The article explores the evolution of data processing from traditional transaction-focused systems to event-oriented infrastructure, highlighting the differences between events and transactions in data management. Initially, the high cost of computing and storage led to the development of On-Line Transaction Processing (OLTP) systems that prioritized the current state, while On-Line Analytics Processing (OLAP) later emerged to automate transaction analysis. As technology advanced, the distinction between transactions and events became more pronounced, with events representing immutable occurrences and transactions reflecting changeable states. The rise of event-native infrastructure, driven by the growth of the internet and streaming systems like Kafka, allows for the real-time analysis and storage of events. This shift is exemplified by companies like Amazon, which use event data to gain insights into customer behavior and improve e-commerce strategies. While Hybrid Transactional/Analytic Processing (HTAP) databases aim to integrate OLTP and OLAP, the need for specialized event-oriented systems persists, particularly for large-scale and real-time applications.