To be a relational database the database must meet a very specific set of requirements. While a standard view of the databases from the DB administrators and normal users view may allow limited ways to manipulate the data, looking at the actual storage structures and how to efficiently use the resultant files can provide some extremely efficient computational methods. For example, if one structures data in a relational database with few fields, perhaps as low as two, per representation where each representation represents a single file then the data read directly from the file, the reads may be via flat file techniques, into an array or efficient storage table, then you achieve the best of both worlds.
If the above example requires compressed data, then the relational data must be read from a single file via DB operations with two different mappings overlaying the single database file. The programmer must understand the data orientation so that compression/decompression occurs correctly. Extremely fast reads/writes may be achieved this way.
The advantage of a column oriented database is that the files are inherently optimized for data mining without the need to hire an expensive programmer. If the company had multiple requirements for the same data then multiple databases may be required, unless they are willing to hire the expensive programmer. A key problem results when multiple databases loose synchronization.
BTW, row based databases optimized for storage size (footprint) compress data by column, not by row or record. Using these techniques, I have achieved far greater data compression than comparable Google stored data.