Filter size vs. FFT size and Overlap add First of all I would like to say that :

I'm new to DSP and in a &quot;learning curve&quot; hence the &quot;basicness&quot; of my questions.
I've read as many posts/articles as possible on the internet but still need &quot;guidance&quot;.
I'm french hence the bad formulation sometimes.

My mission/data context :
Performing data analysis on real data sets (oil-rig data).
Sometimes data collected on the rig present low frequency component due to rig movements.
Data are collected every 5 seconds so the sampling rate is 0.2 Hz. The size of the data to be analysed will be 1024/2048/4096 points let's call it N is this post.
My mission is to perform a filtering on these data to eliminate the low frequency component.
This is not a real time application. The range of data to be analysed will be chosen and then the analysis performed.
Chosen approach :
Process these data with a FFT, filtering the FFT and perform an IFFT to obtain and clean(er) set of data. Basically the lowest frequency found in the FFT is to be eliminated.
Many users suggested me to use the overlap-add method to obtain the desired result.
First I was thinking of just zeroing low frequency related bins in my FFT but my readings on the subject suggested to rather use a high pass filtering (with spectral inverted windowed-sinc).
Designing this filter and applying it is what I post here for because it turned out to be way more difficult than expected.
Questions :
Do I have to use the overlap-add method considering that my application is not a real time one ?
For what I have understood I need to design a high pass filter kernel with a spectral inverted sinc function. Then I have to perform a FFT on my filter kernel and multiply points by points my filter FFT(1) coefficients with my data FFT(2) coefficient before performing a IFFT on the resulting FFT. Am I correct on this one ?

According to my readings I can obtain a spectral inverted sinc function by inverting (-sin(x)/x) it and adding 1 to the center of symmetry. I am right, do you guys confirm ? Do I add 1 on the one center point only ?

What is the right size to choose for my filter kernel ? Is it the same size N as my data ? As I have to multiply my FFT(1) with my FFT(2) I would think that I have to choose the same size but I'm probably wrong on this one and the overlap add method may be the answer.

The kernel filter designing is tricky, I don't know where to place the central lobe of my spectral inverted sinc, in 0, on a specific offset in the time domain ?
For your non-real-time application, you do not need OS or OA.  Direct form convolution (such as Octave function conv()) is easy and gives the same result.
But since the title of your post is &quot;Filter size vs. FFT size and Overlap add&quot;, this graphic directly addresses that issue.