Event-Driven Architectures in Modern Cloud-Native Systems: From Traditional ETL to Event-Native Data Processing

Abstract

Event-driven architectures mark a major change in how modern cloud-native computing systems work, replacing traditional request-response patterns with reactive programming that responds to business events immediately. Batch processing methods are being replaced by real-time responsiveness that manages state changes and data updates as they occur. Loose coupling mechanisms, eventual consistency models, and asynchronous communication patterns are part of the architectural transformation that increases system resilience and scalability. Real-time telemetry platforms show practical uses through session-based batching techniques that manage late-arriving data and out-of-order event delivery while keeping analytical consistency intact. Watermarking mechanisms work with session management to guarantee event completeness in distributed environments where network conditions vary. Streaming analytics platforms are created through machine learning integration that manages both online learning and real-time inference scenarios, while edge computing reduces latency and bandwidth requirements. Traditional extract, transform, and load processes are changing into event-native data processing, creating basic changes in how data engineering works and requiring different techniques for schema evolution, exactly-once processing semantics, and distributed state management. There is a need for high availability, immediate processing capabilities, and dynamic scalability in changing operational environments for event-driven systems that are used in mission-critical applications, due to architectural innovations. These systems maintain consistency guarantees and operational continuity across distributed computing infrastructures while enabling organizations to handle massive data volumes.