Filtering of streaming audio Given something like 103 data points ($N=103$), a DFT will give back 103 frequency values. Then to do something like filtering the high frequencies involves setting the high frequency values from the DFT to zero, and doing the inverse DFT to get back 103 data points that represent the original signal without the high frequencies.

This makes sense to me given all 103 data points all at once. But what about streaming an audio WAV file that is rather large (say for example $N=10^5$). If one wanted to filter the high frequencies then the approach i just described on the entire data of $10^5$ points logically makes sense. But that is not reasonable when streaming a WAV file for playback. What is done to high frequency filter a streaming playback of an audio file?
FFT -> zeroing coefficients -> IFFT is not the correct way of doing filtering - the actual filter realized by doing so has poor characteristics.

The correct way of filtering signals is to compute the coefficients of a digital filter, a process known as filter design and for which a large body of software tools/documentation is available, and apply it to your input sequence. In short, this consists in evaluating for each sample a linear combination of the past input samples, and the past output samples. Depending on the requirements of your filter in terms of stop-band rejection / ripple, only a few coefficients could be necessary, making it way more efficient than FFT. Since the only information required to compute an output sample is the few past input/output samples, there is no problem to apply it to streaming audio.

You will need to use FFT only if you decide to go with FIR filters, and if your filter requirements cause them to have an insanely large number of coefficients. In this particular case, it will be efficient to apply the filter to successive blocks of your input data through FFT and overlap-add.