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.

We build queries in PHP with DSQL that work with SQLite. We also have SQLite data controller, so that you can build SQLite-based models.

Used during the "build process" of Coolfront Mobile's Flat rate search engine database. Flat rate data that resides in Salesforce is transformed using SQLite into a format that is usable for our mobile Flat rate search engine (AKA: Charlie).

RDBTools is a self-hosted application, and it is important that the installation process is simple. With SQLite, we create a new database file for every analysis. Once the analysis is done, the SQLite file can be thrown away easily.

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...

All the dynamic data (i.e.: jobs) is stored in a simple SQLite database.

Все динамические данные (вакансии) хранятся в простой SQLite БД.

There's really no call for something heavier for this site. SQLite is simple, easy to use and quite reliable given its age.

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

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