Almost every human endeavour seems simple from a distance: even database deployment. Reality always comes as a shock, because the closer you get to any real task, the more you come to appreciate the skills that are necessary to accomplish it.

One of the big surprises I have when I attend developer conferences is to be told by experts how easy it is to take a database from development and turn it into a production system, and then implement the processes that allow it to be upgraded safely. Occasionally, I’ve been so puzzled that I’ve drawn the speakers to one side after the presentation to ask them for the details of how to do it so effortlessly, mentioning a few of the tricky aspects I’ve hit. Invariably, it soon becomes apparent from their answers that their experience, from which they’ve extrapolated, is of databases the size of a spreadsheet with no complicated interdependencies, compliance issues, security complications, high-availability mechanisms, agent tasks, alerting systems, complex partitioning, queuing, replication, downstream analysis dependencies and so on about which you, the readers, know more than I. At the vast international enterprise where I once worked in IT, we had a coded insult for such people: ‘They’ve catalogued their CD collection in a database’. Unfair, unkind, but even a huge well-used ‘Big Data’ database dealing in social media is a tame and docile creature compared with a heavily- used OLTP trading system where any downtime or bug means figures for losses where you have to count the trailing zeros. The former has unique problems, of course, but the two types of database are so different.

I wonder if the problem is one of language. Just as the English have fifty ways of describing rainfall, and the Inuit have many ways of describing pack ice, it is about time that we created the language for a variety of databases from a mild drizzle (‘It is a soft morning to be sure’) to a cloud-burst. Until anyone pontificating about the database lifecycle can give their audience an indication of the type of database they’re referring to, we will continue to suffer the sort of misunderstandings that so frustrate the development process. Though I’m totally convinced that the development culture should cross-pollinate far more with the science of IT operations, It will need more than a DevOps group-hug; it will require a change in the technical language so that it can accurately describe the rich variety of databases in operational use and their widely- varying requirements. The current friction is surely due more to misunderstandings on both sides, because it is so difficult to communicate these requirements. Any suggestions for suitable descriptive words for types of database?