In this paper, it is shown that Big Data sources, which are generally known to suffer from coverage bias, can be used to integrate with the data from probability samples for efficient finite population inferences. The methods developed in this paper can be extended to address situations where the variables in the Big Data, or in the probability sample, suffer from measurement errors, and also when there is unit non-response in the probability sample. We will demonstrate the efficacy of the methods using simulation.