The High Cadence Transient Survey (HiTS) aims to discover and study transient objects with characteristic timescales between hours and days, such as pulsating, eclipsing and exploding stars. This survey represents a unique laboratory to explore large etendue observations from cadences of about 0.1 days and to test new computational tools for the analysis of large data. This work follows a fully \textit{Data Science} approach: from the raw data to the analysis and classification of variable sources. We compile a catalog of \({\sim}15\) million object detections and a catalog of \({\sim}2.5\) million light-curves classified by variability. The typical depth of the survey is \(24.2\), \(24.3\), \(24.1\) and \(23.8\) in \(u\), \(g\), \(r\) and \(i\) bands, respectively. We classified all point-like non-moving sources by first extracting features from their light-curves and then applying a Random Forest classifier. For the classification, we used a training set constructed using a combination of cross-matched catalogs, visual inspection, transfer/active learning and data augmentation. The classification model consists of several Random Forest classifiers organized in a hierarchical scheme. The classifier accuracy estimated on a test set is approximately \(97\%\). In the unlabeled data, \(3\,485\) sources were classified as variables, of which \(1\,321\) were classified as periodic. Among the periodic classes we discovered with high confidence, 1 \(\delta\)-scutti, 39 eclipsing binaries, 48 rotational variables and 90 RR-Lyrae and for the non-periodic classes we discovered 1 cataclysmic variables, 630 QSO, and 1 supernova candidates. The first data release can be accessed in the project archive of HiTS.