Smoothing is everywhere. It is preprocessing for signal processing, it makes text segmentation work well, and it is used in a variety of programs to cull noise. However, there are a wide variety of ways to smooth data to achieve more appropriate predictions and lines of fit. This overview should help get you started.
When to Use Smoothing
The rather simple art of smoothing data is best performed when making predictions with temporal data where data comes from one or more potentially noisy sources (think a weather station with a wind speed monitor that is a bit loose or only partially unreliable) and dealing with tasks such converting digital sound to analogue waves for study. When a source appears capable of making decent predictions but is relatively noisy, smoothing helps. It is even used in fitting smoothing splines.
Types of Smoothing to Consider
Now that the rather blunt explanation is out of the way, the proceeding list is based on my own use with text mining and some sound data. I have tried to include the best resources I could for these.
However, do not rely on the matrix based calculation for OLS as the most efficient as gradient descent is clearly the winner. No self-respecting programmer will use a matrix implementation on large data sets. Spark contains an optimized gradient descent algorithm for distributed and even single node programming. The algorithm is tail recursive and seeks to minimize a cost function.
For the programmer or developer looking to distribute a Savitsky-Golay calculation and not using Spark gradient descent. Map partitions works well on the local areas. It also works well when smoothing many splines or for the Hanning and Hamming Window based smoothing.