In conventional relational databases, a field has to exist for each piece of information—and in every entry. If the information is not available, the cell is kept empty, but it must still exist. Document-oriented databases are much more flexible: the structure of individual documents does not have to be consistent. Even large volumes of unstructured data can be accommodated in the database.
Plus, it’s easier to integrate new information. While in the case of a relational database a new information criterion must be added to all datasets, the new information only needs to be included in just a few datasets in a document store. The additional content can be added to further documents, but it’s not required.
Moreover, with document stores the information is not distributed over multiple linked tables. Everything is contained in a single location, and this can result in better performance. However, this speed advantage is only realized in document databases so long as you don’t attempt to use relational elements: references don’t really suit the concept of document stores. If you do try to interlink the documents, the system will become highly complex and cumbersome. So, a relational database system is more advisable for highly networked data volumes.