A document-oriented database is a designed for storing, retrieving, and managing document-oriented, or semi structured data. Document-oriented databases are one of the main categories of NoSQL databases.
The central concept of a document-oriented database is the notion of a Document. While each document-oriented database implementation differs on the details of this definition, in general, they all assume documents encapsulate and encode data (or information) in some standard format(s) (or encoding(s)).
Encodings in use include XML, YAML, JSON and BSON, as well as binary forms like PDF and Microsoft Office documents (MS Word, Excel, and so on).
MongoDB is a collection-oriented, schema-free document database. Data is grouped into sets that are called ‘collections’. Each collection has a unique name in the database, and can contain an unlimited number of documents. Collections are analogous to tables in a RDBMS, except that they don’t have any defined schema.
It store data (which is in BASON – “Binary Serialized dOcument Notation” format) that is a structured collection of key-value pairs, where keys are strings, and values are any of a rich set of data types, including arrays and documents.
Quick Start: http://www.mongodb.org/display/DOCS/Quickstart
Quick Start: http://couchdb.apache.org/docs/intro.html
Terrastore is a modern document store which provides advanced scalability and elasticity features without sacrificing consistency. It is based on Terracotta, so it relies on an industry-proven, fast clustering technology.
Quick Start: http://code.google.com/p/terrastore/wiki/Documentation
Raven is a .NET Linq enabled Document Database, focused on providing high performance, schema-less, flexible and scalable NoSQL data store for the .NET and Windows platforms.
Raven store any JSON document inside the database. It is schema-less database where you can define indexes using C#’s Linq syntax.
Quick Start: http://ravendb.net/tutorials
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