Overall Satisfaction with Couchbase Server
As a consultant, I stay up to date on the latest Couchbase Server news and features. By my organization, it is strictly being evaluated as a arguably better NoSQL database alternative to MongoDB. As a consultant at Avalon Consulting, LLC., I helped implement 2 benchmarks comparing the major Couchbase releases of 4.0 and 4.5, which introduced the new N1QL functionality.
- Scaling - By adding more hardware to the Couchbase Server cluster, one can quickly benefit from the overall increase in performance of the system
- Mobile Synchronization - As a mobile developer, keeping data in sync between an embedded SQLite database and a RDBMS system takes more effort and thought than necessary and reduces time spent developing the rest of the application. Couchbase Server paired with the Sync Gateway and Couchbase Mobile significantly simplifies those transactions.
- Hands-on documentation via Classes, Seminars, and Tutorials - The free videos on Couchbase's YouTube channel and the courses and seminars that are offered cover much of the basics needed to understand how to quickly get started with Couchbase Server and the features it provides.
- No enforced schema policy - While this is true of most NoSQL technologies, the main benefit that I see Couchbase has is that they have recommended patterns for designing stored data.
- Written documentation - Overall, navigating the documentation site is difficult. There have been several times when I try to search for something and find outdated code references or articles.
- As a Consultant, one needs to stay up-to-date on latest news in their industry and be ready to support breaking features and discuss ideas related to implementation. Couchbase provides many resources to assist people get up and running with Couchbase Server
I would recommend Couchbase Server to someone looking for a quick, scalable NoSQL database with some additional useful features such as Mobile Synchronization, and SQL query support. In addition to that, there are connectors to Elasticsearch, which make it useful in search analytics, and Kafka, which make it useful in a Big Data / Hadoop environment. While the examples and tutorials are plentiful, the online written documentation is difficult to navigate.