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Atualizado em : May 22, 2013
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
May 2013
Experimente ? Vale a pena ir atrás. É importante entender como desenvolver essa capacidade. As empresas devem experimentar esta tecnologia em um projeto que possa lidar com o risco.
Hadoop continues to be the most popular framework to develop distributed data-processing applications. Although programming Hadoop applications in Java is not particularly difficult, designing efficient MapReduce pipelines does require a good amount of experience. Apache Pig simplifies Hadoop development by offering a high level language, called Pig Latin, and an execution runtime. Pig Latin is procedural and provides a SQL-like interface to work with large datasets. The execution infrastructure compiles Pig Latin into an optimized sequence of MapReduce programs that run on the cluster. Pig Latin is extensible through user-defined functions in different languages such as Ruby, JavaScript, Python and Java.
Oct 2012
Experimente ? Vale a pena ir atrás. É importante entender como desenvolver essa capacidade. As empresas devem experimentar esta tecnologia em um projeto que possa lidar com o risco.
publicado : Oct 22, 2012
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