From the Bayesian approach, data is seen as fixed. Once we have measured things, the values are fixed. On the other hand, parameters can be described by probability distributions. The probability distribution describes how much is known about a certain parameter. This description might change if we get new data, but the model itself will not change. There is lots of literature on this, and there is no rule of thumb for when to use frequentist or when to use Bayesian analysis.
For simple and fairly well-behaved data, I would say that the frequentist approach is fine when you need a quick estimate. To get more insights and for more constrained problems, that is, when we know more about our parameters and can estimate the prior distributions with more than a simple uniform prior, it is better to use the Bayesian approach. Due to the slightly more intuitive handling of things in Bayesian analysis, it is easier to build more complex models and answer complex questions.