Exploring Architectural Patterns from Diverse Perspectives ๐ŸŒ


3 min read

Architectural styles and patterns are essential in defining how systems are structured and operate. By categorizing these patterns from various angles, we gain insights into their unique applications and benefits. Here's a breakdown of architectural patterns viewed through different lenses:

Connections: Crafting Links Between Devices and Applications ๐ŸŒ

  • REST (Representational State Transfer): A lightweight, stateless architecture for building scalable web services.

  • BFF (Backend for Frontend): tailors backend services specifically for the needs of individual frontend applications.

  • RPC (Remote Procedure Call): Enables a program to execute a procedure on a different address space (commonly another computer on a shared network).

  • P2P (Peer to Peer): A decentralized network architecture where each participant (peer) shares part of their resources.

  • SOA (Service-Oriented Architecture): A design where services are provided to the other components by application components through a communication protocol over a network.

Composition: Assembling Internal Structures ๐Ÿ› ๏ธ

  • Microservices: independently deployable services modeled around business capabilities.

  • Monolith: A single-tiered software application where the user interface and data access code are combined into a single program on a single platform.

  • Microkernel: a minimal software architecture with essential functions that is extensible with plugins.

  • Layers: Organized in hierarchical layers, each with a specific responsibility.

  • Plugins are extensions to a software application that add new features or functionalities.

  • Components: modular and interchangeable parts of a system defined by their interfaces.

  • Nanoservices: Extremely small services, focusing on doing one thing very well.

Events: Decoupling Components in an Event-Driven Manner ๐Ÿ“ฃ

  • Publish/Subscribe: A messaging pattern where messages are broadcasted to multiple subscribers.

  • CQRS (Command Query Responsibility Segregation): Separates read and write operations for better scalability and security.

  • ES (Event Sourcing): Uses an event sequence to reconstruct past states of an application.

  • Reactive: Responds to changes in the input by propagating updates to connected components.

Stream: Directing Data Flow ๐ŸŒŠ

  • Pipe and Filters: Processes a stream of data through a series of processing units.

  • Message Brokers: intermediary software modules that translate messages between formal messaging protocols.

  • Fast Data: Processing large streams of real-time data.

Data: Focusing on Data-Oriented Systems ๐Ÿ“Š

  • Data Mesh: A decentralized approach to data architecture and organizational design.

  • Data Warehouse: A central repository for all data collected by an enterprise's various operational systems.

  • Data Fabric: An architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning hybrid multicloud environments.

  • Data-centric Business Logic: Systems designed with data at the core of business logic.

  • Data Lake: A storage repository that holds a vast amount of raw data in its native format.

Purpose: Specialized Architectural Patterns ๐ŸŽฏ

  • API Platform: A set of tools and services that allow for the easy creation, management, and consumption of APIs.

  • Integration Hub: centralizes integration processes and connects disparate systems.

  • Developer Portal: A community and resource center for developers to access APIs, tools, and resources.

  • Developer Platform: An environment that allows developers to create applications and services.

Each perspective offers a unique approach to system design, emphasizing the importance of selecting the right pattern based on specific project needs and goals. ๐Ÿš€

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