Dream. Dare. Do – that is Suyati’s work principle in a nutshell.
Traditional relational databases are inadequate for big data. In fact, NoSQL databases, with looser consistency models, work better. Such databases are highly scalable, and even allow horizontal scalability. They are flexible as well, allowing growing or changing schemas with remarkable ease.
Initially, Couchbase had the limitation of the database storing the JSON data as a single entity, making it effectively a key/value database. However, Couchbase Server 2.0, released in December 2012 removes this shortcoming and makes it a full-blown “document database.” The Couchbase Management GUI offers easy to use GUI consoles for management, for which Mongo DB has no answer. The management in MongoDB is through command lines.
However, MongoDB has been quick to step up to the challenge. Unlike Couchbase, MongoDB has always been a document database. MongoDB also uses Binary JSON (BSON). BJSON is a superset of JSON and defines more data types compared to the latter. MongoDB 2.4, released in March 2013, incorporates many performance and usability enhancements. MongoDB 2.4 handles documents better than Couchbase. It is possible to undertake both management and development activities on a MongoDB database. The shell hosts collections, documents, and databases as first-class entities.
The cloud-based MongoDB Monitoring Service (MMS) not only gathers statistics, but also offers seamless connectivity between the abstracted data objects found in the mongo shell and the modelled entitles of the database. This has many uses. For instance, it becomes possible to create an index based on a specific document field, with a single function call. Creating indexes in Couchbase require more complex mapreduce operations.
The bottomline: richer querying and indexing options, combined with superior ease of use, allow MongoDB to ward off the threat thrown up by the Couchbase Server.