Design and build microservices architectures. Learn patterns for scalable distributed systems. Core Principles ✅ Single responsibility ✅ Loose coupling ✅ High cohesion ✅ Independent deployment Communication Patterns Sync: REST APIs, gRPC Async: Message queues, Event bus API Gateway // Kong, AWS API Gateway, NGINX Routes requests to appropriate microservices Handles authentication, rate limiting Service Discovery … Read more
Build full-stack GraphQL applications. Implement GraphQL APIs with Apollo. Server Setup const { ApolloServer, gql } = require(‘apollo-server’); const typeDefs = gql` type Query { users: [User] user(id: ID!): User } type User { id: ID! name: String! email: String! } `; const resolvers = { Query: { users: () => users, user: (_, { … Read more
Use Prisma for type-safe database operations in Node.js. Modern ORM with excellent developer experience. Setup npm install prisma @prisma/client npx prisma init Schema // prisma/schema.prisma model User { id Int @id @default(autoincrement()) name String email String @unique posts Post[] } model Post { id Int @id @default(autoincrement()) title String content String? author User @relation(fields: [authorId], … Read more
Optimize MySQL for high-performance applications. Learn indexing, query optimization, and configuration. Indexing Strategies — B-tree index (default) CREATE INDEX idx_name ON users(email); — Composite index CREATE INDEX idx_composite ON orders(user_id, created_at); — Full-text index ALTER TABLE posts ADD FULLTEXT(title, content); Query Optimization EXPLAIN SELECT * FROM users WHERE email = ‘test@example.com’; Configuration Tuning innodb_buffer_pool_size = … Read more
Master MongoDB 7 for modern application development. Learn document database concepts and best practices. Why MongoDB? ✅ Flexible schema ✅ Horizontal scaling ✅ JSON-like documents ✅ Powerful aggregation Basic Operations // Connect const { MongoClient } = require(‘mongodb’); const client = new MongoClient(uri); // Insert await collection.insertOne({ name: “John”, age: 30 }); // Find const … Read more
Containerize Python applications with Docker. Deploy Flask/FastAPI apps with Docker Compose. Dockerfile FROM python:3.13-slim WORKDIR /app COPY requirements.txt . RUN pip install –no-cache-dir -r requirements.txt COPY . . CMD [“uvicorn”, “main:app”, “–host”, “0.0.0.0”] docker-compose.yml version: ‘3.8’ services: web: build: . ports: – “8000:8000” redis: image: redis:7-alpine Commands docker-compose up –build docker-compose down Conclusion Docker simplifies … Read more
Process background tasks with Celery. Handle long-running tasks asynchronously. Setup # tasks.py from celery import Celery app = Celery(‘tasks’, broker=’redis://localhost’) @app.task def send_email(to, subject): # Send email logic return “sent” Calling Tasks result = send_email.delay(“user@example.com”, “Hello”) print(result.get(timeout=10)) Docker Compose worker: command: celery -A tasks worker Conclusion Celery enables scalable background processing!
Write better tests with pytest. Master testing patterns for Python applications. Basic Test def add(a, b): return a + b def test_add(): assert add(2, 3) == 5 assert add(-1, 1) == 0 Fixtures import pytest @pytest.fixture def client(): return TestClient(app) def test_index(client): response = client.get(“/”) assert response.status_code == 200 Parametrization @pytest.mark.parametrize(“input,expected”, [ (1, 1), (2, … Read more
Build REST APIs with FastAPI step by step. Create high-performance APIs with automatic documentation. Setup pip install fastapi uvicorn Basic API from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str price: float @app.post(“/items/”) async def create_item(item: Item): return item Run Server uvicorn main:app –reload Documentation Open http://localhost:8000/docs Conclusion FastAPI … Read more
Build ML models with scikit-learn. Learn supervised and unsupervised learning. Classification from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y) model = RandomForestClassifier() model.fit(X_train, y_train) accuracy = model.score(X_test, y_test) Pipelines from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler pipeline = Pipeline([ (‘scaler’, StandardScaler()), (‘classifier’, RandomForestClassifier()) ]) Conclusion scikit-learn makes … Read more