SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world's leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!
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.
SLiP is a quick, alternative syntax for creating and editing XML data by hand and if you know Python, it should also be familiar. not my cup of tea but has a nice comparison of other lightweight xml notations