OrientDB has the flexibility of the Document databases and the power of the Graph databases to manage relationships. It can work in schema-less mode, schema-full or a mix of both. It can store up to 150,000 records per second on common hardware. OrientDB has been designed to be very fast. It inherits the best features and concepts from the Object Databases, Graph DBMS and the modern NoSQL engines. 
 Source code location: <A HREF="http://code.google.com/p/orient/" target="_blank">http://code.google.com/p/orient/</A>

OrientDB has the flexibility of the Document databases and the power of the Graph databases to manage relationships. It can work in schema-less mode, schema-full or a mix of both. It can store up to 150,000 records per second on common hardware. OrientDB has been designed to be very fast. It inherits the best features and concepts from the Object Databases, Graph DBMS and the modern NoSQL engines.

OrientDB - The NoSQL Graph-Document DBMS

MongoDB (from "humongous") is a scalable, high-performance, open source, dynamic-schema, document-oriented database. MongoDB bridges the gap between key-value stores (which are fast and highly scalable) and traditional RDBMS systems. MongoDB runs well on Amazon EC2. MongoDB uses BSON as the data storage and network transfer format for "documents". BSON is a binary encoded serialization of JSON-like documents. MongoDB could be accessed from programming language C, C++, Java, PHP, Python and Ruby. 
 It supports: 
 <UL>
	<LI>DBA operations from the shell</LI>
	<LI>Sharding</LI>
	<LI>Replication</LI>
	<LI>Security</LI>
	<LI>Backup</LI>
	<LI>Database Profiler</LI>
	<LI>Full Text Search</LI> 
 </UL>

MongoDB (from "humongous") is a scalable, high-performance, open source, dynamic-schema, document-oriented database. MongoDB bridges the gap between key-value stores (which are fast and highly scalable) and traditional RDBMS systems.

MongoDB - NoSQL Document Store Database

LokiJS is a document oriented database, Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. It runs in node (including cordova/phonegap and node-webkit) and the browser. It is ideal for client-side in-memory db is ideal (e.g., a session store), data sets loaded into a browser page and synchronised at the end of the work session etc.

LokiJS - A fast, in-memory document-oriented datastore for node.js, browser and cordova

Raven is an document database for the .NET/Windows platform. Raven offers a flexible data model design to fit the needs of real world systems. Raven stores schema-less JSON documents, allow you to define indexes using Linq queries and focus on low latency and high performance. Its features include:
 <ul><li>Scalable infrastructure: Raven builds on top of existing, proven and scalable infrastructure </li> <li>Simple Windows configuration: Raven is simple to setup and run on windows as either a service or IIS7 website </li> <li>Transactional: Raven support System.Transaction with ACID transactions. If you put data in it, that data is going to stay there </li> <li>Map/Reduce: Easily define map/reduce indexes with Linq queries </li> <li>.NET Client API: Raven comes with a fully functional .NET client API which implements Unit of Work and much more </li> <li>RESTful: Raven is built around a RESTful API </li> </ul>

Raven is an document database for the .NET/Windows platform. Raven offers a flexible data model design to fit the needs of real world systems. Raven stores schema-less JSON documents, allow you to define indexes using Linq queries and focus on low latency and high performance.

RavenDB - NoSQL Database .NET

Aerospike is a distributed, scalable NoSQL database. It provides support to create a high-performance, scalable platform that would meet the needs of today's web-scale applications. It supports operational efficiency, robustness and reliability expected from traditional databases.
 
It eliminates complex, costly caching layers commonly used in NoSQL and in-memory solutions. It 
delivers elastic, predictable performance at internet scale via patented Hybrid Memory Architecture.

Aerospike is a distributed, scalable NoSQL database. It provides support to create a high-performance, scalable platform that would meet the needs of today's web-scale applications. It supports operational efficiency, robustness and reliability expected from traditional databases.

Aerospike Database Server – Flash-optimized, in-memory, nosql database

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn. It supports JSON data model, Distributed joins, subqueries, aggregation, atomic updates, Hadoop-style map/reduce.
 
It is scalable, supports sharding and replication to multiple nodes. It takes care of machine failures and network interrupts.

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn. It supports JSON data model, Distributed joins, subqueries, aggregation, atomic updates, Hadoop-style map/reduce.

RethinkDB - Distributed JSON database

PouchDB is an open-source JavaScript database inspired by Apache CouchDB that is designed to run well within the browser. PouchDB was created to help web developers build applications that work as well offline as they do online. It enables applications to store data locally while offline, then synchronize it with CouchDB and compatible servers when the application is back online, keeping the user's data in sync no matter where they next login.

PouchDB - The Javascript Database that Syncs

Sensei is a distributed data system that was built to support many product initiatives at LinkedIn, including the real-time faceted search in <A HREF="http://www.linkedin.com/signal/" target="_blank">LinkedIn Signal</A> and the news feed and tabs on the Homepage. Sensei is both a search engine and a database. It is designed to query and navigate through documents that consist of unstructured text and well-formed and structured metadata. 
 It supports Fast realtime updates, Complex query language and REST/JSON api, Hadoop integration and lot more.

Sensei is a distributed data system that was built to support many product initiatives at LinkedIn, including the real-time faceted search in LinkedIn Signal and the news feed and tabs on the Homepage. Sensei is both a search engine and a database. It is designed to query and navigate through documents that consist of unstructured text and  well-formed and structured metadata. Sensei is both a search engine and a database.

SenseiDB - Distributed, Realtime, Semi-Structured Database from LinkedIn

WhiteDB is a lightweight NoSQL database library written in C, operating fully in main memory. There is no server process. Data is read and written directly from/to shared memory, no sockets are used between WhiteDB and the application program. It supports indexes (T-tree), concurrency through locking, persistence through logging and memory dumps, json, CSV and RDF support, easy to use in embedded systems and lot more.  

WhiteDB - NoSQL Memory Database in C

NoSql, JSON based, Document store database with compiled .net map functions and automatic hybrid bitmap indexing and LINQ query filters

RaptorDB - The Document Store

Bagri is a Document Database built on top of distributed cache solution like Hazelcast or Coherence. The system allows to process semi-structured schema-less documents and perform distributed queries on them in real-time. It scales horizontally very well with use of data sharding, when all documents are distributed evenly between distributed cache partitions. 
 
Started as a Native XML DB initially, the system has grown to process any kind of documents with self-describing data format (JSON, for instance) via pluggable parsers API. As a schema-free solution Bagri does not require any meta-data change when processing document formats evolve. An internal XQuery engine used for data querying and transformation provides exceeding flexibility in data processing tasks based on solid foundation of XQuery standards. The XQuery language is still evolving rapidly in answer to market demands which makes it the best candidate for all-in-one tool for processing of any volumes of semi-structured data.

Bagri is a Document Database built on top of distributed cache solution like Hazelcast or Coherence. The system allows to process semi-structured schema-less documents and perform distributed queries on them in real-time. It scales horizontally very well with use of data sharding, when all documents are distributed evenly between distributed cache partitions. 

Bagri -  XML/Document DB on top of distributed cache

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