Core Building Blocks — 12 Fundamental Concepts (Part 1)
I'm Rudraksh Laddha — a DevOps engineer and emerging full-stack developer, passionate about building scalable, reliable systems that solve real-world problems.
With a solid foundation in cloud infrastructure automation using tools like Kubernetes, Docker, Terraform, and AWS, I thrive in environments where efficiency, resilience, and automation are key.
But my journey doesn't stop at infrastructure. I'm actively expanding into full-stack development, building dynamic applications using React, Node.js, and MongoDB. Whether it's designing cloud-native CI/CD pipelines or developing intuitive user interfaces, I enjoy creating end-to-end solutions — from server to screen.
Right now, I'm: 🧩 Building full-stack applications that merge DevOps reliability with engaging frontend experiences 🛠️ Contributing to open-source projects, learning through collaboration and real-world scenarios 🚀 Growing Virendana Ui, my own UI library focused on expressive, clean design systems 🚀 Growing Learn Virendana, where I share my personalized learning journey — from beginner to experienced 🎮 Developing side projects like 2048 Rush, blending product thinking with scalable infrastructure My long-term goal? To bridge DevOps and development — building products that are not just functional and fast, but also resilient, beautiful, and ready for scale.
Each concept below follows the same structure: What it is → Why it exists → When to use → When NOT to use → Trade-offs → Real examples → Diagram → Beginner mistakes.
The 12 Concept are :
Requirements Clarification
Back-of-Envelope Estimation
Databases — SQL vs NoSQL
Caching
Load Balancing
API Design
Message Queues + Event-Driven Systems
Consistency vs Availability (CAP Theorem)
Scaling Strategies
Fault Tolerance + Reliability
.CDN (Content Delivery Network)
Data Partitioning (Sharding)
But in this article we cover only two concept
1. Requirements Clarification
What it is: The process of translating an ambiguous problem into a precise, bounded spec.
WHY it exists: Ambiguity is the enemy of design. 'Design Twitter' could mean 1,000 different systems depending on scope.
When to use: Always. First. Before any component.
When NOT to skip: Never skip this. Even 5 minutes of clarification saves hours of rework.
Trade-offs: More time clarifying = less time building. But building the wrong thing is worse.
Real example: Google Maps v1 wasn't Google Maps today. It was turn-by-turn directions for a browser. Scope was tight.
Beginner mistake: Assuming you know what the problem means. Ask. Always ask.
2. Back-of-Envelope Estimation
What it is: Quick math to determine system scale before designing anything.
WHY it exists: Scale determines architecture. 1,000 users needs a single server. 100M users needs sharding, caching, CDNs.
Key numbers to memorize:
| Unit | Value |
|---|---|
| 1 day in seconds | 86,400 |
| 1 month in seconds | 2.5M |
| 1 KB | 1,000 bytes |
| 1 MB | 1M bytes |
| Average tweet size | ~280 bytes |
| Average photo | ~300 KB |
| Average video (1 min HD) | ~150 MB |
The estimation flowchart:
DAU → actions/day → QPS
QPS × record_size → bandwidth
QPS × record_size × 86400 → storage/day
Thresholds that change your design:
QPS > 1,000 → consider load balancer
QPS > 10,000 → definitely need cache
Storage > 1TB → think about sharding
Storage > 10TB → definitely need distributed storage
Beginner mistake: Skipping this and going straight to components. Your cache size, shard count, and server count all come from estimation.
