AWS Architecture for Order to Delivery Solutions

In retail, speed is no longer a competitive advantage—it’s the price of admission. The difference between leaders and laggards comes down to one thing: real-time data. You either see the moment as it unfolds, or you react after the market has already moved on.   When I sit down with retail leaders, I often talk about what I call the low-hanging fruits—not because they’re easy, but because they deliver disproportionate impact, fast.   - First, ERP integration. When buyers and suppliers operate on the same live version of truth, friction disappears. Decisions get sharper. Trust goes up. - Second, intelligent agents. Not dashboards that explain yesterday, but systems that think in the moment—forecasting demand, monitoring inventory, and optimizing logistics as conditions change. - Third, next-generation VMI. Inventory that manages itself—cutting stockouts without tying up capital in excess stock.   These aren’t moonshots. They’re practical, achievable today, and they build momentum quickly.   Recently, we partnered with a leading luxury retailer to bring this vision to life. Their reality was familiar: no real-time visibility, an overwhelming flood of OMS events, legacy infrastructure that couldn’t scale, and legitimate concerns about protecting sensitive data. We re-architected the foundation. A serverless AWS platform capable of processing millions of OMS events in real time. A secure, centralized data lake. AI and ML models embedded into the flow of operations. And live dashboards that put insight directly into the hands of business leaders.   The outcomes spoke for themselves: - Real-time and historical visibility across the enterprise - A scalable, cost-efficient technology backbone - A future-ready platform for advanced analytics and faster decision-making   This isn’t about operational efficiency alone. This is about competitive advantage.   The next wave of retail disruption is already here. The winners will be the ones who master real-time analytics and AI—not as experiments, but as core capabilities embedded into how they run the business. #AIinRetail

𝙃𝙤𝙬 𝙙𝙤 𝙮𝙤𝙪 𝙙𝙚𝙨𝙞𝙜𝙣 𝙚𝙫𝙚𝙣𝙩-𝙙𝙧𝙞𝙫𝙚𝙣 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝙬𝙞𝙩𝙝 𝘼𝙒𝙎 𝙀𝘾𝙎? 𝙃𝙚𝙧𝙚’𝙨 𝙤𝙣𝙚 𝙬𝙖𝙮 𝙩𝙤 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝 𝙞𝙩: Using Amazon ECS, AWS Fargate, SQS, and other AWS services, I designed a solution to handle both individual and batch order processing for an e-commerce platform. 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝟭: 𝗜𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗢𝗿𝗱𝗲𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Orders from the web app are sent to an SQS queue, where they’re processed by ECS tasks running on Fargate. 🔗 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴: SQS separates the frontend from the backend, enabling scalability and flexibility. 📈 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: CloudWatch keeps track of unprocessed messages in the SQS queue and triggers EventBridge to launch ECS tasks as needed. 🚀 𝗧𝗮𝘀𝗸 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Fargate processes orders without managing servers, ideal for tasks that exceed 15 minutes. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: 🛑 𝗙𝗮𝗶𝗹𝗲𝗱 𝗘𝘃𝗲𝗻𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆: EventBridge DLQ captures events that fail to trigger ECS tasks (e.g., permission issues). 🔄 𝗙𝗮𝗶𝗹𝗲𝗱 𝗢𝗿𝗱𝗲𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: SQS DLQ stores orders that fail during ECS processing, ensuring no message is lost. 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝟮: 𝗕𝗮𝘁𝗰𝗵 𝗢𝗿𝗱𝗲𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Business partners upload batch files to an S3 bucket, triggering an ECS task to parse and validate orders. ✅ 𝗩𝗮𝗹𝗶𝗱 𝗢𝗿𝗱𝗲𝗿𝘀: Sent to the same orders-queue for further processing. ❌ 𝗜𝗻𝘃𝗮𝗹𝗶𝗱 𝗢𝗿𝗱𝗲𝗿𝘀: Stored in a second S3 bucket with metadata for debugging and partner notifications. 𝗪𝗵𝘆 𝗘𝗖𝗦 𝗙𝗮𝗿𝗴𝗮𝘁𝗲? ⏳ 𝗟𝗼𝗻𝗴-𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘁𝗮𝘀𝗸𝘀: Easily handles tasks exceeding Lambda’s 15-min limit. ⚙️ 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗰𝗼𝗺𝗽𝘂𝘁𝗲: Eliminates EC2 management overhead. 💰 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁: Pay only for task runtime. This is one way to design an event-driven architecture for e-commerce. How would you handle this use case? Let me know your thoughts!

Give me 2 minutes, and I'll teach you McDonald’s delivery platform's software architecture (not joking): ➟ They combine hexagonal and event-driven architecture for modularity & loose coupling ➟ They represent the domain logic using the hexagonal core ➟ They use schema registry to maintain contracts for events ➟ They use a standby database to prevent data loss if the message broker is unavailable ➟ They route the events to a dead-letter topic if schema validation fails ➟ They store the menus and restaurant's working hours in an SQL database ➟ They use a queue for guaranteed ordering and exactly-once processing of transactions ➟ They use serverless to add new features without worrying about infrastructure ➟ They use an in-memory cache with Redis to process orders ➟ They do ETL and sentiment analysis on user feedback data ➟ They do smoke testing to check the responsiveness of their API ➟ They use circuit breaker logic and exponential backoff for resilience —— 👋 PS - I wrote an article with visuals of this case study in my newsletter: → https://lnkd.in/e2JxTCDq ——— If you liked this post: 🔔 Follow Neo Kim ♻ Repost to help others find it 💾 Save this post for future reference

𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗼𝗻 𝗔𝗪𝗦 Tired of tightly coupled systems that crumble under scale?  𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (𝗘𝗗𝗔) might be your savior. By leveraging AWS services like 𝗦𝗡𝗦, 𝗦𝗤𝗦, 𝗘𝘃𝗲𝗻𝘁𝗕𝗿𝗶𝗱𝗴𝗲, and 𝗟𝗮𝗺𝗯𝗱𝗮, you can build systems that scale seamlessly, react in real-time, and stay resilient.  Let’s dive in! 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻’𝘁 𝗜𝗴𝗻𝗼𝗿𝗲: 1️⃣ 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗲 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀: Use SNS (pub/sub) and SQS (message queues) to separate producers and consumers. No more bottlenecks! 2️⃣ 𝗘𝘃𝗲𝗻𝘁𝗕𝗿𝗶𝗱𝗴𝗲 𝗳𝗼𝗿 𝗘𝘃𝗲𝗻𝘁 𝗕𝘂𝘀𝗲𝘀: Centralize events across services (SaaS, AWS, custom apps) for cleaner integration. 3️⃣ 𝗟𝗮𝗺𝗯𝗱𝗮 𝗳𝗼𝗿 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗖𝗼𝗺𝗽𝘂𝘁𝗲: Trigger functions in response to events without managing servers. Think: Cost-efficient scaling. 4️⃣ 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: Use SQS dead-letter queues (DLQs) to capture failed events and retry logic. 5️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴: Pair CloudWatch with X-Ray to trace event flows and debug faster. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: ✅ 𝗘-𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗢𝗿𝗱𝗲𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: • Order service → SNS → Lambda (inventory check) + SQS (payment processing). • Scalable even on Black Friday! ✅ 𝗜𝗼𝗧 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: • Thousands of devices → EventBridge → Lambda (transform data) → S3/DynamoDB. • Handle spikes without breaking a sweat. ✅ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗡𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: • User action → SNS → Lambda (send email/SMS) + SQS (audit logs). • Async, fault-tolerant, and fast. 💡 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: EDA isn’t just a buzzword; it’s a blueprint for systems that adapt to demand, reduce dependencies, and cut costs. Whether modernizing legacy apps or building cloud-native solutions, AWS’s event-driven toolkit has you covered. 𝗬𝗼𝘂𝗿 𝗧𝘂𝗿𝗻: Have you implemented EDA on AWS? #AWS #awscommunity #EventDrivenArchitecture