Goal:
Within the course framework, students become familiar with major Big Data and Internet of Things (IoT) solutions. The course presents distributed and parallel architectures, their operational mechanisms, the applied technologies, and cloud-based services across various computing platforms, with the aim of efficiently supporting Big Data and IoT application domains, as well as the integration of artificial intelligence (AI) and machine learning into such environments.
Course description:
In the first part of the course, the evolution and key characteristics of Big Data solutions are discussed, including Hadoop- and Spark-based Big Data systems, as well as in-memory and NoSQL database technologies.
The course also addresses the theoretical and practical background of management and orchestration solutions for cloud-based Big Data application domains. In addition to introducing IoT and related frameworks, students become familiar with various application areas of data collection.
As an extension of the theoretical background, the course covers Lambda, Kappa, and other architectural approaches and provides an overview of the design considerations and impacts of integrating artificial intelligence and machine learning into Big Data and IoT architectures.