Large scale data analysis made easy - Apache Hadoop
Channel: FOSDEM
Date: 2010-02-14

By Isabel Drost

The goal of Apache Hadoop is to make large scale data analysis easy. Hadoop implements a distributed filesystem based on the dieas behind GFS, the Google File System. With Map/Reduce it provides an easy way to implement parallel algorithms.

Storage has become ever cheaper in recent years. Currently one terabyte of harddisk space costs less than 100 Euros. As a result a growing number of businesses have started collecting and digitizing data: Custumer transaction logs, news articles published over decades, crawls of parts o f the world wide web are only few use cases that produce large amounts of data. But with petabytes of data at your fingertips the question of how to make ad-hoc as well as continuous processing efficient arises.

The goal of Apache Hadoop is to make large scale data analysis easy. Hadoop implements a distributed filesystem based on the dieas behind GFS, the Google File System. With Map/Reduce it provides an easy way to implement parallel algorithms.

After motivating the neeed for a distributed library the talk gives an introduction to Hadoop detailing its strengths and weaknesses. It gives an introduction on how to quickly get your own Map/Reduce jobs up and running. The talk closes with an overview of the Hadoop ecosystem.

FOSDEM (Free and Open Source Development European Meeting) is a European event centered around Free and Open Source software development. It is aimed at developers and all interested in the Free and Open Source news in the world. Its goals are to enable developers to meet and to promote the awareness and use of free and open source software. More info at http://fosdem.org