The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.

Document (JSON) DB.

• - queries must be pre-defined as views (not as flexible as query formulation on the fly)
• - community and ecosystem not as large as mongodb
• + PouchDB is an excellent JS library to interact with CouchDB or even work in offline-then-sync moce

in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).

By being built on, of, in and around CouchDB, Smileupps offers to its customers secure and reliable CouchDB hosting and a CouchDB-based app store to build and sell serious business-enabled web applications

The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...

Importing/Exporting data, interpreting results. Possible integration with SAS

TBD. Good to have I think. Analytics on loads of data, recommendations?

We use CouchDB in an internal analysis tool for usage data.

Storage for unstructured, linked and timeseries data.

json store + geo + _changes