Backend Engineering Concept by Concept

Java from first class to expert — JVM internals, OOP, Collections, Streams, Concurrency, Java 21 features, and interview-ready deep dives.

Spring Boot concepts from first app to production — auto-configuration, REST APIs, Spring Data JPA, Spring Security, testing, Actuator, and advanced tuning.

Hibernate & JPA from entity mapping to production performance — relationships, JPQL, Spring Data JPA, N+1 problem, caching, and distributed transactions.

Python from first print() to production — fundamentals, data structures, OOP, generators & decorators, asyncio, and the libraries that matter: requests, Pydantic, SQLAlchemy, pytest, NumPy, pandas.

FastAPI from first endpoint to production — path operations, Pydantic validation, dependency injection, databases, JWT auth, async, WebSockets, testing, and deployment.

Written for engineers who already know a backend language — the language core, async and the event loop, the browser platform, TypeScript end to end, and the tooling and testing a frontend job assumes you know.

Semantic HTML, the cascade and box model, flexbox and grid, responsive design for a mid-range Android, and the accessibility a reviewer checks first — CSS as a set of models, not a pile of tricks.

The framework job descriptions name — weighted towards where juniors actually fail: state that lives in the wrong place, effects misused, data fetching without loading and error states, and forms.

React plus a server — Server Components and the App Router, the four caching layers, server actions and forms, metadata and images, auth on the server, and what changes once it is deployed.

The seam between React and FastAPI or Spring Boot — CORS and preflights, where the token lives, the API contract, retries and idempotency, uploads and long jobs, and deploying both halves.

The mathematics that powers ML & deep learning — intuition-first with runnable NumPy, then formulas. Linear algebra, calculus, probability & statistics, optimization, information theory, and the math behind neural nets, transformers & LLMs. No PhD required.

Core DSA topics from arrays to graphs — Java code examples, complexity tables, and real interview questions from Amazon, Google, and more.

OS concepts from processes to advanced internals — threads, CPU scheduling, virtual memory, paging, TLB, synchronization, deadlocks, file systems, and I/O subsystems.

LLD from SOLID principles to full interview problems — all 23 GoF design patterns, SOLID principles, and real-world problems (Parking Lot, Elevator, Chess, Rate Limiter, LRU Cache) with full Java implementations.

Networking from OSI layers to production security — TCP/IP, HTTP versions, TLS & PKI, DNS, WebSocket, firewalls, VPN, DDoS, CDN, load balancers, API gateway, and service mesh.

Microservices from monolith decomposition to production — DDD, API gateway, circuit breaker, SAGA, CQRS, observability, and Kubernetes deployment.

Kafka from first topic to production — partitions, producers, consumers, delivery semantics, Kafka Streams, Connect, Schema Registry, security, and multi-DC replication.

RabbitMQ from first queue to production — AMQP, exchanges, queue types, reliability, dead-letter queues, clustering, quorum queues, and Spring AMQP.

MySQL from basic queries to advanced tuning — joins, indexing, transactions, ACID, MVCC, query optimisation, partitioning, replication, and high availability.

System Design from fundamentals to real-world interviews — scaling, caching, sharding, CQRS, saga, real-world designs (URL shortener, chat, news feed), and observability.

SQL from first SELECT to window functions — schema design, joins, aggregation, CTEs, recursive CTEs, window functions (ROW_NUMBER, LEAD/LAG, NTILE), indexing, and query optimisation.

Git from first commit to advanced internals — branching, merging, rebasing, undoing changes, reflog, hooks, submodules, and how Git works under the hood.

Docker from first container to production-grade deployments — images, layers, Dockerfile best practices, networking, volumes, Compose, security hardening, and internals.

Linux from filesystem basics to production hardening — permissions, processes, users, shell scripting, networking tools, systemd, performance monitoring, and security.

Kubernetes from first pod to production — Deployments, Services, Ingress, ConfigMaps, RBAC, storage, autoscaling, observability, and production patterns.

CI/CD from first pipeline to production release automation — GitHub Actions workflows, Docker build pipelines, secrets management, deploy strategies, and testing in CI.