Elasticsearch, RethinkDB and the Semantic Web by Michel Dumontier.
From the post:
Everyone is handling big data nowadays, or at least, so it seems. Hadoop is very popular among the Big Data wranglers and it is often mentioned as the de facto solution. I have dabbled into working with Hadoop over the past years and found that: yes, it is very suitable for certain kinds of data mining/analysis and for those it provides high data crunching throughput, but, no, it cannot answer queries quickly and you cannot port every algorithm into Hadoop’s map/reduce paradigm. I have since turned to Elasticsearch and more recently to RethinkDB. It is a joy to work with the latter and it performs faceting just as well as Elasticsearch for the benchmark data that I used, but still permits me to carry out more complex data mining and analysis too.
The story here describes the data that I am working with a bit, it shows how it can be turned into a data format that both Elasticsearch and RethinkDB understand, how the data is being loaded and indexed, and finally, how to get some facets out of the systems.
Interesting post on biomedical data in RDF N-Quads format which is converted into JSON and then processed with ElasticSearch and RethinkDB.
I first saw this in a tweet by Joachim Baran.