Modal decomposition techniques are showing fast growth in popularity for their good properties as data-driven tools. There are many modal decomposition techniques, yet Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) are the most widespread methods, especially in the field of fluid dynamics. Following their outstanding performance on various applications in several fields, numerous extensions of these techniques have been developed. In this work we present an ambitious review comparing eight different modal decomposition techniques, including most established methods, i.e. POD, DMD and Fast Fourier Transform (FFT); extensions of these classical methods: based either on time embedding systems, Spectral POD (SPOD) and Higher Order DMD (HODMD), or based on scales separation, multi-scale POD (mPOD), multi-resolution DMD (mrDMD); and also a method based on properties of the resolvent operator, the data-driven Resolvent Analysis (RA). The performance of all these techniques will be evaluated on four different testcases: the laminar wake around cylinder, a turbulent jet flow, the three dimensional wake around a cylinder in transient regime and a transient and turbulent wake around a cylinder. All these mentioned datasets are publicly available. First, we show a comparison between the performance of the eight modal decomposition techniques when the datasets are shortened. Next, all the results obtained will be explained in detail, showing both the conveniences and inconveniences of all the methods under investigation depending on the type of application and the final goal (reconstruction or identification of the flow physics).