A unified theory of optimal composite estimation in survey sampling settings involving combination of independent or correlated estimates from various survey sources can be formulated using the principle of best linear unbiased estimation. This applies to traditional survey designs involving data combination, such as multiple-frame and multi-phase sampling, and to various forms of combining data from independent or dependent samples with overlapping survey content, as in split-questionnaire designs, rotating panel surveys, non-nested double sampling and supplement surveys. An equivalent practical formulation of optimal composite estimation involving micro-integration of data from different samples is possible through a suitable calibration scheme for the sampling weights of the combined sample. The calibrated weights can be used to calculate weighted statistics, including totals, means, ratios, quantiles and regression coefficients. In particular, they give rise to composite estimators of population totals that are asymptotically best linear unbiased estimators. This unified approach to constructing optimal composite estimators through calibration will be illustrated with three distinct survey paradigms.