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If you need to convert files from one markup format into another, pandoc is your swiss-army knife. Pandoc can convert documents in markdown, reStructuredText, textile, HTML, DocBook, or LaTeX to:  * HTML formats: XHTML, HTML5, and HTML slide shows using Slidy, Slideous, S5, or DZSlides.  * Word processor formats: Microsoft Word docx, OpenOffice/LibreOffice ODT, OpenDocument XML  * Ebooks: EPUB version 2 or 3, FictionBook2  * Documentation formats: DocBook, GNU TexInfo, Groff man pages  * TeX formats: LaTeX, ConTeXt, LaTeX Beamer slides  * PDF via LaTeX  * Lightweight markup formats: Markdown, reStructuredText, AsciiDoc, MediaWiki markup, Emacs Org-Mode, Textile

"Protostuff is the stuff that leverages google's protobuf. A serialization library with built-in support for forward-backward compatibility (schema evolution) and validation. available formats: protostuff (native) graph (protostuff with support for cyclic references. See SerializingObjectGraphs) protobuf json smile (binary json useable from the protostuff-json module) xml yaml (ser only) kvp (binary uwsgi header) support for messages that are generated by the protostuff-compiler (java_bean) cyclic references via graph format see CompilerOptions for more customized compilation of .proto files support for existing pojos (See runtime schemas) cyclic references via graph format polymorphic (a nested message can be an interface/abstract class or even java.lang.Object) support for existing protoc-generated java messages see the io instructions for json, xml, yaml) no support for cyclic references (limitation of the builder pattern) Interoperability across various mobile platforms android kindle j2me (protostuff-me module) Transcoding support converts one encoding to another. See PipeUsage. Source and Sink protostuff, protobuf, json, json-numeric, smile, smile-numeric, xml Sink only yaml "

"HDF (Hierarchical Data Format) technologies are relevant when the data challenges being faced push the limits of what can be addressed by traditional database systems, XML documents, or in-house data formats. Leveraging the powerful HDF products and the expertise of The HDF Group, organizations realize substantial cost savings while solving challenges that seemed intractable using other data management technologies. Many HDF adopters have very large datasets, very fast access requirements, or very complex datasets. Others turn to HDF because it allows them to easily share data across a wide variety of computational platforms using applications written in different programming languages. Some use HDF to take advantage of the many open-source and commercial tools that understand HDF. Similar to XML documents, HDF files are self-describing and allow users to specify complex data relationships and dependencies. In contrast to XML documents, HDF files can contain binary data (in many representations) and allow direct access to parts of the file without first parsing the entire contents. HDF, not surprisingly, allows hierarchical data objects to be expressed in a very natural manner, in contrast to the tables of relational database. Whereas relational databases support tables, HDF supports n-dimensional datasets and each element in the dataset may itself be a complex object. Relational databases offer excellent support for queries based on field matching, but are not well-suited for sequentially processing all records in the database or for subsetting the data based on coordinate-style lookup."

"Now let's focus on Writes and Format in the details ;) "

"Configuration library for JVM languages. Overview implemented in plain Java with no dependencies extensive test coverage supports files in three formats: Java properties, JSON, and a human-friendly JSON superset merges multiple files across all formats can load from files, URLs, or classpath good support for "nesting" (treat any subtree of the config the same as the whole config) users can override the config with Java system properties, java -Dmyapp.foo.bar=10 supports configuring an app, with its framework and libraries, all from a single file such as application.conf parses duration and size settings, "512k" or "10 seconds" converts types, so if you ask for a boolean and the value is the string "yes", or you ask for a float and the value is an int, it will figure it out. JSON superset features: comments includes substitutions ("foo" : ${bar}, "foo" : Hello ${who}) properties-like notation (a.b=c) less noisy, more lenient syntax substitute environment variables This library limits itself to config files. If you want to load config from a database or something, you would need to write some custom code. The library has nice support for merging configurations so if you build one from a custom source it's easy to merge it in."

"Collection+JSON is a JSON-based read/write hypermedia-type designed to support management and querying of simple collections. It is similar to the The Atom Syndication Format (RFC4287) and the The Atom Publishing Protocol (RFC5023) . However, Collection+JSON defines both the format and the semantics in a single media type. It also includes support for Query Templates and expanded write support through the use of a Write Template."

"money and currency formatting for golang"

"Introduction In the modern era, software is commonly delivered as a service: called web apps, or software-as-a-service. The twelve-factor app is a methodology for building software-as-a-service apps that: Use declarative formats for setup automation, to minimize time and cost for new developers joining the project; Have a clean contract with the underlying operating system, offering maximum portability between execution environments; Are suitable for deployment on modern cloud platforms, obviating the need for servers and systems administration; Minimize divergence between development and production, enabling continuous deployment for maximum agility; And can scale up without significant changes to tooling, architecture, or development practices. The twelve-factor methodology can be applied to apps written in any programming language, and which use any combination of backing services (database, queue, memory cache, etc). Background The contributors to this document have been directly involved in the development and deployment of hundreds of apps, and indirectly witnessed the development, operation, and scaling of hundreds of thousands of apps via our work on the Heroku platform. This document synthesizes all of our experience and observations on a wide variety of software-as-a-service apps in the wild. It is a triangulation on ideal practices for app development, paying particular attention to the dynamics of the organic growth of an app over time, the dynamics of collaboration between developers working on the app's codebase, and avoiding the cost of software erosion. Our motivation is to raise awareness of some systemic problems we've seen in modern application development, to provide a shared vocabulary for discussing those problems, and to offer a set of broad conceptual solutions to those problems with accompanying terminology. The format is inspired by Martin Fowler's books Patterns of Enterprise Application Architecture and Refactoring. Who should

Bueno. Eso. Compartí el que me di cuenta que puso antes Pablo en vez del original por error, pero la idea entre ambos, si la obviedad es tolerable, es idéntica :) Está muy bien estructurado en cuanto que cada factor depende de los demás a la vez que los promueve. Permite un enfoque general que incluye prácticas de arquitectura - y de armado cotidiano de productos - que posibilitan llegar donde yo entiendo - según me voy enterando - que es el lugar a donde llegar. Sin embargo, creo que ni éste departamento en sus sistemas más nuevos cumple todos y cada uno de aquellos factores. Esto, lejos de ser una crítica, es una invitación para que revisemos si es el único método posible - cosa improbabilísima - o el mejor método - también bastante improblable - a seguir. Lo que sí sostengo como un absoluto - quien no lo haría - es que es un método practicable. Mi aporte mínimo es defenderlo como uno bueno.

"Optiq makes data anywhere, of any format, look like a database."

"Ning-compress is a Java library for encoding and decoding data in LZF format, compatible with standard C LZF package."

Highly efficient file backup system based on the git packfile format. Capable of doing *fast* incremental backups of virtual machine images.

"Graph for Scala is intended to provide basic graph functionality that seamlessly fits into the Scala standard collections library. Like the other members of scala.collection, Graph for Scala is an in-memory container that exposes a user-friendly interface without sacrificing functionality or flexibility. Graph for Scala also has ready-to-go implementations of JSON-Import/Export and Dot-Export - more popular graph serialization formats are coming soon. In addition, other powerful tools such as graph databases emulation and distributed graph processing are due to be supported."

"Shark is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones."

"This page contains tables of known HTML rel values from specifications, formats, proposals, brainstorms, and non-trivial POSH usage in the wild. In addition, dropped and rejected values are listed at the end for comprehensiveness. usage: see how is 'rel' used. Regarding rev, see: should 'rev' even be used. This page is also the official rel registry ([1][2]). Add new and proposed rel values to the following section: HTML5 link type extension "

"Go minifiers for web formats"

"Shark is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can answer Hive QL queries up to 100 times faster than Hive without modification to the existing data nor queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions."

"Extremely fast string formatting"

"The PayPal API uses HTTP verbs and a RESTful endpoint structure. OAuth 2.0 is used as the API Authorization framework. Request and response payloads are formatted as JSON."