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.

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).

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...

High-speed update-aware storage used in our region server infrastructure; provides a good middle layer for storage of rapidly modified information.

Main database, using it in multiple datacenters in an active-active configuration.

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

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

Angel includes support for multiple databases, out-of-the-box.

As a boring document oriented database with safe defaults.

Sharded and replicated storage, NoSQL with joins