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

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

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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OrientDB - The NoSQL Graph-Document DBMS

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OrientDB - The NoSQL Graph-Document DBMS

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

Bagri - XML/Document DB on top of distributed cache

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