Hadoop

TL;DR

Enables storage and distributed processing of very large data sets across multiple machines.
Designed to scale from single servers to thousands of machines, each with local computation and storage.
Provides fault tolerance and data locality to improve performance and resilience.

Definition

Hadoop is an open-source framework that enables the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Explanation

Hadoop stores and processes vast amounts of data by distributing work across multiple machines in a cluster. This distribution allows faster and more efficient analysis because data and computation can be placed on the same machines (data locality), which reduces data transfer. Hadoop also provides fault tolerance so that if a machine in the cluster fails, the data remains available on other machines and processing can continue without interruption. Its programming models simplify writing distributed applications that run across many nodes.

Examples

A social media company may use Hadoop to analyze user behavior and trends, such as the types of content that are most engaging to users. By distributing the data across multiple machines, the company can quickly and efficiently analyze the data to gain insights and make data-driven decisions.

Genomics

In genomics, large amounts of genetic data must be processed and analyzed to understand disease mechanisms and develop treatments. Using Hadoop, researchers can distribute the data across multiple machines, allowing for faster and more efficient analysis that can help identify patterns and correlations in the data.

Distributed processing
Clusters
Fault tolerance
Data locality
Local computation and storage
Simple programming models