The proliferation of data-generating devices and applications has precipitated a paradigm shift towards real-time data stream processing and analytics. As industries increasingly rely on instantaneous data insights for operational and strategic decision-making, the ability to process and analyze data streams in real-time has become essential. This review article delineates the fundamental concepts and architectures supporting real-time data stream processing and analytics. It further discusses the evolution of technologies and platforms tailored for this purpose, evaluating them based on scalability, latency, fault tolerance, and interoperability. The emerging trends and challenges in the domain, including the integration of machine learning in real-time processing systems and the handling of heterogeneous data sources, are also explored. This paper aims to provide a comprehensive overview of current advancements while offering insights into future research directions in the field of real-time data stream processing and analytics.
Arimondo Scrivano (Fri,) studied this question.