The present chapter discusses biomedical signal processing approaches to perform computer-aided sleep analysis and insomnia detection using Big Data technologies and data science modeling. Sleep neuroelectrophysiology is characterized by time-consuming assessments, large and varied volumes of clinical data, and intersubject variability. Such challenges compromise opportune diagnoses and preventive and corrective treatments against aggravated pathologies related to insomnia. The synergy of positioned biosignal processing and data science techniques accompanied by emerging technologies like Big Data offers a unique opportunity to address these endeavors faster and cheaper. Over the course of the chapter, we elaborate on a Big Data Science model to attain automated sleep analysis and insomnia detection based on 40 subjects’ polysomnograms. The methodology walks through six stages: problem understanding, data understanding, data preparation, modeling, evaluation, and model deployment. The proposed model achieved 0.87 sensitivity, 0.75 specificity, and 0.81 accuracy performance rates with a US $200 cloud on-demand processing infrastructure. The chapter provides novel contributions in biomedical signal processing and Big Data Science modeling pursuing a supportive role in sleep neuroelectrophysiology attending to knowledge-data-driven frameworks. Data is the paradigm of the information era. Scientific and humanistic disciplines are slowly becoming more literate to the paramountcy of data. The digital nature of such data is the engine behind this information revolution. Nowadays, like never before, we are capable of producing massive amounts of data at unprecedented rates, and in very diverse formats and representations, as per White (2015). These three conditions—volume, velocity, and variety—commonly characterize a Big Data project. Therefrom, legacy and novel techniques are capable of extracting excerpts of meaningful information within the Big Data applicable to academic or business contexts. Data science is the designated specialty by the ongoing revolution as that responsible for finding sense in the midst of digital chaos, as Provost and Fawcett (2013) stated. Signal processing straightly understands such a struggle to elicit signal power drowned in noise. Therefore, their joint application has brought insightful advances in biomedical engineering research.