Applying Dynamic Time Warping (DTW) instead of Euclidean Distance for Clustering Synchronized Time series data I am trying to cluster members based on hourly login data. As this is mostly synchronized, I first applied Euclidean and it failed to cluster them into groups with sensible patterns. I tried DTW thinking it might be more accurate for time series, it did show some sensible patterns.
My question is, can we apply DTW for data that have the same length and is synchronous instead of Euclidean in K mean? Are there any issues with using DTW for synchronized time series data?

Yes - if the time series are the same length and aligned, DTW will give you the same result as the Euclidean distance (ED).
There are two possible issues I can think of with using DTW. Firstly, in some time series it may not make sense to allow warping - these small mis-alignments may be important, so as always it pays to check the algorithm is appropriate for the data.
Secondly, DTW is computationally expensive for long time series, especially if you use unconstrained DTW. Although there are ways of speeding it up, a naïve implementation has complexity of $\mathrm O(n^2)$ (assuming the time series are the same length) compared to $\mathrm O(n)$ for ED, where $n$ is the length of the time series. While most implementations will be faster than that, DTW will still be slower than ED.
