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Storing (and querying) RDF in NoSQL database managers. Interesting progress, carefully measured. "...we are confident that NoSQL databases will present an ever growing opportunity to store and manage RDF data in the cloud. " A little over a year ago, in a blog entry titled SPARQL and Big Data (and NoSQL), I wrote this: What I'd love to see, and have heard about tentative steps toward, would be SPARQL endpoints for some of these NoSQL database systems.

The D2RQ and R2RML work have accomplished things that should be easier for graph-oriented NoSQL databases like Neo4J and, if I understand the quote above [from Edd Dumbill's Planning for Big Data] correctly, for column-oriented NoSQL databases as well. Google searches on SPARQL and either Hadoop, Neo4J, HBase, or Cassandra show that some people have been discussing and even doing a bit of coding on several of these. This work is, to the best of our knowledge, the first systematic attempt at characterizing and comparing NoSQL stores for RDF processing. Please add any comments to this Google+ post. Soirée Couchbase. Hibernate OGM. MongoGraph Brings Semantic Web Features to MongoDB Developers. MongoGraph from AllegroGraph team brings semantic web features to MongoDB developers. They implemented a MongoDB interface to AllegroGraph database to give Javascript programmers both joins and the semantic web capabilities. Using this approach JSON objects are automatically translated into triples and both the MongoDB query language and SPARQL work against these objects.

Another goal of MongoGraph is to make the freetext engine of their graph database easy to search as Solr/Lucene. AllegoGraph CEO Jans Aasman gave a presentation and talked about working on the level of objects instead of individual triples. InfoQ spoke with Jans about this new approach and how it helps the NoSQL developers. Infoq: What are the advantages of representing JSON objects as RDF triples in a graph database? Jans: Well, the most direct answer is that you can use JSON to model complex schemas and then perform complicated joins over your data without writing map-reduce queries. The NOSQL Tapes. Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase comparison :: KKovacs. While SQL databases are insanely useful tools, their monopoly in the last decades is coming to an end. And it's just time: I can't even count the things that were forced into relational databases, but never really fitted them.

(That being said, relational databases will always be the best for the stuff that has relations.) But, the differences between NoSQL databases are much bigger than ever was between one SQL database and another. This means that it is a bigger responsibility on software architects to choose the appropriate one for a project right at the beginning. In this light, here is a comparison of Open Source NOSQL databases: The most popular ones # Redis # Best used: For rapidly changing data with a foreseeable database size (should fit mostly in memory). For example: To store real-time stock prices. Cassandra # Best used: When you need to store data so huge that it doesn't fit on server, but still want a friendly familiar interface to it. MongoDB # ElasticSearch # CouchDB # Accumulo # Redis.