E-Commerce Technology Platforms

I'm always on the lookout for "AWS" scale customer case studies 😎 !! This recent blog about how Ancestry tackled one of the most impressive data engineering challenges I've seen recently - optimizing a 100-billion-row Apache Iceberg table that processes 7 million changes every hour. The scale alone is staggering, but what's more impressive is their 75% cost reduction achievement. 𝐓𝐡𝐞 𝐀𝐖𝐒-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 Their architecture combines Amazon EMR on EC2 for Spark processing, Amazon S3 for data lake storage, and AWS Glue Catalog for metadata management. This replaced a fragmented ecosystem where teams were independently accessing data through direct service calls and Kafka subscriptions, creating unnecessary duplication and system load. 𝐖𝐡𝐲 𝐈𝐜𝐞𝐛𝐞𝐫𝐠 𝐌𝐚𝐝𝐞 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 Apache Iceberg's ACID transactions, schema evolution, and partition evolution capabilities proved essential at this scale. The team implemented merge-on-read strategy and Storage-Partitioned Joins to eliminate expensive shuffle operations, while custom partitioning on hint status and type dramatically reduced data scanning during queries. 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞-𝐒𝐜𝐚𝐥𝐞 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 This solution now serves diverse analytical workloads - from data scientists training recommendation models to geneticists developing population studies - all from a single source of truth. It demonstrates how modern table formats combined with AWS managed services can handle unprecedented data scale while maintaining performance and controlling costs. More details in the blog at https://lnkd.in/gN-mvdUE #bigdata #iceberg #aws #ancestry #analytics #scale #apache

From a legacy on-prem platform to a fully cloud-hosted data mesh on AWS, HEMA has been busy. I caught up with Tommaso Paracciani from HEMA, a Dutch retailer with 740+ shops across several regions plus a strong online presence. His team led a full migration from on-prem to AWS and used that moment to redesign how the organization works with data. Their stack on AWS includes: Amazon S3 as the foundation of their data lake AWS Glue and Amazon EMR for ETL pipelines (https://lnkd.in/ebfEFWnW) Databricks on AWS for advanced processing AWS Glue Data Catalog + Amazon DataZone to power their internal data marketplace Now upgrading to Amazon SageMaker and SageMaker Catalog as a single environment for all data mesh participants (https://lnkd.in/eTBCT3rY) The outcomes for HEMA resulted in stronger trust in data, backed by a focused data management and data quality program. They now have less bottlenecks around one big central team; business units now build their own data products on top of a governed platform. His advice if you’re starting a similar journey is to plan your work first - don’t rush big implementations and to start small and build incrementally - avoid monolithic releases and focus on the most urgent use cases. #AWSPartner #AWSreInvent #AWSanalytics

I gave this problem to 30 candidates in mock system design interviews to help them think better when it comes to backend and distributed systems. Only 13 could answer it well. Here’s what I asked: You’re designing an order service for a large e-commerce platform. Whenever a user places an order, your system needs to: – Save the order in an SQL database (first) – Then, trigger one or more side effects, such as: – Publishing an event to Kafka – Invalidating a cache – Sending an HTTP request to another service – Calling a webhook But here’s the catch: – If your service crashes after saving the order (but before notifying other systems), downstream services are out of sync. – If you try to “coordinate” both steps, you realize these writes (to DB and to Kafka/cache/HTTP/etc.) aren’t part of the same transaction. – This is the classic dual writes problem, it’s not just about Kafka. Any time you’re trying to write to two (or more) systems that can fail independently, you risk ending up in an inconsistent state. Question: How would you design this flow so that either all side effects happen reliably after a DB write (no matter if you’re sending to Kafka, invalidating cache, or calling a webhook). or none do? – What pattern would you use? – How would you structure your DB and system? – How would you handle failures and retries, and make sure your solution is robust at scale? Walk through your solution step by step. Here’s a video I made, breaking down the entire solution: (https://lnkd.in/gFCsh58T) If you’re prepping for any serious backend role, be ready to go deep, not just naming patterns, but explaining the tradeoffs, implementation, and why it works.

Ever wondered what happens after you click “Checkout”? Let me try to explain the core building blocks of an E-Commerce Architecture. Here’s a breakdown of the journey of an online order using a microservices-based architecture - where each step, from cart to shipping, is handled by an independent service. The process kicks off when a customer places an order, which is managed by the Shopping Cart microservice via a REST API. The order then flows into the Order Placement service, which records and broadcasts the order details through an event stream. Next, the Inventory service checks stock levels and interacts with the Supplier backorder system if needed. The Payment microservice integrates with third-party providers (via SOAP or REST) to process payments securely. Once payment is confirmed, the Shipping service prepares the consignment, updates order status, and notifies the Operations team for dispatch. Meanwhile, reporting tools consume order and inventory events and store them in an OLAP database for analytics and dashboards. Don’t forget to save this for later !

🚨 Most developers think Microservices Architecture is only about splitting applications into smaller services. But the real challenge begins after the services are created. 🔥 I’m continuing my 30-day journey of breaking down real-world System Design concepts used behind scalable applications. 📍 Day 9: Microservices Architecture — How modern applications scale independently. When I first started learning backend systems, I thought scaling meant increasing server size. But large-scale applications don’t scale using one giant backend anymore. Instead, they break systems into independent services. Imagine an e-commerce platform: • Authentication Service • Payment Service • Order Service • Inventory Service • Notification Service Each service handles one specific business capability. That completely changes how systems scale. ⚡ Instead of scaling the entire application: Only the high-traffic services scale independently. For example: • Payment traffic spikes during sales • Recommendation systems need separate scaling • Notifications process asynchronously That’s where Microservices Architecture becomes powerful. 🧩 Core Components in Modern Microservices Architecture: 🌐 API Gateway Acts as the single entry point for all client requests. ⚖️ Load Balancer Distributes traffic across multiple service instances. 🗂️ Service Registry & Discovery Helps services dynamically find and communicate with each other. 🛡️ Identity Management Centralizes authentication and authorization. ⚡ API Cache & CDN Improve performance and reduce backend load. 📊 Monitoring & Management Track system health, logs, and failures in real time. 🗄️ Independent Databases Each service can manage its own storage independently. What surprised me the most is this: Microservices are not only about scalability. They are about managing complexity in massive systems. Without proper architecture: ⚠️ Services become tightly coupled ⚠️ Deployments become risky ⚠️ Failures spread faster ⚠️ Monitoring becomes difficult With well-designed Microservices: ✅ Teams deploy independently ✅ Systems scale efficiently ✅ Failures stay isolated ✅ Development becomes faster One thing I’m slowly realizing while studying distributed systems is this: Modern backend engineering is less about writing isolated code and more about designing systems that collaborate reliably at scale. 🔥 Day 9/30 — System Design Challenges Series #SystemDesign #Microservices #BackendEngineering #DistributedSystems #SoftwareArchitecture #Scalability #Java #SpringBoot

This is an impressive use case and a detailed case study -- NASA Jet Propulsion Laboratory and ISRO - Indian Space Research Organization are building an AWS-powered system that will download 4.4 TB of satellite data and produce 70 TB of satellite data products on a daily basis, using a combination of Spot and On-Demand Amazon EC2 instances for processing, Amazon S3 for long-term storage, and a host of other #AWS services for coordination, messaging, notification, and more. As part of the NASA-ISRO Synthetic Aperture Radar (NISAR) satellite mission, images of nearly all of Earth's land and ice surfaces will be captured every 6-12 days. The processed data will be archived in and then distributed through NASA's Earthdata Cloud data lake, also built on AWS, in support of NASA's open science policy. Read the entire case study at https://lnkd.in/gQUhg6je to learn a lot more!

Ever wondered how Flipkart handles 29 orders every second — without crashing? That’s over 2.5 million orders a day, and the system remains smooth and responsive. What makes it possible? It’s not just brute force — it’s brilliantly optimized design. Here's how: 🔹 API Gateway + CDN = Bouncer + Concierge Filters bots, routes traffic smartly, and handles millions of clicks (even during Big Billion Days). 🔹 Distributed Order Management System (OMS) Real-time checks on inventory, pin code, delivery availability & payment — in milliseconds. Zero friction, zero wait. 🔹 Microservices Architecture Each service (catalog, payments, fulfilment) runs independently with its own DB & autoscaling. If one fails, the rest keep going. 🔹 Smart Warehousing ML-based product slotting + robotic pickers enable ultra-fast order processing, synced live with OMS. 🔹 Dynamic Logistics Engine ML + location graph decides who delivers what from where — in real-time. 🔹 Event-Driven Architecture Every step (order → ship → deliver) is event-triggered through Kafka-like buses. Reliable. Resilient. Responsive. 📌 Takeaway: Handling scale isn’t about throwing more servers at the problem. It’s about predicting traffic, isolating load, and orchestrating systems that do not have single points of failure and run seamlessly. That’s real engineering at scale ! #SystemDesign #Flipkart #Microservices #CloudArchitecture #EcommerceEngineering #Scalability #Kafka #WarehouseAutomation #BigBillionDays #BackendArchitecture

Behind every smooth “Add to Cart → Checkout → Delivered” experience sits a massive ecosystem of services working together in real time, far more complex than most people ever realize. This model shows how a modern e-commerce platform is designed under the hood: from search and pricing to inventory checks, order processing, logistics, notifications, and recommendations - all stitched together through event-driven systems like Kafka. Here’s a quick breakdown: 1. User Search Flow When someone searches for a product, requests are routed through the CDN to deliver fast responses. The search service interacts with Elasticsearch to fetch relevant items, while consumers process the signals for ranking and personalization. 2. Product Browsing & Wishlist Wishlist and cart services store user preferences and state. These read/write requests hit different DB clusters to keep latency low and availability high. 3. User Purchase Flow Once the user clicks Buy, order-taking, pricing, serviceability (ETA checks), and user service work in sync to validate stock, location, delivery dates, and dynamic pricing. 4. Inventory & Warehouse Systems Inventory databases update availability in real time. Warehouse services check where the product is stored and assign the right fulfillment center, preventing overselling and inaccurate stock values. 5. Order Processing System The order is passed into a robust processing pipeline: • Redis handles caching • OMS validates the order • Archival and historical systems ensure long-term auditability These systems ensure the transaction is consistent and traceable. 6. Kafka as the Backbone Kafka connects everything - from purchase events to updates, notifications, and recommendation pipelines. Each service publishes and consumes events reliably, enabling real-time workflows at scale. 7. Logistics & Notifications Once the order is confirmed, logistics services handle shipping tasks while notification systems send updates across email, SMS, and app alerts. 8. Recommendations & Personalization Cassandra clusters power personalized recommendations. Models use user behavior, order history, and search patterns to generate relevant suggestions. A great e-commerce experience isn’t the result of one powerful system - it’s the coordination of dozens of services, databases, and event streams working perfectly together. Understanding this architecture helps engineers build scalable, resilient, and user-friendly platforms.

Title: "Designing a Scalable and Resilient Serverless E-commerce Architecture with AWS Microservices" Architecting a highly available serverless, microservices-based e-commerce site is a complex endeavor that requires careful planning and execution. The architecture presented here utilizes a combination of AWS services to create a scalable, resilient, and efficient system. Static Content Delivery For static content, the architecture uses Amazon CloudFront as the content delivery network (CDN), which caches content at edge locations closer to the users, thus improving load times and reducing latency. Static content is stored in Amazon S3, which provides high durability storage. User Authentication When it comes to handling user authentication, the system relies on a dedicated authentication layer. This layer is responsible for validating authentication tokens, which ensures that only authenticated traffic can interact with the application's dynamic components. API Gateway and Serverless Functions The dynamic traffic is managed through AWS API Gateway, which acts as the front door for all the API calls from the client side. It integrates with AWS Lambda, a serverless compute service, which runs the application's backend code in response to HTTP requests via the API Gateway. This setup allows for a pay-as-you-go model where you only pay for the compute time you consume. Data Storage and Processing AWS DynamoDB, a NoSQL database service, is used to store and retrieve any necessary data. It is designed to handle high-velocity, large-scale applications such as an e-commerce platform. The data from DynamoDB can be used in various Lambda functions for order submission and processing. Orchestration and Workflow Management AWS Step Functions is utilized to orchestrate microservices into serverless workflows. This is crucial for managing the order submission process, where several steps, such as payment processing and email notifications, need to be coordinated in a specific sequence. External Integrations For email communications, Amazon Simple Email Service (SES) is used for its ability to send notifications, marketing messages, and other types of high-quality content to the users. The payment processing is handled through an external payment system, which is integrated into the workflow. Similarly, an external shipment system is used to handle physical order deliveries, with the architecture supporting a twice-per-week interaction with this system. Monitoring and Resilience AWS CloudWatch is included for monitoring the performance of the applications, which allows for real-time tracking of metrics, logging, and alarms. This service is vital for maintaining the health of the system and ensuring high availability. Conclusion: By using microservices, the application can be updated or scaled in parts without affecting the whole system. This design pattern also helps in fault isolation, making the system more resilient.

MAG (Airports Group) built an Agentic AI system to solve unplanned staff absences across their airport operations, in collaboration with the AWS Generative AI Innovation Center.    The use case is straightforward but the complexity makes it ideal for Agentic AI to solve: When an employee calls in sick at an airport running over 1000 flights daily, someone needs to authenticate the employee, apply the correct HR policy based on absence type, update multiple systems, notify managers, and re-roster shifts. Different airports have different job types and different absence categories, so there are hundreds of workflow permutations.   MAG built an Agentic AI solution using Amazon Bedrock AgentCore. The system conducts natural conversations with employees (using Amazon Nova Sonic), applies context-aware guardrails for compliance with critical infrastructure requirements, and coordinates across multiple backend systems. They achieved 99% consistency in absence reporting and reduced processing time by 90%.   The video from AWS re:Invent 2025 is worth watching to understand the technical approach and lessons learned. They used multi-agent architecture where a speech-to-speech model connects to a text-based agent that orchestrates the tools, built custom guardrails that track conversation patterns across multiple turns rather than just screening individual messages, and separated tools from agents using model context protocol for reusability.   https://lnkd.in/e4NiQKVW This is the first use case in MAG's vision for a digital colleague workplace where multiple AI agents coordinate airport operations, with the full session covering their security implementation for critical national infrastructure and practical lessons on adapting tools for agents and maintaining user engagement through thoughtful interface design.   The complexity inherent in airport operations is exactly where Agentic AI demonstrates its value over traditional automation. Thanks Steve Campagnaro Margherita Rosnati Tom Chester for your insightful presentation and innovative solution! Amazon Web Services (AWS) #airportAI