In the current fast-paced digital age, many data sources generate an unending flow of information, a never-ending torrent of facts and figures that, while perplexing when examined separately, provide profound insights when examined together. Stream processing can be useful in this situation. It fills the void between real-time data collecting and actionable insights. It’s a data processing practice that handles continuous data streams from an array of sources. Real-time data streaming has started having an important impact on modern AI models for applications that need quick decisions.
We can consider a few examples where AI models need to deliver instant decisions, such as self-driving cars, fraud in stock market trading, and smart factories that utilize technology like sensors, robots, and data analytics to automate and optimize manufacturing processes.