Building Reliable Software and Sustainable Systems

Systems don’t fail because something went wrong - they fail because nothing was prepared to handle what went wrong. That’s why failure-handling patterns are a core part of system design. This visual breaks down 12 essential techniques engineers use to build resilient, fault-tolerant systems that stay reliable under real-world pressure: - Retry Reattempt failed operations to handle temporary network or service glitches. Used in API calls, database queries, and distributed requests. - Circuit Breaker Stops calls to unhealthy services to prevent cascading failures. Common in microservices communication. - Bulkhead Isolates failures so one overloaded component doesn’t crash the entire system. Used with thread pools and microservice resource isolation. - Fallback Provides a degraded or cached response when a dependency fails. Keeps the user experience smooth with static data or defaults. - Timeouts Prevents waiting forever for slow or stuck services. Critical for APIs, databases, and distributed systems. - Dead Letter Queue (DLQ) Captures failed messages for later inspection or reprocessing. A staple in message queues and event-driven architectures. - Rate Limiting Protects systems from abuse or overload by restricting excessive requests. Used widely in public APIs and authentication services. - Load Shedding Drops non-critical traffic during peak load to keep core functions alive. Common in high-traffic or real-time systems. - Graceful Degradation Reduces functionality instead of failing completely. Used in dashboards, e-commerce platforms, and streaming apps. - Redundancy Duplicates critical components to eliminate single points of failure. Standard practice for databases, servers, and networks. - Health Checks Detects unhealthy services and removes them from rotation. Used by load balancers and orchestration tools. - Failover Automatically switches to a backup system when the primary one fails. Essential for multi-region deployments and database clusters. Mastering these techniques is what separates systems that work in theory from systems that work in production. Which ones have you used in your architecture?

Engineering systems with near 100% uptime is no easy feat 🎯 Yet, we at Liquid AI with LFM-7B have achieved precisely that. Here's how we built reliability into every layer. When you're serving AI models at scale, every second of downtime matters. Users depend on consistent access, applications break without it, and trust erodes quickly. Achieving near 100% uptime for LFM-7B wasn't luck. It was systematic engineering. What makes a bulletproof AI serving infrastructure? 1️⃣ Redundancy at Every Level Multiple model replicas. If one instance fails, traffic seamlessly routes to healthy ones. Zero disruption for users. 2️⃣ Proactive Health Monitoring Real-time health checks every couple of seconds. Automated alerts before issues escalate. We catch problems before users even notice them. 3️⃣ Smart Load Balancing Dynamic traffic distribution based on instance performance. No single point of failure. Ever. 4️⃣ Rigorous Testing Pipeline Every deployment goes through staging environments first. Canary releases catch edge cases. Automated rollbacks if metrics drift. 5️⃣ Graceful Degradation When extreme load hits, the system scales horizontally. Request queuing ensures no dropped connections. Performance might slow, but service continues. The result? LFM-7B maintains near-perfect availability while processing hundreds of millions of tokens. Because in production AI, reliability isn't optional. Building resilient systems takes more than good intentions. It takes engineering discipline, the right architecture, and constant vigilance 💪

𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Modern platforms must be secure, resilient, and globally scalable. After years of working with architects, engineers, and product leaders, one thing has become clear: most system failures are not caused by bad code but by poor design choices. The System Design Topic Map consolidates the twelve foundational pillars you must master to architect reliable, enterprise-ready systems: 𝟭. 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 𝗮𝗻𝗱 𝗘𝗱𝗴𝗲 Design entry points with load balancing, CDN caching, adaptive throttling, and WAF integration for security and performance. 𝟮. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Enable reliable connectivity with HTTP, WebSockets, gRPC, and service discovery strategies that keep distributed systems synchronized. 𝟯. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 Design storage that fits the workload: SQL for structure, NoSQL for flexibility, and distributed models with sharding and replication for scale. 𝟰. 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 Deliver sub-second responses with multi-tier caching, eviction strategies, and latency reduction techniques like hedged requests. 𝟱. 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 Decouple services with Kafka, RabbitMQ, SQS, or EventBridge. Enable event-driven pipelines and exactly-once delivery for fault tolerance. 𝟲. 𝗦𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Power semantic search, hybrid ranking, and analytics at scale using indexing strategies and vector-enhanced queries. 𝟳. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗮𝗻𝗱 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 Run workloads efficiently with Kubernetes, containers, serverless compute, and autoscaling across environments. 𝟴. 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Plan for failure with circuit breakers, graceful degradation, cross-region failover, and chaos testing frameworks. 𝟵. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 Protect systems using IAM, OAuth2, encryption, and secure defaults that enforce the principle of least privilege. 𝟭𝟬. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Monitor health with metrics, traces, logs, dashboards, and SLO-driven alerting for proactive detection. 𝟭𝟭. 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗮𝗻𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Accelerate releases with CI and CD pipelines, infrastructure as code, rolling updates, and feature flag-driven rollouts. 𝟭𝟮. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁, 𝗖𝗼𝘀𝘁, 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 Integrate FinOps, GDPR, HIPAA, and SOC2 strategies to optimize cost, enforce policies, and scale responsibly. The System Design Topic Map is your blueprint to build platforms that are resilient, intelligent, and trusted by millions. Follow Umair Ahmad for more insights #SystemDesign #Architecture #CloudComputing #DevOps #EngineeringLeadership

Most ML systems don’t fail because of poor models. They fail at the systems level! You can have a world-class model architecture, but if you can’t reproduce your training runs, automate deployments, or monitor model drift, you don’t have a reliable system. You have a science project. That’s where MLOps comes in. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟬 - 𝗠𝗮𝗻𝘂𝗮𝗹 & 𝗙𝗿𝗮𝗴𝗶𝗹𝗲 This is where many teams operate today. → Training runs are triggered manually (notebooks, scripts) → No CI/CD, no tracking of datasets or parameters → Model artifacts are not versioned → Deployments are inconsistent, sometimes even manual copy-paste to production There’s no real observability, no rollback strategy, no trust in reproducibility. To move forward: → Start versioning datasets, models, and training scripts → Introduce structured experiment tracking (e.g. MLflow, Weights & Biases) → Add automated tests for data schema and training logic This is the foundation. Without it, everything downstream is unstable. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟭 - 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 & 𝗥𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 Here, you start treating ML like software engineering. → Training pipelines are orchestrated (Kubeflow, Vertex Pipelines, Airflow) → Every commit triggers CI: code linting, schema checks, smoke training runs → Artifacts are logged and versioned, models are registered before deployment → Deployments are reproducible and traceable This isn’t about chasing tools, it’s about building trust in your system. You know exactly which dataset and code version produced a given model. You can roll back. You can iterate safely. To get here: → Automate your training pipeline → Use registries to track models and metadata → Add monitoring for drift, latency, and performance degradation in production My 2 cents 🫰 → Most ML projects don’t die because the model didn’t work. → They die because no one could explain what changed between the last good version and the one that broke. → MLOps isn’t overhead. It’s the only path to stable, scalable ML systems. → Start small, build systematically, treat your pipeline as a product. If you’re building for reliability, not just performance, you’re already ahead. Workflow inspired by: Google Cloud ---- If you found this post insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more deep dive AI/ML insights!

Ensuring data quality at scale is crucial for developing trustworthy products and making informed decisions. In this tech blog, the Glassdoor engineering team shares how they tackled this challenge by shifting from a reactive to a proactive data quality strategy. At the core of their approach is a mindset shift: instead of waiting for issues to surface downstream, they built systems to catch them earlier in the lifecycle. This includes introducing data contracts to align producers and consumers, integrating static code analysis into continuous integration and delivery (CI/CD) workflows, and even fine-tuning large language models to flag business logic issues that schema checks might miss. The blog also highlights how Glassdoor distinguishes between hard and soft checks, deciding which anomalies should block pipelines and which should raise visibility. They adapted the concept of blue-green deployments to their data pipelines by staging data in a controlled environment before promoting it to production. To round it out, their anomaly detection platform uses robust statistical models to identify outliers in both business metrics and infrastructure health. Glassdoor’s approach is a strong example of what it means to treat data as a product: building reliable, scalable systems and making quality a shared responsibility across the organization. #DataScience #MachineLearning #Analytics #DataEngineering #DataQuality #BigData #MLOps #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gUwKZJwN

Your code works on your laptop. Congrats. 🎉 Happy? Now make it work for 100 million users. That's where most of the data engineers need to emphasize on System Design. 🎯 Why System Design Actually Matters The brutal truth: →Your SQL query might be perfect →Your pipeline might be beautiful But can it handle Black Friday traffic? Database failures? Regional outages? System Design = Building solutions that don't collapse under real-world chaos. 📚 Here's the Learning Roadmap you can follow(No Fluff) - 🟢 FOUNDATION LEVEL Master these first, or everything else falls apart: Core Infrastructure: → Load Balancers (distribute traffic before it breaks you) → API Gateways (your system's front door) → CDNs (stop making users in Tokyo wait 3 seconds for data from Virginia) Data Fundamentals: → ACID vs BASE properties → SQL vs NoSQL (and when each will save/ruin your day) → Indexing strategies (the difference between 10ms and 10s queries) 🟡 INTERMEDIATE LEVEL Now you're building for scale: Performance Patterns: → Caching Layers (Redis, Memcached) - because hitting the DB every time is a crime → Database Sharding - when one database isn't enough anymore → Read Replicas & Replication Patterns - spread the load, reduce the pain Reliability Building Blocks: → Rate Limiting & Throttling → Circuit Breakers (fail fast, recover faster) → Retry Mechanisms with Exponential Backoff 🔴 ADVANCED LEVEL Welcome to distributed systems nightmares: The Hard Stuff: → CAP Theorem (you can't have it all, so choose wisely) → Consensus Algorithms (Raft, Paxos) - how distributed systems agree on reality → Event Sourcing & CQRS patterns → Distributed Transactions & Saga Pattern Data Engineering Specifics: Stream Processing Architecture (Kafka, Flink, Spark Streaming) → Lambda vs Kappa Architecture → Data Lake vs Data Warehouse design → ETL/ELT Orchestration at scale Preparing for data engineering system design interviews? DO THIS: ✅ Think out loud (interviewers want to see your thought process) ✅ Ask clarifying questions (shows you don't make assumptions) ✅ Discuss trade-offs (every decision has pros/cons) ✅ Draw diagrams (visual communication matters) ✅ Mention monitoring & observability (production-ready thinking) ✅ Consider failure scenarios (what happens when X goes down?) Stick to these Impactful Habits to grow - →Don’t focus only on tools—master principles, system thinking, and communication with non-data teams. →Pursue hands-on learning (projects, peer reviews, learning from production mishaps). →Treat AI and new tech as force multipliers, not adversaries—learn to steer, not just ride. Here's amazing System Design Blueprint crafted by Alex Xu!! Start simple. Learn incrementally. Practice real problems. What's the most complex system you've designed or broken in production? Share your challenging stories below 👇

As a software engineer, learn below to master System Design and build scalable, reliable systems: →Fundamentals a. System components (clients, servers, databases, caches) b. High-level vs. low-level design c. CAP Theorem d. Consistency models (eventual, strong, causal) e. ACID vs. BASE properties f. Trade-offs in design (scalability, availability, cost) →Scalability a. Horizontal vs. vertical scaling b. Load balancing algorithms c. Sharding techniques d. Partitioning strategies e. Auto-scaling and elasticity f. Data replication (master-slave, multi-master) →Reliability & Fault Tolerance a. Redundancy and failover b. Circuit breakers c. Retry and backoff mechanisms d. Chaos engineering e. Graceful degradation f. Backup and disaster recovery →Performance Optimization a. Caching layers (CDN, in-memory like Redis) b. Indexing and query optimization c. Rate limiting and throttling d. Asynchronous processing e. Compression and data serialization f. Profiling tools and bottlenecks analysis →Data Management a. Database selection (SQL vs. NoSQL, key-value, graph) b. Data modeling and schema design c. Transactions and isolation levels d. Data migration strategies e. Big data tools (Hadoop, Spark) f. ETL processes →Networking & Communication a. API gateways and service discovery b. RPC vs. REST vs. GraphQL vs. gRPC c. Message queues (Kafka, RabbitMQ) d. Proxies and reverse proxies e. DNS and CDN integration f. Latency and bandwidth considerations →Security in Design a. Authentication and authorization flows b. Encryption at rest/transit c. Threat modeling d. Access controls and RBAC e. Compliance (GDPR, HIPAA) f. Vulnerability scanning →Architectural Patterns a. Monolithic vs. microservices b. Event-driven architecture c. Serverless and FaaS d. Domain-driven design (DDD) e. CQRS and event sourcing f. Hexagonal architecture →Observability & Maintenance a. Monitoring and metrics (Prometheus, Grafana) b. Logging and distributed tracing (ELK stack, Jaeger) c. Alerting and on-call processes d. SLAs, SLOs, and error budgets e. Versioning and backward compatibility f. A/B testing and feature flags →Case Studies & Best Practices a. Designing URL shorteners b. Social media feeds or notification systems c. E-commerce checkout flows d. Ride-sharing platforms e. Real-time chat applications f. Lessons from outages (e.g., AWS, Google incidents) 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗝𝗮𝘃𝗮 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? I’ve got you covered 𝐂𝐡𝐞𝐜𝐤 𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗱𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗝𝗮𝘃𝗮 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗣𝗿𝗲𝗽 𝗞𝗶𝘁: https://lnkd.in/dfhsJKMj 40% OFF for a limited time: use code 𝗝𝗔𝗩𝗔𝟭𝟳 #Java #Backend #JavaDeveloper

What separates good software design from truly great software design? After speaking with over 100 software engineers in 2024 alone, one thing is clear: a strong understanding of design and architecture principles is the foundation for building scalable, maintainable, and high-performing systems. This roadmap captures key insights from those conversations, breaking down the journey into manageable, actionable steps. It covers everything you need to master, including: • Programming Paradigms like structured, functional, and object-oriented programming, which are the building blocks of clean code. • Clean Code Principles that ensure your code is consistent, readable, and easy to test. Engineers consistently highlighted the importance of small, meaningful changes over time. • Design Patterns and Principles such as SOLID, DRY, and YAGNI. These were frequently mentioned as the “north star” for keeping systems adaptable to change. • Architectural Patterns like microservices, event-driven systems, and layered architectures, which are the backbone of modern software design. • Enterprise Patterns and Architectural Styles that tie it all together to solve complex, real-world challenges. Every engineer I’ve spoken to this year emphasized the value of breaking the learning journey into smaller milestones—and this roadmap does exactly that. It’s not just a guide, but a practical resource to help you understand what to learn and why it matters. If you’re a software engineer, team lead, or architect, this is your chance to take a step back and evaluate: • What areas are you strong in? • What should you prioritize next? This roadmap isn’t just about learning—it’s about equipping yourself to solve the real-world challenges every developer faces. What part of this roadmap resonates with your journey? Share your thoughts below—I’d love to hear what you’re focusing on in 2025. Join our Newsletter to stay updated with such content with 137k subscribers here — https://lnkd.in/dCpqgbSN #data #ai #ravitanalysis #theravitshow