Co-prime sensing is a sub-Nyquist technique for signal acquisition. Several modifications to the prototype co-prime array have been proposed in the literature. Researchers have also demonstrated low latency estimation. This paper describes the functioning of a Family of Adjustable Pivot Co-Prime Arrays. However, the main focus is to introduce the reader to a concept called Extremely Sparse Co-Prime Arrays and Samplers. Adjustable pivot co-prime arrays are a special case of the extremely sparse arrays. The closed-form expressions for the weight function and the correlogram bias window are derived. Low latency estimation is demonstrated using the extremely sparse co-prime scheme. Furthermore, a multidimensional and a hybrid extremely sparse co-prime array is proposed as a straightforward extension of the 1D-theory. Finally, a generalized extremely sparse structure is developed with several design parameters. Most existing structures may be viewed as a special case of the generalized scheme.