PUBLIC MARKS with tags api & framework

GeoREST is a web-centric framework for distributing geospatial data. It allows RESTful feature-based access to spatial data sources, including full editing capabilities, through a MapGuide server or directly via FDO.

FRAPI is a high-level API framework that puts the “rest” back into RESTful. Use it to power your web apps, mobile services, and legacy systems.

Backbone supplies structure to JavaScript-heavy applications by providing models with key-value binding and custom events, collections with a rich API of enumerable functions, views with declarative event handling, and connects it all to your existing application over a RESTful JSON interface.

"CloudFusion is a fast, easy-to-use toolkit for the world's most popular cloud computing services."

"I think that the fundamental problem is that using the servlet API is the only way you can plug with the HTTP layer of a JEE server. Perhaps the java ecosystem needs something like Rack. I mean, a real low level and portable API to deal with HTTP. Not an half assed HTTP API built on some flawed architectural principles."

The Open Web Analytics (OWA) framework provides a generic set of PHP and HTTP APIs that application developers can use to integrate web analytics into any application. The Framework also has built-in support for popular web applications such as WordPress and MediaWiki.

FlexMonkey is a testing framework for Flex apps that provides for the capture, replay and verification of Flex UI functionality. FlexMonkey can record and playback Flex UI interactions, and generates ActionScript-based testing scripts that can easily be included within a continuous integration process. It uses the Flex Automation API and was created by extending Adobe's sample automation adapter, AutoQuick.

Gisgraphy is a free and open source framework that provides fulltext search and find nearby services for places on earth (aka : toponyms) from several databases (aka : gazeteers) on the Web (mainly Geonames, but not only : ESRI for instance). It provides an importer to inject the data into a strongly typed Postgres / Postgis database and use them via 2 webservices or a java API : geolocalisation and fulltext in many format (XML, json, PHP, ruby, python, Atom, RSS / GeoRSS).

Opsview is enterprise network and application monitoring software designed for scalability, flexibility and ease of use. Opsview has been in development since 2003 and is released under the GNU GPL license. Current version is 2.12. Opsview is a fully integrated monitoring tool that incorporates popular Open Source software including Nagios, Net-SNMP and RRDtool. The Catalyst web framework provides an extensible monitoring and configuration user interface. Opsview software is supported on Linux (Debian, CentOS, RHEL and Ubuntu) and Solaris 10. It will monitor all common operating systems including Windows. Opsview extends the capabilities of Nagios in the following ways: * Distributed monitoring with high availability and fail-over * Much improved SNMP support with trap processing with rules engine * API supporting automation of Opsview configuration * Data warehouse for storage of historical performance and event data * Opsview Reports customisable reporting * Powerful configuration and management UI * Extended monitoring UI * Extensible architecture based on Catalyst Web Framework and Altinity middleware software

MooTools is a compact, modular, Object-Oriented JavaScript framework designed for the intermediate to advanced JavaScript developer. It allows you to write powerful, flexible, and cross-browser code with its elegant, well documented, and coherent API.

orange frameworks

Even though the Hadoop framework is written in Java, programs for Hadoop need not to be coded in Java but can also be developed in other languages like Python or C (the latter since version 0.14.1). However, the documentation and the most prominent Python example on the Hadoop home page could make you think that you must translate your Python code using Jython into a Java jar file. Obviously, this is not very convenient and can even be problematic if you depend on Python features not provided by Jython. Another issue of the Jython approach is the overhead of writing your Python program in such a way that it can interact with Hadoop - just have a look at the example in ${HADOOP_INSTALL}/src/examples/python/WordCount.py and you see what I mean. I still recommend to have at least a look at the Jython approach and maybe even at the new C MapReduce API called Pipes, it's really interesting. Having that said, the ground is prepared for the purpose of this tutorial: writing a Hadoop MapReduce program in a more Pythonic way, i.e. in a way you should be familiar with.