Data scientists write analytics programs to look for solutions to business problems, like forecasting demand or setting an optimal price.
The continuous approach runs multiple stateless engines which concurrently enrich, aggregate, infer and act on the data.
So it is logical to conclude that their approach to writing software code does not enjoy the same efficiencies as the traditional programming team.
It also means saving the configuration of the big data cluster (sets of virtual machines) in some kind of repository as well.
That facilitates sending out analytics code and big data software and objects in the same automated way as the continuous integration process.