###### Abstract  
Data in the educational context are becoming increasingly important in decision-making and teaching-learning processes. Similar to the industrial context, educational institutions are adopting data-processing technologies at all levels. To achieve representative results, the processes of extraction, transformation and uploading of educational data should be ubiquitous because, without useful data, either internal or external, it is difficult to perform a proper analysis and to obtain unbiased educational results. It should be noted that the source and type of data are heterogeneous and that the analytical processes can be so diverse that it opens up a practical problem of management and access to the data generated. At the same time, ensuring the privacy, identity, confidentiality and security of students and their data is a \"sine qua non\" condition for complying with the legal issues involved while achieving the required ethical premises. This work proposes a modular and scalable data system architecture that solves the complexity of data management and access. On the one hand, it allows educational institutions to collect any data generated in both the teaching-learning and management processes. On the other hand, it will enable external access to this data under appropriate privacy and security conditions. cloud computing; modularity; educational data analytics; learning analytics; academic analytics; data privacy and security; digital identity; API; data storage; open data +
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[PERSON] et al., \"one of the significant opportunities of cloud computing lies in its potential to help... upfront investments that have stymied past efforts.... Small businesses can exploit high-end applications like ERP software or business analytics that were hitherto unavailable to them.\" [3; 4; 5] (pp. 181-182). The union of these technologies has directly impacted society, with the Internet of Things being a clear example of the ease of everyday tasks using thermostats, surveillance cameras and buttons to dispense toilet paper. This trend is expected to follow an exponential growth in terms of technical change and socioeconomic impact [6; 7]. Natural language processor technologies based on machine learning or deep learning are examples providing correction and translation services online. Artificial intelligence-based technologies capable of recreating non-existing human faces are sometimes used to create fake videos promoting legal and moral debate. This can be seen in the works of authors such as [PERSON] et al. regarding the juristic challenges of artificial intelligence [8], [PERSON] and [PERSON] regarding research ethics in big data [9], [PERSON] and [PERSON] regarding the growth of data capitalism and ethics in artificial intelligence [10] or [PERSON] et al. regarding artificial intelligence in an education context [11]. Many solution stacks are based on a cloud computing centralized structure to offer the same product to everyone. Despite that fact, these technologies cannot be placed on closed environments such as a mobile app because its development is continuous and the machine learning process requires quality data to improve, providing a big data environment and interceding for it. Machine learning provides an accurate and precise solution if the dataset on which it is based contains lots of quality data. That is why these technologies require lots of real-time data to offer a decent service, with its mistakes included, even in the research context, in the need to balance privacy when creating knowledge for society's good as stated by [PERSON] and [PERSON] [9]. Some technologies, like machine learning, are not based on new concepts. Some authors already acknowledged this concept in the 90s [12], but the amount of data creates a need for those technologies. To sum up, the business sector offers machine learning-based services on cloud computing environments, promoting a society-business interdependent cycle with data lying in its central point.