Software engineering · learning path

A backend roadmap you can actually finish.

42 curated topics, 7 categories, three flavours — 36 concepts to understand, 4 projects to build, and 2 books worth reading. Bookmark, take notes, and tick things off — your progress lives in your browser, no account needed.

Found.APIs Patterns Data Scale Distrib.Ops

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Engineering Foundations

The everyday base layer: Git, Linux, debugging, complexity, runtimes, and dependency hygiene.

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Git and version control

Commits, branches, merges, rebases, pull requests, and safe recovery.

Command line and Linux basics

Shell navigation, files, processes, permissions, pipes, and service inspection.

Debugging workflow

Reproduce, isolate, inspect logs, form hypotheses, fix, and verify.

Data structures and complexity

Arrays, maps, sets, trees, queues, graphs, Big O, and trade-offs.

Language runtime basics

Memory, stack vs heap, garbage collection, async I/O, threads, and processes.

Dependency management

Packages, lockfiles, semantic versioning, supply-chain risk, and upgrades.

API & Networking

The contract between services and the protocols that carry them — HTTP, REST, auth, networking primitives.

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HTTP fundamentals

Methods, headers, status codes, request/response cycle.

REST API design

Resources, endpoints, CRUD mapping, versioning.

Data formats

JSON, XML, Protobuf, schema validation.

Build a REST API

Create CRUD endpoints with validation and error handling.

Authentication

API keys, JWT tokens, OAuth2 flows.

Networking basics

DNS, TCP/IP, TLS, ports, sockets.

Design Patterns & Principles

Reusable shapes for organising code: creational, structural, behavioural, and the SOLID principles.

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Creational patterns

Singleton, Factory, Builder — when and why.

Structural patterns

Repository, Adapter, Decorator, Facade.

Behavioral patterns

Observer, Strategy, Chain of Responsibility.

SOLID principles

SRP, OCP, LSP, ISP, DIP — the backbone of clean code.

Middleware pattern

Request pipelines: auth → logging → validation → handler.

Head First Design Patterns

Most approachable book on design patterns.

Databases

How data is modelled, stored, indexed, cached and kept consistent under pressure.

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SQL vs NoSQL

Relational, document, key-value, graph — tradeoffs.

Indexing and queries

B-trees, composite indexes, query plans, N+1 problem.

Data modeling

Normalization, denormalization, schema design.

Caching with Redis

Cache-aside, write-through, TTL, invalidation.

Transactions and ACID

Isolation levels, deadlocks, optimistic locking.

Design a schema

Model an e-commerce system end-to-end.

Scalability & Resilience

Keeping systems fast and standing under load: scaling, queues, fault tolerance, the CAP triangle.

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Scaling strategies

Horizontal vs vertical, stateless design, connection pooling.

Load balancing

Round-robin, least-connections, health checks.

Message queues

Kafka, RabbitMQ, SQS — async processing.

Fault tolerance

Circuit breaker, retries, backoff, graceful degradation.

Rate limiting

Token bucket, sliding window, API throttling.

CAP theorem

Consistency, availability, partition tolerance tradeoffs.

Distributed Systems

Shapes of large architectures: monoliths, microservices, events, gateways, and classic design exercises.

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Monolith vs microservices

When to split, modular monolith as middle ground.

Event-driven architecture

Event sourcing, CQRS, pub/sub patterns.

API gateway

Routing, aggregation, cross-cutting concerns.

GraphQL and gRPC

Beyond REST — when to use alternatives.

Design a URL shortener

Classic system design exercise.

Design a chat system

WebSockets, presence, message delivery.

Production & Operations

Everything that surrounds the code in real life: observability, deploys, containers, migrations, docs.

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Observability

Logging, metrics, tracing, alerting.

CI/CD and deployment

Blue-green, canary, rolling deployments.

Containers and K8s

Docker, Kubernetes, service discovery.

API documentation

OpenAPI, Swagger, developer experience.

Database migrations

Schema evolution, zero-downtime changes.

DDIA book

Designing Data-Intensive Applications by Kleppmann.