Scientists who analyze physical phenomena usually work on data coming from various sensors spread over the environment. Visualization of these data is a key issue in this process. In addition, these data are massive and provided at a very high rate. Under these conditions, it can be a real challenge to efficiently implement a reliable, dedicated, and itself efficient visualization tool, especially without the support of a large scale platform. In this paper, we propose to take up this challenge with FaST: a model-driven framework which makes it possible to generate a complete solution for the storage, the querying and the visualization of time series in a big data context. The specification of the solution is efficient thanks to a dedicated language intended for data scientists. It enables a simple description of both the architecture of the solution and the data it has to handle. Deployment is efficient thanks to code generation and server-side dockerization. The generated tool is efficient thanks to the implementation of ad hoc optimizations. On server-side, they come from pre-computation of views based on the Min-Max principle. On client-side, they come from the anticipation of queries related to the data navigation abilities of the generated tool. The underlying principles of FaST and its optimizations are detailed in this paper, as well as its implementation and its performance evaluation. It highlights a significant gain in execution time, with a limited database overhead.