今回は、 Hadoopの構成要素である並列データ処理フレームワークMapReduceにおける実装アーキテクチャの特徴について解説します。加えて、 類似のシステムである並列データベースを取り上げ、 想定するワークロードなどの違いについて解説します。 Apache ...

第一部では、 Hadoopなどの並列データ処理系の基礎である並列データベース技術や分散システム技術を解説してきました。第二部では、 実際の処理系により焦点を当て、 それらの設計と実装を見ていきます。 第二部では、 最初の4回を用いて、 Apache Hadoopの ...

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MapReduce was invented by Google in 2004, made into the Hadoop open source project by Yahoo! in 2007, and now is being used increasingly as a massively parallel data processing engine for Big Data.

Scientists and mathematicians have long loved Python as a vehicle for working with data and automation. Python has not lacked for libraries such as Hadoopy or Pydoop to work with Hadoop, but those ...

When the Big Data moniker is applied to a discussion, it’s often assumed that Hadoop is, or should be, involved. But perhaps that’s just doctrinaire. Hadoop, at its core, consists of HDFS (the Hadoop ...

When your data and work grow, and you still want to produce results in a timely manner, you start to think big. Your one beefy server reaches its limits. You need a way to spread your work across many ...

What are some of the cool things in the 2.0 release of Hadoop? To start, how about a revamped MapReduce? And what would you think of a high availability (HA) implementation of the Hadoop Distributed ...

Google and its MapReduce framework may rule the roost when it comes to massive-scale data processing, but there’s still plenty of that goodness to go around. This article gets you started with Hadoop, ...

Did you know that 90% of the world’s data has been created in the last two years alone? With such an overwhelming influx of information, businesses are constantly seeking efficient ways to manage and ...