plists is a drop-in replacement for the Erlang module lists, making most list operations parallel. It can operate on each element in parallel, for IO-bound operations, on sublists in parallel, for taking advantage of multi-core machines with CPU-bound operations, and across erlang nodes, for parallizing inside a cluster. It handles errors and node failures. It can be configured, tuned, and tweaked to get optimal performance while minimizing overhead.
Disco is an open-source implementation of the Map-Reduce framework for distributed computing. As the original framework, Disco supports parallel computations over large data sets on unreliable cluster of computers.
The Disco core is written in Erlang, a functional language that is designed for building robust fault-tolerant distributed applications. 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.
Erlang + Python = complete beautifulness
MapReduce vs MySQL (speaker Stu Hood) Next Generation Data Storage with CouchDB (speaker: Jan Lehnardt)
Here’s a paper from Stanford showing how to use MapReduce to scalably implement ten different machine learning algorithms!
another thing to read