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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

"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."

"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."

"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 "

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