How to Simplify Design for Scalability

Have you ever spent endless hours on a project just to end up realising that a more straightforward method would have been more effective? This common mistake, referred to as over-engineering, can cause needless complexity and inefficiency when developing new products. Understanding Over-engineering > Over-engineering happens when a solution gets more difficult than it needs to be, usually by adding features or functionalities that do not directly meet the needs of customers. > This can lead to higher costs, longer development cycles, and less user-friendly products. Real-World Example: The Juicero The Juicero, a high-tech juicing machine, was released in 2016. It cost $700 and was designed to squeeze proprietary juice packets with considerable force. Later on, though, it was found that the costly machine was not essential because the same juice bags could be squeezed by hand. The company was eventually shut down as a result of the public outcry following this disclosure. My Own Story: The Overly Complex Website I was in a team early in my career that was assigned with creating a company website. We included the newest interactive elements and design trends in an effort to wow. Feedback received after the launch, however, indicated that visitors found the website overwhelming and challenging to use. In our pursuit of innovation, we had failed to realise the website's main purpose, which is to provide easily comprehensible information. I learnt the importance of simplicity and user-centred design from this experience. Useful Tips to Prevent Over-Engineering 1. Pay attention to the essential needs: Focus on key features that meet user needs and clearly explain the issue you're trying to solve. Don't include features that aren't directly useful. 2. Adopt Incremental Development: Begin with an MVP that satisfies the fundamental specifications. By using this method, you may get user input and decide on new features with knowledge. 3. Put Simplicity First: Use the KISS philosophy, which stands for "Keep It Simple, Stupid." Simpler designs are frequently easier to use and more efficient. 4. Verify Assumptions: Talk to users to learn about their wants and needs. This guarantees that the things you create will actually be useful to them. 5. Promote Open Communication: Create an environment where team members are at ease sharing thoughts and possible difficulties. Over-engineering tendencies can be recognised and avoided with the support of this collaborative environment. Have any of your initiatives involved over-engineering? How did you respond to it? Post your thoughts and experiences in the comments section below!

Most embedded software teams overcomplicate architecture. They obsess over patterns, diagrams, and endless “best practices”. But here’s the secret I’ve used for years to create scalable, reusable architectures: I don’t start by thinking about components or fancy design patterns. I let the data tell me what the architecture should be. Here are the exact questions I ask myself on every project: 1. Where is data produced? 2. What data is produced? 3. How often is it produced? 4. Where does it flow inside the system? 5. Where is it stored? (RAM, Flash, EEPROM, Disk, Cloud?) 6. Who consumes that data? 7. Where is it processed? These 7 questions give me what I call a Data-Centric Blueprint, a pattern-free, bias-free design based on how the system actually needs to work. Because here’s the thing: When you follow the data, the right architecture reveals itself. You don’t need to force-fit design patterns. You don’t need to guess where the boundaries are. The data shows you. And when you build this way, you end up with an architecture that’s: ✅ Flexible ✅ Scalable ✅ Reusable ✅ Adaptable All because it’s built around what actually matters, the data.

Most leaders try to fix problems by adding more. ➕ More features. ➕ More resources. ➕ More cost. But in complex sales and business growth, adding 'more' often makes problems worse — bloating offers, slowing decisions, and creating friction. The leaders who consistently win work differently. They strip things away first. 🪶 I call it the 'Breakdown–Build-Back (BBB) Model'. A two-step framework I’ve used and taught to transform deals, operations, and go-to-market strategies. 💡 Why It Works Most constraints in business are inherited, not real. - Legacy processes that no one remembers justifying - Feature lists built for 'average' customers rather than a specific one - Assumptions repeated so often they’ve become 'truth' The BBB Model dismantles these false constraints and rebuilds only what drives measurable value. It works because it focuses attention, resources, and alignment on what matters most for the outcome, not the baggage that’s been carried along. 🛠 The Model 1️⃣ Breakdown - Deconstruct the challenge into its smallest components; whether that’s a product, a sales process, or a negotiation package. - Separate the essential from the assumed. - Use data and direct customer input to identify what actually creates value. 2️⃣ Build-Back - Reassemble only the components that deliver impact for this specific deal or market need. - Substitute, simplify, or eliminate low-value elements. - Align stakeholders early so the rebuilt solution is executable. 📌 Real Example I watched a senior exec face a multi-million-unit notebook order with three constraints: - Strict technical specs - Minimal features - A price point close to commodity-level Instead of starting with “how can we cut costs?” they began with 'Breakdown' — mapping every component: display, casing, ports, keyboard, storage, assembly, packaging. Then they moved to 'Build-Back' — cutting unused ports, simplifying casing, streamlining packaging, and preserving core specs. ✅ 20% cost reduction ✅ Price target met ✅ Margins protected ✅ Deal saved 🚀 How BBB Applies to Sales & Growth 1. Solution Design – Craft offers that meet buyer priorities with precision, not excess. 2. Negotiation – Remove low-value elements to meet price points without gutting profitability. 3. Market Entry – Launch lean, focused offers that win early adoption and scale faster. Why it’s powerful: When you stop treating all features, processes, and 'requirements' as sacred, you start to see where speed, simplicity, and cost efficiency live. This creates competitive advantages in margin, agility, and deal velocity. And this is critical for enterprise sales, where complexity kills deals. Did this resonate? If yes, please follow me and repost.

7 must know principles of solution architecture design. Designing effective solutions bridges business goals with technical requirements, ensuring systems remain scalable, secure, and adaptable. A well-architected system is not just about meeting immediate needs—it’s about building for growth, resilience, and change. Struggling to build high-performing systems or design sound solutions? The Solutions Architect's Handbook by Saurabh Shrivastava is a fantastic resource for tackling these challenges. Whether you're new or experienced, it equips you with strategies from the fundamentals to advanced techniques. Grab your copy here: https://lnkd.in/gBW7X9eN Below are 𝟳 𝗰𝗼𝗿𝗲 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝘄𝗶𝘁𝗵 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 for designing solutions: 🔹 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 Systems must handle growth without degrading performance. Vertical scaling adds resources to a node, but horizontal scaling (adding nodes) with load balancers offers better flexibility. Use auto-scaling and manage state (e.g., distributed caches) to handle traffic spikes efficiently. 🔹 𝗛𝗶𝗴𝗵𝗹𝘆 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 High availability ensures systems stay operational despite failures; resilience enables fast recovery. Use active-passive or active-active failover setups to minimize downtime. Build redundancy and synchronize data where needed to maintain continuity during disruptions. 🔹 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝘁 Performance optimization reduces latency and maximizes throughput. Apply non-blocking I/O, asynchronous processing, and caching to handle concurrency. Use p99 latency metrics to monitor worst-case scenarios and maintain a consistent user experience. 🔹 𝗦𝗲𝗰𝘂𝗿𝗲 Security must be embedded throughout. Implement end-to-end encryption, RBAC, and threat modelling to address risks proactively. Use OAuth2 or JWT for authentication, and consider Zero Trust Architecture (ZTA) to ensure continuous identity verification. 🔹 𝗟𝗼𝗼𝘀𝗲𝗹𝘆 𝗰𝗼𝘂𝗽𝗹𝗲𝗱 Modular systems scale and adapt more easily. Use event-driven architectures and asynchronous messaging systems (e.g., Kafka, RabbitMQ) to decouple dependencies. This ensures scalability, fault tolerance, and seamless updates. 🔹 𝗘𝘅𝘁𝗲𝗻𝗱𝗮𝗯𝗹𝗲 Extendable systems grow smoothly over time. Follow the open-closed principle (OCP) to allow new features without modifying core functionality. Ensure API-first designs are backward compatible to support new integrations without disruptions. 🔹 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 Reusable components accelerate development and improve maintainability. Design with composability—using shared libraries, SDKs, or reusable components. Apply domain-driven design (DDD) to promote reusability across business domains. By following these principles, we can build systems that meet today’s demands while staying adaptable to future challenges. Thoughtful architecture ensures scalability, security, and resilience while supporting continuous innovation.

I have taken more than 50+ System Design Interviews in my 20 years of career. Here are the 30 System Design lessons I want to give you: - Design clear & secure APIs - Use auto scaling for traffic spikes - Index databases to optimize reads - Assume failures. Make it fault-tolerant - Partition and shard data for large datasets - Shard SQL databases for horizontal scaling - Use CDNs to reduce latency for global users - Use websockets for real-time communication - Use write-through cache for write-heavy apps - Use an API gateway for multiple microservices - Use microservices over monoliths for scalability - Denormalize databases for read-heavy workloads - Use SQL for structured data and ACID transactions - Use load balancers for traffic distribution and availability - Implement data replication and redundancy for fault tolerance - Clarify functional and non-functional requirements before designing - Add functionality only when needed. Avoid over-engineering - Use rate limiting to prevent overload and DOS attacks - Use heartbeats/health checks for failure detection - Use the circuit breaker pattern to prevent failures - Use message queues for async communication - Make operations idempotent to simplify retries - Use read-through cache for read-heavy apps - Use event-driven architecture for decoupling - Use async processing for non-urgent tasks - Use data lakes or warehouses for analytics - Prefer horizontal scaling for scalability - No perfect solution—only trade-offs - Use NoSQL for unstructured data - Use blob storage for media files Follow me Raman Walia for more content on Interview Prep, System Design and Software Engineering 

→ 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐁𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞: 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 𝐄𝐯𝐞𝐫𝐲 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐌𝐮𝐬𝐭 𝐊𝐧𝐨𝐰 𝐄𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫 𝐰𝐡𝐚𝐭 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞𝐬 𝐚 𝐟𝐫𝐚𝐠𝐢𝐥𝐞 𝐚𝐩𝐩 𝐟𝐫𝐨𝐦 𝐚 𝐫𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐭 𝐩𝐨𝐰𝐞𝐫𝐡𝐨𝐮𝐬𝐞? 𝐓𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐥𝐢𝐞𝐬 𝐢𝐧 𝐦𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐜𝐨𝐫𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐭𝐡𝐚𝐭 𝐤𝐞𝐞𝐩 𝐲𝐨𝐮𝐫 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐬𝐞𝐜𝐮𝐫𝐞, 𝐚𝐧𝐝 𝐞𝐚𝐬𝐲 𝐭𝐨 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧. • API Design: - Define clear, consistent endpoints. - Ensure secure and scalable access. • Saga: - Manage distributed transactions reliably. - Handle failures with compensating actions. • DNS: - Translate domain names to IPs. - Provide distributed, scalable naming. • Idempotency: - Prevent duplicate request effects. - Support safe retries without side effects. • Consistent Hashing: - Evenly distribute load across servers. - Minimize reallocation when nodes change. • Service Discovery: - Dynamically locate service instances. - Enable scalable microservices communication. • JWT: - Securely represent user identities. - Facilitate stateless authentication. • RPC: - Enable remote calls effortlessly. - Abstract network complexities. • API Versioning: - Manage backward-compatible API changes. - Maintain multiple versions simultaneously. • Modular Monolith: - Organize code into modules. - Simplify deployment with one unit. • Redis: - Fast in-memory storage. - Supports caching, pub/sub, persistence. None of these are just buzzwords. They’re the pillars of building reliable systems that scale as your users grow. follow Suresh G. for more insights

10 Design Principles from My Journey to Scale In my career of scaling large complex systems, the 10 principles I've learned have been hard-won through countless challenges and moments of breakthrough. 1. Control Plane and Data Plane Separation: Decouple management interfaces from data processing pathways, enabling specialized optimization of read and write operations while improving system clarity and security. 2. Events as First-Class Citizens: Treat data mutations, metrics, and logs as immutable events, creating a comprehensive system behavior narrative that enables powerful traceability and reconstruction capabilities. 3. Polyglot Data Stores: Recognize that different data types require unique storage strategies. Select datastores based on specific security, consistency, durability, speed, and querying requirements. 4. Separate Synchronous APIs from Asynchronous Workflows: Distribute responsibilities across different servers and processes to maintain responsiveness and handle varied workload characteristics effectively. 5. Map-Reduce Thinking: Apply divide-and-conquer strategies by decomposing complex workflows into manageable, parallelizable units, enabling horizontal scaling and computational efficiency. 6. Immutable Data and Idempotent Mutations: Make data unchangeable and ensure mutations are repeatable without side effects, gaining predictability and comprehensive change tracking through versioning. 7. Process-Level Scaling: Scale at the process or container level, providing clearer boundary semantics, easier monitoring, and more reliable failure isolation compared to thread-based approaches. 8. Reusable Primitives and Composition: Build modular, well-understood components that can be flexibly combined into larger, more complex systems. 9. Data as a Product: Shift perspective to view data as a long-term asset, recognizing its potential beyond immediate application context, especially with emerging machine learning and big data technologies. 10. Optimize What Matters: Focus on strategic improvements by measuring and addressing top customer pain points, avoiding premature optimization. These principles represent more like a philosophy of system design that helped me navigate complexity while seeking elegant solutions. They often transform seemingly impossible challenges into scalable, resilient architectures. In coming weeks, I will try to talk about each one of them, with stories how I learned them in hard ways.

If I had a system design interview tomorrow at Google, this is exactly how I’d approach it.   (This framework helped me crack 3 FAANG+ companies in the past, including Amazon.)  Most engineers fail system design interviews not because they lack knowledge but because they lack structure.  You don’t need to memorize 100 architectures. You need a structured and easy-to-apply approach that works every time.  Here’s how I break it down:   1/ Clarify the Problem Before Writing Anything   - System design interviews aren’t about throwing buzzwords—they’re about trade-offs.   - Start with scoping → Are we designing just one feature or the entire system?   - Ask constraints upfront → How many users? Read/write ratio? Latency requirements?   - Define success criteria → What matters most? Scalability? Cost? Low-latency?  Most candidates assume things and jump into solutions.   I make sure I know what we’re solving before I even start.   2/ Define Functional & Non-Functional Requirements Clearly   - Functional: What features does the system need?   - Non-functional: What are the performance expectations?   - What’s the biggest technical challenge? (This helps guide the discussion.)  Example: If we’re designing YouTube, is the focus on video uploads, recommendations, or live streaming?   Each has a different set of constraints.   3/ Estimate the Scale & Plan Capacity Like an Engineer   - Users per second? Requests per second?   - Storage needs? If we store 10MB per user and have 100M users, what does that mean?   - Throughput? Can a single database handle the load, or do we need sharding?  Most candidates throw random numbers.   I do quick, back-of-the-envelope calculations to validate my assumptions.   4/ Break the System into Core Components (High-Level Design)   - Define the major building blocks → API Gateway, Load Balancer, Service Layers, Databases.   - Don’t overcomplicate. Simple and scalable always wins.   - Clearly define the interactions between services.  If I’m designing a messaging app, I break it down into:   — User Service (auth, profiles)   — Messaging Service (storing chats)   — Notification Service (real-time updates)   — Media Storage (for images, videos)  Each has different constraints, so I build around what’s most important.  Continued Here: https://lnkd.in/eiHQs-qT P.S. Preparing for a software engineering interview? Check out my book, System Design and Behavioral Intelligence. It will help you: • Build strong System Design foundations and approach design problems with confidence • Develop practical frameworks to navigate complex behavioral and leadership challenges 📘 Paperback available on Amazon internationally: https://mybook.to/systemdesignbehavioralhttps://lnkd.in/eiHQs-qT P.S. Preparing for a software engineering interview? Check out my book, System Design and Behavioral Intelligence. It will help you: • Build strong System Design foundations and approach design problems with confidence • Develop practical frameworks to navigate complex behavioral and leadership challenges 📘 Paperback available on Amazon internationally: https://mybook.to/systemdesignbehavioral Check it out!

🍱 How To Organize Your Design System At Scale (https://lnkd.in/e9343uqv), a fantastic case study on how to set up a design system with 900 shared components and 25 designers — with product-specific domain components and shared ownership between the design system guild and product designers. Written by Jérôme Benoit Reveau ↓ Key takeaways: ✅ 1 design system, 8 design libraries, 1 library serves 1 goal. ✅ Each library has owners, editors (edit/publish), users (view-only). ✅ All designers have access to all resources from all files. ✅ Product team has domains, each domain has feature teams. ✅ Foundations + Core components are owned by design system team. ✅ Domain components are product-specific, owned by product designers. ✅ Each feature team has its own frame (not a page!) on a Domain page. ✅ Domain components are structured [Instance name] 💠 [Core name]. ✅ The work by product teams can move up to the Core level, too. In many products, different feature teams often have very different needs, and that’s why secondary design systems emerge. With this set-up, all teams are still working within 1 single design system, pulling and pushing components between levels and having search across all design work in all domains at once — without an organizational overhead! 👏🏼👏🏽👏🏾 Useful resources: How To Organize 1250+ Design Screens in Figma (+ File examples), by Lorenzo Palacios Venin https://lnkd.in/e7X4fKcj Booking.com: Multi-Platform Design System (+ Figma), by Nicole Saidy https://lnkd.in/dVjZscBV Frog: Building A Global Design System, by Anthony Nguyen https://lnkd.in/etkiTxfB Doctolib Figma Files Organization Tips, by Jérôme Benoit Reveau https://lnkd.in/eK7bhQeS Multi-Brand Design System, by Pavel Kiselev https://lnkd.in/eShgnPnW Design System Strategy Map, by Romina Kavcic https://lnkd.in/exp7Az7N Design System Structure for Teams, by Luis Ouriach https://lnkd.in/eFZUjUCU Organizing Design System For Scalability, by Allie Paschal https://lnkd.in/eeAtakGs Design That Scales (Book), by Dan Mall https://lnkd.in/eeFrqFfP And kudos to the wonderful design team at Doctolib and all the wonderful designers above for sharing their insights for everyone to learn from!👏🏼👏🏽👏🏾 #ux #DesignSystems