Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties: * Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. * Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically * Extensibility.
Disco is an oss implementation of the Map-Reduce framework for distributed computing. Disco supports parallel computations over large data sets on unreliable cluster of computers. The Disco core is written in Erlang. Users of Disco typically write jobs in Python, which makes it possible to express even complex algorithms or data processing tasks often only in tens of lines of code. This means that you can quickly write scripts to process massive amounts of data. Disco was started at Nokia Research Center as a lightweight framework for rapid scripting of distributed data processing tasks. This far Disco has been succesfully used, for instance, in parsing and reformatting data, data clustering, probabilistic modelling, data mining, full-text indexing, and log analysis with hundreds of gigabytes of real-world data. Linux is the only supported platform but you can run Disco in the Amazon's Elastic Computing Cloud.