How to Clear System Design Rounds by 2025

💡𝗖𝗤𝗥𝗦: 𝗱𝗼 𝗜 𝗿𝗲𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝘁𝘄𝗼 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀? When I first started studying 𝗖𝗤𝗥𝗦, I thought it was all about having two databases: 𝗼𝗻𝗲 𝗳𝗼𝗿 𝘄𝗿𝗶𝘁𝗲𝘀 and 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗳𝗼𝗿 𝗿𝗲𝗮𝗱𝘀. 𝘐 𝘵𝘩𝘰𝘶𝘨𝘩𝘵: 𝘴𝘺𝘯𝘤𝘩𝘳𝘰𝘯𝘪𝘻𝘪𝘯𝘨 𝘵𝘩𝘦 𝘵𝘸𝘰 𝘥𝘢𝘵𝘢𝘣𝘢𝘴𝘦𝘴 𝘪𝘴 𝘨𝘰𝘪𝘯𝘨 𝘵𝘰 𝘨𝘪𝘷𝘦 𝘮𝘦 𝘵𝘳𝘰𝘶𝘣𝘭𝘦 😅 Later, I realized that this is actually the most simplified (and maybe the most misleading) view. One of the real values of CQRS lies in the 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘂𝗮𝗹 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻: • Commands handle state changes. • Queries handle reading and projections. You can even start with a 𝘀𝗶𝗻𝗴𝗹𝗲 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 — the clarity already comes from separating responsibilities. The point is: 𝗖𝗤𝗥𝗦 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 “𝗽𝗿𝗲𝘁𝘁𝘆 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲.” 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀: ➡️ Are your business rules mixed with huge joins? ➡️ Does your system need to scale reads independently from writes? In practice, you can start small (even with one database) and evolve later. The evolution should come 𝗻𝗮𝘁𝘂𝗿𝗮𝗹𝗹𝘆, when the 𝘀𝘆𝘀𝘁𝗲𝗺 𝗻𝗲𝗲𝗱𝘀 𝗶𝘁, not as a mandatory starting point. If you apply it too early, maybe you are just adding unnecessary complexity.

Choosing the right database can make or break your architecture. Most teams still rely on gut instinct or past experience — and pay for it later. So I built a Database Decision Framework that helps engineers, architects or leaders choose the right DB based on data type, access pattern, and consistency needs. This guide covers: How to navigate SQL vs NoSQL vs Specialized DBs When to favor Consistency, Availability, or Partition Tolerance Visual frameworks and decision matrices to make the call fast. If you’ve ever debated “Postgres or Mongo?” or “Do we need a graph DB?” — this is for you.

On the topic of GraphQL vs REST, let's delve into the main differences and benefits of each, along with guidance on when to employ them in your architecture design. **GraphQL:** - GraphQL is a query language for APIs that enables clients to request only the data they need. - It allows for fetching multiple resources in a single request, reducing the number of API calls. - With GraphQL, clients can specify the structure of the response, leading to efficient data retrieval. **REST:** - REST (Representational State Transfer) is an architectural style that uses standard HTTP methods for CRUD operations. - It follows a resource-based approach where each endpoint represents a resource (e.g., /users, /products). - REST is well-suited for stateless client-server communication and caching mechanisms. **Choosing Between GraphQL and REST:** - Use GraphQL when you need flexibility in data fetching, have evolving requirements, or want to reduce over-fetching of data. - Opt for REST when working with well-defined resources, leveraging HTTP caching, or following a more traditional approach to API design. By understanding the distinctions and advantages of GraphQL and REST, you can make informed decisions on which to integrate into your architecture design. #APIs #ArchitectureDesign

🌟 Day 7 — Exploring RabbitMQ Architecture & Database Indexing in System Design Today’s learning focused on RabbitMQ, a powerful message broker for asynchronous communication, and the basics of Database Indexing, which boosts query performance. 🚀 🔍 Areas Explored: • RabbitMQ Architecture: Understood how RabbitMQ enables decoupled communication between producers and consumers through exchanges, queues, and bindings for reliable message delivery. • Types of Exchanges: Fanout – Broadcasts to all queues. Direct – Routes by exact key match. Topic – Uses patterns for flexible routing. Headers – Routes based on message headers. • Database Indexing Basics: Learned how indexes, data pages, and data blocks improve data retrieval by reducing the amount of data scanned during queries. 🎯 Key Takeaway: RabbitMQ ensures smooth message flow in distributed systems, while indexing enhances database efficiency — both key to building scalable, high-performance applications. 💡 #SystemDesign #RabbitMQ #MessageQueue #DatabaseIndexing #BackendDevelopment #Microservices #ScalableSystems #LearningJourney #ContinuousLearning

System Design Cheatsheet - save it now! System design is key to building scalable, reliable, and efficient applications. Here’s a breakdown of essential concepts: 1. Fundamentals of System Design Scalability, availability, and reliability ensure systems can handle growth and maintain performance. Latency, throughput, and the CAP theorem define system trade-offs. 2. Load Balancing Techniques like round robin, least connections, and consistent hashing distribute traffic efficiently across servers to optimize performance. 3. Databases SQL is structured and ACID-compliant, while NoSQL is flexible and scalable. Scaling strategies include vertical scaling, sharding, and replication. 4. Message Queues & Event-Driven Systems Kafka, RabbitMQ, and Pub/Sub models enable asynchronous communication for high-throughput and real-time processing. 5. Caching Strategies Write-through and write-back caching improve data access speed, while eviction policies like LRU, LFU, and FIFO manage cache efficiency. 6. API & Communication REST, GraphQL, gRPC, and WebSockets facilitate data exchange and real-time communication between distributed systems. Explore more in the image below. Follow Noble Approach for more and register for your free webinar here: https://lnkd.in/eeDxDwNP

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐬𝐞𝐜𝐮𝐫𝐞, 𝐚𝐧𝐝 𝐫𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐝𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐤𝐧𝐨𝐰 𝐦𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬. 𝐋𝐞𝐚𝐫𝐧 𝐭𝐡𝐞 𝐝𝐞𝐬𝐢𝐠𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐭𝐡𝐚𝐭 𝐩𝐨𝐰𝐞𝐫 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 👇 1️⃣ Architecture & Data Patterns → CQRS (Command Query Responsibility Segregation) → Event Sourcing → Aggregator → Shared DB vs DB-per-Service → Data Management Patterns 2️⃣ Communication Patterns → Choreography vs Orchestration → Publish–Subscribe → Request–Response → Event-Driven → Service Mesh → API Gateway 3️⃣ Security Patterns → Rate Limiting → Access Control → Token-Based Auth (OAuth, JWT) → API Gateway Authentication → Encryption (In Transit & At Rest) 4️⃣ Deployment Patterns → Service Instance per Container → Blue/Green Deployment → Canary Release → A/B Testing → Serverless 5️⃣ Reliability Patterns → Circuit Breaker → Retry → Fallback → Bulkhead → Timeouts → Health Checks 6️⃣ Scalability Patterns → Autoscaling → Sharding → Load Balancing → Vertical & Horizontal Scaling 7️⃣ Observability Patterns → Distributed Logging → Distributed Tracing → Real-Time Monitoring → Auditing → Metrics Collection → Health Check (again-yes, it's that important!) - - - - - 🔗 Want to escape the 4–8 LPA trap? Check comments. follow Sandeep Bonagiri for more insights

This 3-part series explores several approaches to implement enhanced event-driven patterns for DynamoDB-backed applications. This post (Part 2) explores another method which uses global secondary indexes (GSIs) to handle fine-grained Time to Live (TTL) requirements.

Dual Write and the Transactional Outbox Pattern Introduction to the Dual Write problem When dealing with an application that uses the Event Driven Architecture (EDA), for asynchronous communication between components, we often come against a situation that we must update state in the database and after publish a messsage into a broker, or make an HTTP call. This scenario is typically called the Dual Write Problem, when we need to make an operation in two different systems at the same time and it must be atomic. As we know, those operations aren't atomic, so if we write/update a record in the database and then we have an error publishing to the broker, we need to undo our command. The Transactional Outbox Pattern comes to solve this problem in a simple, consistent, resilient and elegant way. It transforms two writes, in different systems, into just one transaction in the database. Following this approach, we ensure strong consistency between the application state, since SQL or even NoSQL databases ensure atomicity in a transaction https://lnkd.in/g3ATK2cZ

Indexes are deceptively simple but critical to database performance. They accelerate queries, reduce latency spikes, and support hybrid workloads. But over-indexing or poor strategy can slow writes, increase maintenance costs, and create unpredictable behavior. Our latest blog dives into the mechanics and strategy behind indexing, showing how to treat it intentionally. Think profile queries, index high-selectivity columns, maintain regularly, and monitor impacts. In IneryDB, we’ve built indexing into the architecture itself. From distributed nodes to multi-index containers, our system balances fast lookups with minimal overhead, making sure indexes stay efficient and reliable even as data scales. Discover the friend-enemy dynamic of indexing and learn practical tips to turn it into a powerful tool rather than a liability. Check it out here ➡️ https://lnkd.in/dpcQaSv6

Database Architecture Patterns: From Domain Models to Production-Ready Repositories Here's the uncomfortable truth: Most data layers fail not from bad code, but from hastily constructed architecture that crumbles under load. After reviewing hundreds of production systems, the data persistence layer consistently serves as both the foundation and potential bottleneck. The difference becomes evident under load, during schema evolution, or when debugging transaction anomalies at 2 AM. Our comprehensive practitioner's guide covers the patterns that actually work in production: 🎯 Repository Pattern - Martin Fowler's collection-like interfaces that mediate between domain and data layers 💡 CQRS Applied - Critical warning: "Should only be used on specific portions of a system, not the whole system" ⚡ ORM Inheritance Mapping - Single Table vs Joined Table vs Table-Per-Concrete-Class (each with specific trade-offs) ✅ Migration-Driven Development - TypeORM docs: "Unsafe to use synchronize: true in production" 📊 Transaction Isolation - When to use Read Committed vs Repeatable Read vs Serializable 🔧 Connection Pool Sizing - The formula: ((core_count × 2) + effective_spindle_count) Key insight: For a 4-core database server with one SSD = 9 connections optimal. Not 50. Not 100. This formula balances CPU utilization with disk I/O capacity. Read the full guide: https://lnkd.in/gJYk8Qqc What's your biggest database architecture challenge? 👇 #DatabaseArchitecture #PostgreSQL #TypeORM #RepositoryPattern #CQRS #SoftwareDevelopment #BackendEngineering