (resource management system), Hive (SQL-like query language), Pig, HBase, and Spark, which made the platform more flexible and user-friendly.
Applications
Hadoop is used in scenarios where massive amounts of unstructured data must be processed, such as:
- log, web traffic, and user behavior analysis;
- storage and processing of IoT device data;
- financial and marketing model computation;
- large-scale image, video, and text processing.
Major companies including Yahoo, Facebook, LinkedIn, and Netflix have used Hadoop as the foundation for their analytical and recommendation systems.
Advantages
Key advantages of Hadoop include:
- High scalability – adding new nodes increases computing capacity without system downtime;
- Fault tolerance – data is automatically replicated across multiple nodes;
- Flexibility – supports structured, semi-structured, and unstructured data;
- Cost efficiency – runs on standard servers without requiring specialized hardware.
Example of Use
A company analyzing billions of log records daily can use Hadoop to automatically sort, filter, and aggregate data to identify patterns and predict service load.