One of the compelling features of Yahoo Pipes for me is the way the the user interface encourages you think of programming in terms of pipelines and feeds, in which a bundle of stuff (RSS feed, CSV data, or whatever) is processed in a sequence of steps (the pipeline), with each step being applied to each item in the feed.
A few days ago I blogged about pipe2py, a toolkit from Greg Gaughan that lets you “compile” a simple Yahoo pipe into a Python code equivalent programme (Yahoo Pipes Code Generator (Python)). Given that, in general, I don’t believe the “build it and they will come” mantra, I spent half an hour or so this morning looking round the web for people who had posted queries about how to generate code equivalents of Yahoo Pipes, so that I could point them to pipe2py.
In doing so, I came across a couple of other visual pipeline environments that are maybe worth looking at in a little more detail.
PyF is a “[flow based] open source Python programming framework and platform dedicated to large data processing, mining, transforming, reporting and more.”
On the other hand, Orange claims to offer “[o]pen source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Extensions for bioinformatics and text mining. Packed with features for data analytics.”
Here’s one of their promo shots:
I haven’t had a chance to play with either of these environments – and probably won’t for a little time yet – so whilst I feel like I’m cheating by posting about them in such a cursory way without having even a simple demo to show, they’re maybe of interest to anyone who stumbles across this blog by way of pipe2py… [Update: my Orange Visualisation tool review).]
PS as well as PyF, see also: Pypes [via @dartdog]