3 Major Decisions When Choosing Your Data Platform –

Most data architects need to make quick database platform decisions to support business applications. The applications teams notoriously fail to include the data team until late in the game, by which time deadlines have been set. This is an organizational problem that should be fixed, but meanwhile, data architects can still prepare themselves mentally and plan their process ahead of time.

Today there are new consequences of the database platform decision. Those who continually reach for the same hammer to fix every problem will soon find they are not aspiring to their larger role in the organization to improve the data maturity. Departments that never take the time to place a workload in the cloud or that never consider a non-relational solution are creating technical debt that will come back to haunt the shop and the data architect.

When considering the platform for a workload, there are now three major decisions (amid numerous other decisions you'll have to make). There used to be just one -- the data store itself. It used to be everything goes in a database, and largely the same database at that. Now, even the use of a database is very much in question, with file-based scale-out systems such as Hadoop and NoSQL providing immense utility for big data in particular.

The largest factor for distinguishing between databases and file-based scale-out system utilization is the data profile. The latter is best for data that fits the loose label of 'unstructured' (or semi-structured) data, while more traditional data -- and smaller volumes of all data -- still belong in a relational database.

You must also decide where to place your data store -- on-premises or in the cloud (and which cloud). In the past, the only clear choice for most organizations was on-premises data. However, the costs of scale are gnawing away at the notion that this remains the best approach for a data platform.