Document Oriented Database

        Designed for storing, retrieving, and managing document-oriented, or semi structured data, information. Document-oriented databases are one of the main categories of so-called NoSQL databases and the popularity of the term "document-oriented database" (or "document store") has grown with the use of the term NoSQL itself. In contrast to well-known relational databases and their notions of "Relations" (or "Tables"), these systems are designed around an abstract notion of a "Document".           
 At the highest level of organization, it is quite similar to a relational database, but as you get closer to the data itself, you will notice a significant change in the way the data is stored. Instead of databases, tables, columns, and rows you have documents. A document doesn’t have a direct correlation in the relational world. Documents do not have a predefined schema like relational database tables. A document is partly a row, in that it’s where the data is located, but it's also part columns, in that the schema is defined in each document (not table-wide).

          The best way to think of a document is as a multidimensional array. In an array, you have a set of keys that map to values. The values could themselves be another array. Documents map extremely well to objects and other PHP data types like arrays and even multidimensional arrays. Documents encapsulate and encode data (or information) in some standard formats or encoding. Encoding in use include XML, YAML, JSON, and BSON, as well as binary forms like PDF and Microsoft Office documents.

           Currently lot of web based online shopping websites are moving to this kind of databases. Simple and amaizing handling with database solutions like MongoDB, Apache CouchDB , Clusterpoint etc