This document discusses architectures for AI systems and big data engineering. It addresses topics like what constitutes data engineering; how to store and process event data using pipelines; when to use batch vs stream processing; how AI models can be integrated into data pipelines; and considerations for engineering systems involving big data and AI like non-functional requirements, reprocessing data as models improve, and storing model outputs. The overall aim is to provide an overview of key concepts in engineering systems that handle large volumes of data and incorporate artificial intelligence.