Implementing Decoupled Architecture

Esteemed colleagues, Legacy E/E architectures hard-bind a function to “its” 𝗘𝗖𝗨. Change the function → change the hardware. Scale or redeploy it → impossible. That coupling is exactly what 𝘇𝗼𝗻𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 are breaking. 𝘍𝘶𝘯𝘤𝘵𝘪𝘰𝘯 𝘥𝘦𝘤𝘰𝘶𝘱𝘭𝘪𝘯𝘨 = 𝘵𝘩𝘦 𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘵𝘰 𝘳𝘶𝘯 𝘢 𝘷𝘦𝘩𝘪𝘤𝘭𝘦 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯 (𝘦𝘹: 𝘥𝘰𝘰𝘳 𝘭𝘰𝘤𝘬𝘪𝘯𝘨, 𝘓𝘒𝘈, 𝘵𝘩𝘦𝘳𝘮𝘢𝘭 𝘮𝘨𝘮𝘵) 𝘰𝘯 𝘢𝘯𝘺 𝘤𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘵 𝘤𝘰𝘮𝘱𝘶𝘵𝘦 𝘯𝘰𝘥𝘦, 𝘪𝘯𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘵𝘭𝘺 𝘰𝘧 𝘵𝘩𝘦 𝘰𝘳𝘪𝘨𝘪𝘯𝘢𝘭 𝘌𝘊𝘜. 𝘏𝘢𝘳𝘥𝘸𝘢𝘳𝘦 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘢 𝘱𝘰𝘰𝘭 𝘰𝘧 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘴; 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘥𝘦𝘱𝘭𝘰𝘺𝘢𝘣𝘭𝘦, 𝘮𝘰𝘷𝘢𝘣𝘭𝘦, 𝘢𝘯𝘥 𝘶𝘱𝘨𝘳𝘢𝘥𝘢𝘣𝘭𝘦. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: 𝗘𝗖𝗨 𝗲𝘅𝗽𝗹𝗼𝘀𝗶𝗼𝗻 → consolidation: from ~100–150 ECUs to ~20–40 nodes (zonal + a few HPCs). 𝗧𝗶𝗺𝗲-𝘁𝗼-𝗳𝗲𝗮𝘁𝘂𝗿𝗲: from 36–60 months program cycles to sub-12-month software feature drops. 𝗪𝗶𝗿𝗶𝗻𝗴 & 𝘄𝗲𝗶𝗴𝗵𝘁: zonal + decoupled functions enable double-digit % reductions in harness length/weight. 𝗥𝗲𝗰𝗮𝗹𝗹𝘀 → OTA: many software defects no longer imply hardware recalls; they become patchable services. 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: failover becomes a software decision (reallocate the service to another zone) rather than a hardware redesign. 𝗛𝗼𝘄 𝗶𝘁’𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹𝗹𝘆 𝗲𝗻𝗮𝗯𝗹𝗲𝗱: 𝙎𝙚𝙧𝙫𝙞𝙘𝙚-𝙊𝙧𝙞𝙚𝙣𝙩𝙚𝙙 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 (𝙎𝙊𝘼): functions exposed as discoverable, versioned services. 𝙈𝙞𝙙𝙙𝙡𝙚𝙬𝙖𝙧𝙚 / 𝙃𝘼𝙇: AUTOSAR Adaptive, DDS, POSIX layers abstract I/O, scheduling, and communication. 𝙄𝙨𝙤𝙡𝙖𝙩𝙞𝙤𝙣 & 𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣𝙞𝙣𝙜: hypervisors, time & memory partitioning for mixed-criticality (ASIL-D next to QM). 𝘿𝙮𝙣𝙖𝙢𝙞𝙘 𝙤𝙧𝙘𝙝𝙚𝙨𝙩𝙧𝙖𝙩𝙞𝙤𝙣: runtime deployment / re-deployment of services based on load, failure, or updates. 𝙫𝙀𝘾𝙐𝙨 & 𝙨𝙞𝙢𝙪𝙡𝙖𝙩𝙞𝙤𝙣-𝙛𝙞𝙧𝙨𝙩: develop, validate, and integrate functions before they ever touch silicon. 𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗳𝗼𝗿 𝗢𝗘𝗠𝘀 & 𝗧𝗶𝗲𝗿-1𝘀: The integration bottleneck shifts from hardware to software orchestration. KPIs move from “ECU cost & count” to compute density, latency budgets, service SLAs, and OTA cadence. The sourcing model evolves: fewer black-box ECUs, more platform + service ecosystems. Micro-example: A body control “door lock” service originally running in the Front-Left zone can be reallocated to the Central HPC (or another zone) during a controller fault ; no harness redesign, no ECU swap, no vehicle immobilization. This is the quiet foundation of everything we sell as “𝗦𝗗𝗩”, “𝘇𝗼𝗻𝗮𝗹”, and “𝗵𝘆𝗽𝗲𝗿𝗰𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻”. #ZonalArchitecture #SoftwareDefinedVehicle #FunctionDecoupling #AUTOSARAdaptive #SOA #Middleware #EEArchitecture #AutomotiveSoftware #SDV #SystemsEngineering

Building Strong and adaptable Microservices with Java and Spring While building robust and scalable microservices can seem complex, understanding essential concepts empowers you for success. This post explores crucial elements for designing reliable distributed systems using Java and Spring frameworks. 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: The core principles of planning for failure, instrumentation, and automation are crucial across different technologies. While this specific implementation focuses on Java, these learnings are generally applicable when architecting distributed systems with other languages and frameworks as well. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: A typical microservices architecture involves: Multiple Microservices (MS) communicating via APIs: Services interact through well-defined Application Programming Interfaces (APIs). API Gateway for routing and security: An API Gateway acts as a single entry point, managing traffic routing and security for the microservices. Load Balancer for traffic management: A Load Balancer distributes incoming traffic efficiently across various service instances. Service Discovery for finding MS instances: Service Discovery helps locate and connect to specific microservices within the distributed system. Fault Tolerance with retries, circuit breakers etc.: Strategies like retries and circuit breakers ensure system resilience by handling failures gracefully. Distributed Tracing to monitor requests: Distributed tracing allows tracking requests across different microservices for better monitoring and debugging. Message Queues for asynchronous tasks: Message queues enable asynchronous communication, decoupling tasks and improving performance. Centralized Logging for debugging: Centralized logging simplifies troubleshooting by aggregating logs from all services in one place. Database per service (optional): Each microservice can have its own database for data ownership and isolation. CI/CD pipelines for rapid delivery: Continuous Integration (CI) and Continuous Delivery (CD) pipelines automate building, testing, and deploying microservices efficiently. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Frameworks like Spring Boot, Spring Cloud, and Resilience4j streamline the implementation of: Service Registration with Eureka Declarative REST APIs Client-Side Load Balancing with Ribbon Circuit Breakers with Hystrix Distributed Tracing with Sleuth + Zipkin 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗼𝗯𝘂𝘀𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: Adopt a services-first approach Plan for failure Instrument everything Automate deployment

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

At Chase, modernization is key, especially for our digital banking platform. In our latest "Next at Chase" blog post, Aditya Lodha reveals how we've re-engineered our middleware to move beyond the monolith, unlocking agility and scalability. Decoupled Releases: With Scalable Functional Aligned Services (SFAS), we've boosted productivity and streamlined development processes. Accelerated Product Lifecycles: Our modular architecture has improved time to market and responsiveness to customer needs. Quantifiable Improvements: API response time improved by 20%, code coverage jumped from 15% to 80%, and fail-change rate is at an all-time low, enhancing stability. Our new platform handles high traffic volumes with ease, ensuring uninterrupted service. This journey showcases a smart modernization approach for large enterprises managing legacy systems. Proud of our progress and excited for continued innovation. Want to dive deeper? Check out the full blog post below.

I recently spoke with a product manager who was frustrated with the close ties between metering and pricing in her organization. This interconnectedness was causing significant misalignments between the finance, sales, and engineering teams. Here’s why: In traditional billing platforms, metering is tightly integrated with pricing. This leads to various challenges: ➡️ Engineers have to manage usage tracking while being forced to grasp pricing logic, diverting their focus from core product development. ➡️Finance teams are heavily reliant on engineers to set up and maintain accurate metering infrastructure, which slows their ability to iterate quickly on pricing strategies. ➡️This complexity is compounded by the sales team’s need for flexibility in pricing configurations, limiting their ability to respond to customer demands and close deals faster. However, by decoupling metering from pricing, you can create the most flexible environment you can conceive. 1️⃣Engineers can focus solely on improving data accuracy and usage tracking without needing to integrate complex pricing considerations 2️⃣ Finance teams can independently manage pricing models, whether pay-per-use, tiered, or custom, without relying on engineers.  3️⃣ Sales teams can propose and implement customer-specific pricing strategies (e.g., based on API calls, GBs stored, etc.) seamlessly without requiring technical changes. Clear boundaries between metering and pricing can facilitate improved cross-functional collaboration, drive growth, and optimize revenue. By decoupling, teams can innovate faster, meet customer needs, and remove the roadblocks to growth. Have you also been hassled by a coupled architecture? DM to share your experience. #Billing #Pricing #Finance #Decoupling #Revenue

𝗛𝗼𝘄 𝗜’𝗱 𝗕𝘂𝗶𝗹𝗱 𝗮𝗻 𝗦𝗗𝗩 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗧𝗼𝗱𝗮𝘆 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗟𝗼𝘀𝗶𝗻𝗴 𝗠𝘆 𝗠𝗶𝗻𝗱) —𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘷𝘪𝘦𝘸; 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘮𝘺 𝘰𝘸𝘯. 𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 “𝘈𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘺𝘰𝘶𝘳 𝘧𝘪𝘳𝘴𝘵 𝘵𝘦𝘴𝘵 𝘤𝘢𝘴𝘦. 𝘍𝘢𝘪𝘭 𝘩𝘦𝘳𝘦, 𝘢𝘯𝘥 𝘦𝘷𝘦𝘳𝘺 ‘𝘧𝘪𝘹’ 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘥𝘦𝘣𝘵.” 𝟳 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗦𝘁𝗲𝗽𝘀 (𝗮𝗻𝗱 𝗪𝗵𝘆 𝗧𝗵𝗲𝘆 𝗠𝗮𝘁𝘁𝗲𝗿) 𝟬. 🏗️ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗼𝗿 𝗦𝘂𝗳𝗳𝗲𝗿 No Franken‑ECUs. Define blocks, constraints, and absolute 𝗡𝗢𝘀 up‑front. → “𝘐𝘧 𝘪𝘵 𝘣𝘳𝘦𝘢𝘬𝘴 𝘵𝘩𝘦 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦, 𝘪𝘵 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘴𝘩𝘪𝘱.” 𝟭. 🚦 𝗦𝗮𝗳𝗲𝘁𝘆 / 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: 𝗧𝗵𝗲 𝗕𝗮𝗿𝗲 𝗠𝗶𝗻𝗶𝗺𝘂𝗺 Not a phase — it’s your entry ticket. MPU, crypto, E2E in CI from Day 1. “𝘏𝘰𝘱𝘦 𝘪𝘴𝘯’𝘵 𝘢 𝘵𝘩𝘳𝘦𝘢𝘵 𝘮𝘰𝘥𝘦𝘭.” 𝟮. ⚡ 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗲 𝗼𝗿 𝗗𝗶𝗲 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁 = 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻. Stress‑test separately:  • Apps ride on a “dumb pipe” first.  • Transport survives network hell (latency spikes, packet carnage). 𝟯. 🧩 𝗥𝘂𝗻𝘁𝗶𝗺𝗲 𝗔𝗴𝗻𝗼𝘀𝘁𝗶𝗰𝗶𝘀𝗺 𝗗𝗼𝗻𝗲 𝗥𝗶𝗴𝗵𝘁  • Abstraction layer is 𝗺𝗮𝗻𝗱𝗮𝘁𝗼𝗿𝘆 for application dev.  • Runtime optimization happens underneath (apps stay binary‑compatible).  • Clear constraints per deployment:  • “Need hard real‑time? The runtime must guarantee it—no exceptions.” 𝟰. 🔧 𝗧𝗼𝗼𝗹𝘀: 𝗗𝗲𝗳𝗶𝗻𝗲 𝗢𝗻𝗰𝗲, 𝗙𝗼𝗹𝗹𝗼𝘄 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀𝗹𝘆  • Standardize toolchain early (simulators, CI, analyzers).  • New tools? Budget for disruption—3× the cost you expect. “𝘚𝘪𝘮𝘶𝘭𝘢𝘵𝘪𝘰𝘯 𝘪𝘴 𝘫𝘶𝘴𝘵 𝘰𝘯𝘦 𝘸𝘦𝘢𝘱𝘰𝘯. 𝘙𝘦𝘢𝘭 𝘌𝘊𝘜𝘴 𝘥𝘰𝘯’𝘵 𝘭𝘪𝘦.” 𝟱. 🧪 𝗧𝗲𝘀𝘁 𝗟𝗶𝗸𝗲 𝘁𝗵𝗲 𝗩𝗲𝗵𝗶𝗰𝗹𝗲 𝗜𝘀 𝗪𝗮𝘁𝗰𝗵𝗶𝗻𝗴 Pyramid or perish: 𝗨𝗻𝗶𝘁 → 𝗦𝗪 → 𝗛𝗪 → 𝗩𝗲𝗵𝗶𝗰𝗹𝗲. “𝘓𝘢𝘣‐𝘱𝘦𝘳𝘧𝘦𝘤𝘵 = 𝘙𝘰𝘢𝘥‐𝘳𝘦𝘢𝘥𝘺” (EMI, 40 °C to ‑30 °C, vibration torture). “𝘐𝘧 𝘪𝘵 𝘧𝘢𝘪𝘭𝘴 𝘰𝘯 𝘢𝘴𝘱𝘩𝘢𝘭𝘵, 𝘺𝘰𝘶 𝘥𝘪𝘥𝘯’𝘵 𝘵𝘦𝘴𝘵—𝘺𝘰𝘶 𝘨𝘢𝘮𝘣𝘭𝘦𝘥.” 𝟲. 🤝 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗯𝘆 𝗗𝗲𝘀𝗶𝗴𝗻 Not a phase—part of the blueprint. “𝘕𝘰 𝘣𝘦𝘯𝘤𝘩 𝘱𝘳𝘰𝘰𝘧? 𝘐𝘵’𝘴 𝘷𝘢𝘱𝘰𝘳𝘸𝘢𝘳𝘦.” 𝟳. 📜 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗧𝗵𝗮𝘁 𝗦𝘂𝗿𝘃𝗶𝘃𝗲𝘀 𝗔𝘂𝗱𝗶𝘁𝘀 Hate bureaucracy? Same. But:  • Traceability isn’t optional—ASPICE / ISO 26262 will come knocking.  • Document the 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 𝘃𝗶𝗮𝗯𝗹𝗲 𝘁𝗿𝗮𝗶𝗹:  • Requirements ↔ Design ↔ Test • Changes ↔ Justifications  • “𝘓𝘪𝘨𝘩𝘵𝘸𝘦𝘪𝘨𝘩𝘵 = 𝘚𝘭𝘰𝘱𝘱𝘺. 𝘗𝘳𝘰𝘷𝘦 𝘺𝘰𝘶𝘳 𝘸𝘰𝘳𝘬 𝘰𝘳 𝘧𝘢𝘪𝘭 𝘤𝘦𝘳𝘵𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯.” 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗥𝗲𝗮𝗹𝗶𝘁𝘆  • Your supply chain isn’t a vendor list—it’s an innovation network.  • Co‑design with silicon / ECU partners before freezing architecture.  • Turn “absolute NOs” into joint feasibility studies.  • Multi‑sourcing isn’t just anti‑lock‑in—it’s resilience. “A perfect architecture that ignores supply‐chain realities is just expensive fiction.” #SDV #AutomotiveSoftware #StrategicPartnerships #ZeroCompromise

This week's sketch note is about event brokers. In an event-driven system, an event broker is the central software component that mediates the communication of events between producer and consumer applications. Why should you use an event broker in an event-driven architecture? Spatial decoupling An event broker decouples event producers from consumers. Producers and consumers are unaware of each other, not knowing their address/location in the space, thus promoting loose coupling. Temporal decoupling The event exchange happens asynchronously through the event broker, which means that the producer or consumer doesn't have to be available simultaneously to exchange messages. That allows components to operate independently and respond to events in a non-blocking manner, improving system responsiveness. Durability for events Event broker provides long-term event retention, enabling event replaying and backfilling. That allows consumers to catch up on missed events or replay events for historical analysis. Scalability Event brokers facilitate scalability by distributing events among multiple consumers. That is especially important in systems where the load on different components can vary, as the event broker can balance the load and scale the system horizontally. Fault tolerance The event broker can provide fault tolerance by replicating events and ensuring that they are delivered even if some components or nodes in the system fail. That enhances the resilience of the overall architecture. Event routing and filtering Event brokers often support sophisticated routing and filtering mechanisms. Producers can publish events without knowing who or what will consume them. Consumers, in turn, can subscribe to specific types of events or apply filters to receive only the events that are relevant to them. Enhanced Maintainability With components decoupled and communication handled through events, the system becomes more maintainable. Changes to one component can be made without affecting others, making it easier to evolve and update the architecture over time. Interoperability Event brokers can act as intermediaries that enable interoperability between different components and services. They provide a standardized way for components developed using different technologies or languages to communicate. In practice, event-driven systems use message brokers (E.g., ActiveMQ, RabbitMQ, etc.) and streaming data platforms (E.g., Apache Kafka, Redpanda, etc) as event brokers. #eventdrivenarchitecture