What emerging system architecture trends and technologies should you follow?

1 Cloud-native architectures

Cloud-native architectures are designed to leverage the benefits of cloud computing, such as elasticity, resilience, automation, and scalability. They use microservices, containers, orchestration, serverless functions, and APIs to create modular, distributed, and loosely coupled systems that can run on any cloud platform. Cloud-native architectures enable faster development, deployment, and iteration of software systems, as well as easier integration with other services and platforms. They also reduce the operational and maintenance costs and risks of running complex systems.

2 Event-driven architectures

Event-driven architectures are based on the principle of reacting to events, such as user actions, system changes, or external triggers, rather than following a predefined workflow or process. They use event sources, event brokers, event processors, and event sinks to produce, transmit, consume, and store events. Event-driven architectures enable more decoupled, scalable, and responsive systems that can handle asynchronous and concurrent processes, as well as complex business logic and workflows. They also facilitate better observability, traceability, and auditability of system events.

3 Edge computing architectures

Edge computing architectures are designed to move the computation and data processing closer to the source of data generation, such as sensors, devices, or users, rather than relying on centralized servers or cloud services. They use edge nodes, edge gateways, edge clusters, and edge clouds to provide low-latency, high-bandwidth, and reliable access to data and resources. Edge computing architectures enable more efficient, secure, and autonomous systems that can handle large volumes of data, real-time analysis, and local decision making. They also support new applications and scenarios, such as IoT, AR/VR, and smart cities.

4 AI-enabled architectures

AI-enabled architectures are designed to incorporate artificial intelligence, machine learning, and deep learning capabilities into system components, interfaces, and behaviors. They use AI models, frameworks, platforms, and tools to enable system intelligence, such as natural language processing, computer vision, speech recognition, recommendation systems, anomaly detection, and predictive analytics. AI-enabled architectures enable more adaptive, personalized, and innovative systems that can learn from data, improve performance, and generate insights. They also create new opportunities and challenges for system architects, such as ethical, legal, and social implications of AI.

5 Domain-driven design

Domain-driven design is a methodology for modeling and designing systems based on the domain knowledge, language, and logic of the problem space, rather than the technical details or constraints of the solution space. It uses concepts such as bounded contexts, entities, value objects, aggregates, domain events, domain services, repositories, and domain-driven APIs to create a rich and consistent domain model that captures the essence and complexity of the system. Domain-driven design enables more aligned, coherent, and maintainable systems that can evolve with the changing needs and expectations of the stakeholders.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?