CouchDB

CouchDB fulfills a nice problem for us: we use it to store time-series data for a test sensor network, and it is a great introduction to non-SQL concepts and technologies. After performing feasibility analysis on a few projects looking for a good, solid no-SQL solution, several platforms were examined, including MongoDB. However, for this particular project, MongoDB was simply too large for the Job, and no one on the staff had much experience with non-SQL DBMS's. So, we needed a small, powerful database that worked out of the box, and was easy to understand and therefore implement. The value proposition of CouchDB is its simplicity: It's easy to setup and configure and therefore easy to delegate to an IT analyst, and not worry about blowing project funds on hiring outside talent (usually). The Second proposition of CouchDB is that it is developer friendly: you don't need a salesman from XYZ, Corp. to pitch your development team(s) multi-grand licensing deals just to get it up and running: it's open source and works on all of the big platforms (i.e. OSes).

Currently, we use CouchDB in various projects throughout our Big Data Insight Product Division for development and production environment setup. Mainly it is being used to store serialized (JSON formatted) unstructured data. For our development environment, usually, a single node DB is more than sufficient for prototyping and to deliver Minimum Viable Product (MVP). However, for production setup, which demands High Availability (HA), a cluster DB with replication capability is a must.

It's being used as a document store for a social media analytics system, dumping thousands of updates an hour into a map/reduce system that generates reports and feeds into other task-specific databases. It's a more flexible alternative to relational databases like MySQL, and is easier to scale due to master-master replication.

Datawhere provides a file intelligence platform that helps people and companies find digital assets ("file") across platforms, devices, and geographic barriers. Our customers are mostly in media-oriented companies - advertising, film, visual effects, post-production, etc., - that have large numbers of distributed, rapidly changing file sets that are touched by many users.

We use CouchDB + Logstash + Elasticsearch to provide incredibly fast, powerful "search and do stuff" functionality for our customers. And they love it!

It held a million SERP pages gathered each day. That information was then parsed to find ads on each page. Building the views and actually deleting files with a compact took ages. On a large AWS server, it took about a day to delete information for one day which has to happen every 6 months. We replaced CouchDB a year ago with AWS S3 and S3 has been amazing. We keep track of the metadata to pull S3 files in our own data base.

It is being used within all the mobile applications we develop where offline data is needed. The ability for CouchDB to sync is truly amazing and saved us lots of headaches and heartache.

There are multiple legacy applications which use SQL databases as a backend for services which are invoked from POS systems. We managed to migrate these applications to CouchDB which provided a faster response.

I am using CouchDB as the main NoSQL information database server for our product, a globally used network testing and security product. CouchDB helps us save and access thousands of documents of crucial information representing the data points and meta data of the product activities.

CouchDB was used for data replication across different audiences and when users were in offline mode (no/low internet). It was initially used as a proof of concept with an intent to use across mobile apps.

CouchDB is corporate wide solution used for:

• route planning for sales force
• a central farm and mobile devices replicating the content partitioned per user