Methods of Smart Alarming intend to detect as soon as possible novelty or anomaly in Data Streams. A review is proposed to highlight the key points of using them. In case of univariate data, the more suitable method is not the same as for stationary variable or non-stationary variable. Multivariate data set are often dealt with using unsupervised learning based methods, either with fac-tor analysis (mostly PCA) or clustering algorithms. Each of these methods must be applied in a spe-cific situation: prior knowledge of possible anomalies should be needed or not, learning data set can be large sized or not, and so on. Some examples are outlined. Discussion underlines the importance of having a prior knowledge of variable behaviour, and to consider the global flow chart, including eventually a data preprocessing.