Dear Reader, Deepak Vohra has published three NoSQL books. Please have a look if they could be of interest for you! NoSQL Web Development with Apache Cassandra
Pro Couchbase Development
Pro Couchbase Development: A NoSQL Platform for the Enterprise discusses programming for Couchbase using Java and scripting languages, querying and searching, handling migration, and integrating Couchbase with Hadoop, HDFS, and JSON. It also discusses migration from other NoSQL databases like MongoDB. http://www.apress.com/9781484214350?gtmf=s
Pro MongoDB Development
NCache .NET Open Source Distributed Cache. Written in C#. API .NET & Java. Query Parallel SQL Query, LINQ & Tags.Misc: Linear Scalability, High Availability, WAN Replication, GUI based Administration & Monitoring Tools, Messaging, Runtime Data Sharing, Cache & Item Level Events, Continuous Query & Custom Events, DB Dependencies & Expirations
TayzGrid Open Source In-Memory JCache compliant Data Grid. Written in Java. API Java, JCache JSR 107 & .NET.Query SQL & DB Synchronization. Misc: Linear Scalability, High Availability, WAN Replication, GUI based Administration & Monitoring Tools, Distributed Messaging, MapReduce, Entry Processor and Aggregator
Serenity database implements basic Redis commands and extends them with support of Consistent Cursors, ACID transactions, Stored procedures, etc. The database is designed to store data bigger then available RAM. Good luck!
A distributed database for many core devices. GPUdb leverages many core devices such as NVIDIA GPUs to provide an unparallelled parallel database experience. GPUdb is a scalable, distributed database with SQL-style query capability, capable of storing Big Data. Developers using the GPUdb API add data, and query the data with operations like select, group by, and join. GPUdb includes many operations not available in other "cloud database" offerings. Good luck! (thx MB for pointing me to this DB)
Please welcome BayesDB: http://probcomp.csail.mit.edu/bayesdb From the homepage: BayesDB, a Bayesian database table, lets users query the probable implications of their tabular data as easily as an SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries. Good luck!