How Does Youtube Store So Much Data?

YouTube is an integral part of your life. And this is true for most people on Earth! This is easily proved by the fact that more than 400 hours of video content is uploaded on YouTube every minute, and approximately 1 billion hours of YouTube videos are watched every day. This makes YouTube the 2nd most popular social media platform in the world with 1.9 billion users (The 1st is Facebook!)

GFS Google File System is a distributed file system developed by Google to manage large scale data in a distributed environment.

Google BigTable is a low latency distributed data storage system built on Google File System to deal with petabyte-scale data spread over thousands of machines. It’s used by over 60 Google products.

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers.

YouTube, is one of the best examples of services which produces a huge quantity of data in a very short period. Data mining of such an enormous quantity of data is performed using Hadoop and MapReduce to measure performance.

Hadoop is a system which provides a reliable shared storage of such huge datasets on the cloud and also provides an analysis system. The storage is provided by HDFS (Hadoop Distributed File System) and analysis by MapReduce. MapReduce is a programming model and an associated implementation for processing large data sets.

Youtube is solving its big data problem using optimal solution of Hadoop cluster and HDFS for YouTube dataset storage and using parallel processing to process large data sets using Map Reduce programming framework.

So , at last I end up by saying that Youtube relies on a massive installation of Hadoop, a highly scalable open-source framework that uses clusters of low-cost servers to solve problems.