What is conjugacy and how does it relate to bayesian posterior updates?

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I am reading about conjugacy here: https://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributions

The idea seems to be that, if we can make the prior be of a specific distribution, then the posterior will also be of that distribution.

What I don't understand is: why is this helpful, and how does it tie into posterior updates?

An example would be super helpful!