We use postgresql for the merge between sql/nosql. A lot of our data is unstructured JSON, or JSON that is currently in flux due to some MVP/interation processes that are going on. PostgreSQL gives the capability to do this.

At the moment PostgreSQL on amazon is only at 9.5 which is one minor version down from support for document fragment updates which is something that we are waiting for. However, that may be some ways away.

Other than that, we are using PostgreSQL as our main SQL store as a replacement for all the MSSQL databases that we have. Not only does it have great support through RDS (small ops team), but it also has some great ways for us to migrate off RDS to managed EC2 instances down the line if we need to.

PostgreSQL combines the best aspects of traditional SQL databases such as reliability, consistent performance, transactions, querying power, etc. with the flexibility of schemaless noSQL systems that are all the rage these days. Through the powerful JSON column types and indexes, you can now have your cake and eat it too! PostgreSQL may seem a bit arcane and old fashioned at first, but the developers have clearly shown that they understand databases and the storage trends better than almost anyone else. It definitely deserves to be part of everyone's toolbox; when you find yourself needing rock solid performance, operational simplicity and reliability, reach for PostgresQL.

Relational data stores solve a lot of problems reasonably well. Postgres has some data types that are really handy such as spatial, json, and a plethora of useful dates and integers. It has good availability of indexing solutions, and is well-supported for both custom modifications as well as hosting options (I like Amazon's Postgres for RDS). I use HoneySQL for Clojure as a composable AST that translates reliably to SQL. I typically use JDBC on Clojure, usually via org.clojure/java.jdbc.

PostgreSQL is responsible for nearly all data storage, validation and integrity. We leverage constraints, functions and custom extensions to ensure we have only one source of truth for our data access rules and that those rules live as close to the data as possible. Call us crazy, but ORMs only lead to ruin and despair.

Tried MongoDB - early euphoria - later dread. Tried MySQL - not bad at all. Found PostgreSQL - will never go back. So much support for this it should be your first choice. Simple local (free) installation, and one-click setup in Heroku - lots of options in terms of pricing/performance combinations.

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.

As part of the cacheing system within Drupal.

Memcached mainly took care of creating and rebuilding the REST API cache once changes had been made within Drupal.

Distributed cache exposed through Google App Engine APIs; use to stage fresh data (incoming and recently processed) for faster access in data processing pipeline.

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

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

Memcache caches database results and articles, reducing overall DB load and allowing seamless DB maintenance during quiet periods.

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

Used to cache most used files for our clients. Connected with CloudFlare Railgun Optimizer.

Memcached is used as a simple page cache across the whole application.