People who moved abroad alone in their 20s, handled all docs, bank account, visa, tax, jobs, accomadation, and culture difference
These people fear nothing anymore
Your System Design Should NOT Look the Same at 1K, 1M, and 10M Users
Most teams over-engineer too early or scale too late, and both mistakes are expensive.
This framework shows exactly how architecture must evolve as users grow.
Hereβs the simple breakdown -
πΉ 1K Users (MVP Stage)
- Optimize for speed, not complexity.
- A single monolith, basic REST APIs, manual deployments, and simple relational DBs work perfectly here.
- Your only goal: validate the idea fast.
πΉ 1M Users (Scaling Stage)
- The system must survive real traffic.
- You introduce load balancers, read replicas, queues, caching, and automated CI/CD.
- Architecture shifts toward modular monoliths or early microservices.
πΉ 10M Users (Enterprise Scale)
- The system must be highly available, globally distributed, and fault-tolerant.
- Now you need auto-scaling, API gateways, distributed databases, streaming platforms, zero-trust security, and full observability.
Good engineering is not about building the βmost advanced system.β
Itβs about building the right system for the stage youβre in, and knowing exactly when to evolve.
Backend Development Project Ideas
Beginner Level
β’ Authentication API
β’ CRUD API For Users
β’ Blog Backend
β’ URL Shortener
Intermediate Level
β’ Pagination and Search API
β’ File Upload API
β’ Rate Limiter API
β’ Muti User chat Application
Advanced Level
β’ Multi role user System
β’ O Auth2 Login API
β’ Job Queue System
β’ Recommendation System
Expert Level
β’ Video Steaming Service
β’ High Performance API With Caching
β’ Distributed File Storage System
I donβt have much desire to be so rich so I can buy a Rolex, have a Lambo or take trips to Dubai.
I want to be rich so I can control my time and go to the gym at 1pm on a Monday without worrying.
Sit at a Cafe with espresso and relax for an hour
So I can cook meals at home with clean ingredients.
Spend on my beautiful mid, family and friends without worrying about a budget.
That's my idea of a rich life, not the fake consumerist idea shoved down my throat to suffer in the suburbs.
As a backend engineer.
Please learn:
- DB
- System Design
- Algorithms
- API design
- API Security
- CI/CD
- DS
- Docker/ Kubernetes
- Caching
- Messaging
Stop jumping from one language to the other
A backend engineer asked me why I recommend Fundamentals of Data Engineering for backend folks.
Because most backend engineers accidentally become data engineers the moment their API hits production.
Backend = owns writes.
Data Engineering = owns reads.
But both eat the same consequences when the data sucks.
If you learn Data Engineering fundamentals, you level up faster because you stop treating data as βsomeone elseβs problem.β
You get better at:
- Designing schemas that wonβt break downstream
- Shipping clean, traceable events (no dual-write disasters)
- Thinking in batch vs. streaming trade-offs
- Debugging data issues faster than βblame analyticsβ
- Building APIs that scale with actual usage patterns
Backend without data fundamentals = βthe feature worksβ
Backend with data fundamentals = βthe business trusts what you buildβ
The short answer:
Most backend engineers only understand half the system.
This book helps you finally see the whole thing.
@farmDev79 Hashmap, binary search, BFS/DFS, two pointers e sliding window.
Dominando esses padrΓ΅es dΓ‘ pra matar 90% das entrevistas.
Alguns lugares pode cair dynamic programming e problemas mais βhardβ, mas sΓ£o exceΓ§Γ£o, nΓ£o a regra.
Fiz um teste online de uma firma gringa e caΓram dois algoritmos lΓ‘ que foi fogo de resolver mas consegui.
Estudem:
- greedy algorithm
- group consecutive days with same hours