Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. Similar to HDFS, Hadoop MapReduce can also be executed even in commodity hardware, and assumes that nodes can fail anytime and still […]
HDFS – Hadoop Distributed File System Architecture Tutorial
HDFS (Hadoop Distributed File System) is where big data is stored. Primary objective of HDFS is to store data reliably even in the presence of failures including Name Node failures, Data Node failures and/or network partitions (‘P’ in CAP theorem). This tutorial aims to look into different components involved into implementation of HDFS into distributed […]
Brewer’s CAP Theorem in Simple Words
When you start discussing distributed architecture, there is high possibility that you will encounter this CAP theory (or Brewer’s theorem). CAP stands for Consistency, Availability and Partition tolerance. It wants system designers to make a choice between above three competing guarantees in final design. It’s said that achieving all 3 in system is not possible, […]
Hadoop – Big Data Tutorial
In this hadoop tutorial, I will be discussing the need of big data technologies, the problems they intend to solve and some information around involved technologies and frameworks. Table of Contents How really big is Big Data? Characteristics Of Big Data Systems How Google solved the Big Data problem? Evolution of Hadoop Apache Hadoop Distribution […]