A NoSQL database that perfectly suits a real-time app may not even function for traditional statistics. So both relational and NoSQL databases have valuable roles in data analytics.

The NoSQL taxonomy supports key-value stores, document store, BigTable, and graph databases. MongoDB, for example, uses a document model, which can be thought of as a row in a RDBMS.

A NoSQL database makes it simpler to add new fields for the entries that need them. Some NoSQL databases use a “document” model, where sets of keys and their values are grouped into documents.

Current enterprise data architectures include NoSQL databases co-existing with RDBMS. In this article, author discusses a solution for managing NoSQL & relational data using unified data modeling.

The Oracle NoSQL Database clearly came from a development team with experience in open source tradition. The only installation headache I had went away when I changed localhost to 127.0.0.1.

Another NoSQL benchmark was published in October 2012 when Cassandra, HBase, MongoDB, and Riak were compared. MySQL was also included in those tests for a reference against SQL technologies.

By contrast, a NoSQL database provides a more flexible data model, and it’s specifically built to scale across a vast number of machines. “With NoSQL, you get greater agility and scalability.

Key-value NoSQL databases are ideal for handling Web scale operations that need to scale across thousands of servers and millions of users with extremely quick and optimized retrieval, Forrester says.

Discover more about NoSQL Databases and how they help make Big Data analytics faster and easier. You’ll find more in Get To Know Relational and NoSQL Databases That Power Big Data Analytics.

From managing water meter data, through Bitcoin and video data, to web publishing, NoSQL database technology is finding real use cases. But it's likely to be stuck at 10% of the market for some time.