Curriculum:
Zero to Systems Engineer
A structured learning path covering algorithms, operating systems, networking, databases, distributed systems, DevOps, security, software engineering craft, and artificial intelligence — sequenced so each topic builds on the previous.
Foundations & Tooling
The zero-to-master on-ramp — Git, the command line, a systems language, and the tooling every engineer uses daily.
Abrir página da disciplina →Git & Version Control
Master the version control system every engineering team depends on — the three-state model, branching strategies, rewriting history, collaboration workflows, and the commands behind each.
Shell & Command-Line
Become fluent in the Unix command line — the shell, filesystem navigation, pipes, redirection, process control, permissions, and text-processing tools that glue every other systems skill together.
Systems Programming Languages (C, Rust, Go)
A practical orientation to the languages systems engineers actually write — C's closeness to the machine, Rust's memory safety, and Go's concurrency — and when to reach for each.
Text Editing, Search, and Regex
Learn the editing and search workflows that turn raw files into maintainable systems — from fast navigation to refactoring with regular expressions.
Build Tools & Compilation
Understand what happens between source code and a running binary — compilers, linkers, package managers, and the build pipelines that CI systems orchestrate.
Project Structure, Packaging & Dependency Management
Monorepo vs polyrepo, workspaces, package managers across ecosystems, lockfiles, semver, and the everyday governance of what depends on what.
Testing & Debugging Fundamentals
Build the habits that let you verify changes, catch regressions early, and diagnose failures with confidence.
Programming Paradigms (OOP, FP, Concurrent)
The three paradigm families every engineer should recognise — object-oriented, functional, and concurrent — both as cognitive tools and as guidance for which language fits which problem.
Linux Services & Process Management
Understand how long-running services start, stop, fail, and recover on Linux — from processes and signals to systemd and service-level debugging.
Secure Working Practices
Learn the everyday habits that reduce risk — safe secrets handling, SSH discipline, least privilege, and trustworthy tool usage.
Documentation & Technical Communication
Learn how to write the docs, runbooks, and change notes that make engineering work understandable, reviewable, and resilient.
Software Engineering Craft
Engineering craft beyond tooling — design patterns, refactoring, code review, advanced testing strategy, and reading code you did not write.
Abrir página da disciplina →Design Patterns & Idiomatic Refactoring
A working engineer's framework for design patterns — the few worth internalizing, the ones to skip, and the principle that makes patterns useful rather than dogmatic.
Reading Legacy Code & Code Review Craft
Two skills that separate senior from junior engineers more than any other: reading code you did not write, and giving useful critique on code you did not author.
Testing Strategy Beyond Unit Tests
The test pyramid is the floor, not the ceiling. Property-based tests, contract tests, mutation testing, and snapshot tests round out a strategy that actually catches regressions.
Refactoring Legacy Code
The discipline of changing legacy code without breaking it: strangler fig, characterization pins, seam analysis, and the courage needed to delete.
API & Interface Design in the Small
The local craft of designing functions and interfaces — parameter choice, return shapes, versioning, deprecation, and the small rules that make APIs survive their callers.
Core Computer Science
Data structures, algorithms, and the core CS foundations — plus an optional advanced track for expert topics.
Abrir página da disciplina →Operating Systems
Processes, IPC (including semaphores), scheduling, memory, I/O, file systems, virtualization, concurrency models, performance profiling, and the hardware-software interface.
Abrir página da disciplina →Computer Hardware Fundamentals
Build a mental model of the parts a process runs on — CPU, memory, storage, input/output, and buses — and how the OS coordinates them so many processes can share the machine. The quantitative cost model and the fetch-execute loop are covered in Computer Architecture Fundamentals.
Computer Architecture Fundamentals
Understand the hardware the OS virtualizes — CPU pipelines, the memory hierarchy, caches, storage tiers, and how the fetch-execute loop becomes the foundation for every systems topic that follows.
OS Overview: Kernel, Syscalls & Abstractions
Understand the role of an operating system as an intermediary between hardware and software, covering kernel types, user vs kernel mode, system calls, the boot process, and the fundamental abstractions provided by modern OSes.
Process Management & Scheduling
Learn how the OS creates, schedules, and terminates processes — including process states, context switching, and the classic CPU scheduling algorithms with an interactive Gantt-chart visualizer.
Threads & Concurrency
Explore threads as lightweight units of execution within a process, covering thread models, synchronization primitives, and common concurrency challenges like races and deadlock.
Memory Management & Virtual Memory
Dive into how the OS manages physical and virtual memory — paging, segmentation, page tables, TLB, and the mechanics of virtual address translation — with an interactive page-replacement simulator and address translator.
System Calls & Kernel Interface
Examine the boundary between user space and kernel space — how system calls work, their performance cost, and the standard interfaces (POSIX and Win32) programs use, with syscall tracing.
File Systems & Storage
Understand how the OS organizes persistent data — file metadata (inodes, MFT records), directory structures, file allocation methods, journaling, and the Virtual File System layer.
Linux/Unix Fundamentals
Get hands-on with Linux — the filesystem hierarchy, users and permissions, processes and signals, systemd services, and package management — the environment where systems engineering actually happens.
Systems Performance Analysis
Brendan Gregg's toolkit for production systems performance — USE method, RED method, flame graphs, perf, eBPF, and the difference between utilisation and saturation.
Debugging & Profiling
Debug production systems like a professional — strace and gdb, profilers, core dumps, flamegraphs, and the systematic workflow that separates guessing from diagnosis.
Inter-Process Communication (IPC)
Understand the mechanisms processes use to exchange data — pipes, named pipes, message queues, shared memory, sockets, and signals — and how to choose the right one for a given job.
Deadlock & Resource Allocation
Go deep on deadlock — resource allocation graphs, wait-for graphs, detection, prevention, and avoidance with the Banker's algorithm, including a full safe-sequence worked example.
I/O Systems: Interrupts, DMA & Devices
Follow a byte from a program's read() all the way to a device — memory-mapped I/O, interrupts, DMA, blocking vs async I/O, and the block I/O stack that makes storage fast.
Virtualization & Containers
Understand how one physical machine runs many isolated OSes or applications — Type 1 and Type 2 hypervisors, hardware-assisted virtualization, and containers built from namespaces and cgroups.
OS Security & Access Control
Explore the operating system as a security boundary — DAC vs MAC, ACLs and capabilities, privilege separation, sandboxing, TOCTOU races, and secure boot.
Memory Allocation & Allocators
Go inside the heap — free lists, first/best-fit strategies, segregated lists, thread caches and arenas, kernel slab allocators, fragmentation, and overcommit.
Multiprocessor & Real-Time Scheduling
Extend CPU scheduling to many cores and to deadlines — SMP scheduling, load balancing, CPU affinity and NUMA, the Linux CFS and Windows schedulers, and real-time policies like EDF and rate-monotonic.
Firmware, Secure Boot & Hardware Security
The software stack below the operating system — UEFI Secure Boot, the TPM, side-channel attacks, TEEs, and the silicon that defends against tampering with the bootloader.
Networking
How systems communicate — TCP/IP, HTTP, DNS, load balancing, and security.
Abrir página da disciplina →Network Models (OSI, TCP/IP)
Understand the layered architecture of computer networks — the OSI model's seven layers and the practical TCP/IP model that powers the internet.
TCP & UDP Deep Dive
Master the two core transport layer protocols — TCP's reliable connection-oriented delivery vs UDP's lightweight best-effort datagrams.
IP Addressing, Subnetting, NAT & IPv6
CIDR, private ranges, NAT traversal, and the IPv6 migration every operator must understand — the bread-and-butter operational skills behind every network decision.
HTTP/HTTPS & REST APIs
Dive into the application layer protocol that powers the web — request/response model, methods, status codes, caching, and secure HTTPS connections.
gRPC, Protobuf & RPC Internals
Binary framing, HTTP/2 multiplexing,IDL-driven contracts, streaming RPCs, and the trade-offs of REST vs gRPC vs GraphQL in a platform tier.
HTTPS, TLS & Certificates
Go beneath the lock icon — the TLS 1.3 handshake, certificate authorities and the chain of trust, forward secrecy with ECDHE, 0-RTT session resumption, and the operational pitfalls that break HTTPS in production.
QUIC & HTTP/3 Internals
What changes when HTTP moves from TCP to UDP — QUIC's streams, 0-RTT, connection migration, and the production significance of HTTP/3.
DNS & Load Balancing
Understand the internet's phone book — the DNS hierarchy, record types, and resolution flow — plus the load-balancing patterns that keep web services available.
Wireless Networking & Wi-Fi Fundamentals
Master how wireless differs from wired networking — the 802.11 standard family, radio bands, CSMA/CA medium access, MAC frames, and how access points serve stations.
Proxies, VPNs & Tunneling
Explore how proxies, VPNs, and tunneling protocols enable secure remote access, traffic forwarding, and network segmentation.
Network Security Fundamentals
Cover the essential security concepts for network engineers — firewalls, IDS/IPS, segmentation, and common attack vectors.
Wi-Fi Security: WEP, WPA2 & WPA3
Understand how wireless security evolved — from breakable WEP to WPA2's 4-way handshake and WPA3's Simultaneous Authentication of Equals — plus enterprise 802.1X and the attacks each generation defends against.
API Design & Integration
Design APIs that survive contact with real clients — versioning, pagination, idempotency, error semantics, and when REST gives way to gRPC or GraphQL.
WebSockets, SSE & Realtime Systems
Realtime server push — long-polling, SSE, WebSockets, presence, fan-out/RPC fanout, and the trade-offs that decide which transport a realtime system uses.
Cellular, Mobility & Handover (4G/5G)
Understand the architecture behind cellular data and seamless roaming — radio access networks, base stations, core networks, handover, and why your connection survives moving between cells.
Databases
SQL, indexing, transactions, replication, caching, and when to use NoSQL.
Abrir página da disciplina →SQL Fundamentals & Schema Design
Learn the foundations of relational databases — SQL syntax, table design, relationships, and normalization principles.
Data Modeling & Schema Design
The shape of data decides the shape of everything downstream — normalization, denormalization, dimensional modeling, and when to break the rules.
Indexing & Query Optimization
Understand how database indexes work — B-tree structures, query planning, EXPLAIN analysis, and how to optimize slow queries.
Schema Migrations & Zero-Downtime Evolution
Databases outlive every application version. Schema migrations — expand/contract, backfills, online schema changes — are the discipline of evolving storage without downtime.
Transactions & ACID Properties
Master database transactions — ACID guarantees, isolation levels, read phenomena, and how databases handle concurrent access.
Replication & Sharding
Explore horizontal scaling strategies — leader-follower replication, multi-leader setups, and sharding techniques for distributed databases.
NoSQL Overview & Use Cases
Survey the NoSQL landscape — document stores, key-value, column-family, and graph databases — and when to choose each over SQL.
Caching & CDNs
The fastest data is the data you already have — caching strategies, invalidation, Redis, browser and edge caches, and why caching is the first lever in every latency budget.
Timeseries & Columnar Databases (OLAP)
Why columnar stores dominate analytics — column-major layout, compression, vectorised execution, and the timeseries workload that lives on top.
Storage Engines & LSM Trees
What actually stores your data — B-trees and LSM trees, write-ahead logs, memtables, compaction, and how RocksDB/Cassandra/LSM-based engines optimize for write-heavy workloads.
Data Warehouses, Lakes & Lakehouses
The analytical data stack — Kimball vs Inmon data warehousing, the data lake, the lakehouse, ETL/ELT, and the trade-offs behind modern table formats (Iceberg, Delta).
NewSQL & Distributed SQL
Spanner, CockroachDB, TiDB, Calvin — the lineage of systems that offer SQL across geo-distributed nodes with strong consistency and horizontal scale.
Distributed Systems
CAP theorem, consensus, message queues, microservices, and system design at scale.
Abrir página da disciplina →Distributed Systems Fundamentals
Understand the core challenges of distributed computing — fallacies, time/order, failure models, and the fundamental differences from single-machine systems.
CAP Theorem & Consistency Models
Master the CAP theorem's consistency-availability-partition tolerance trade-off, plus strong vs eventual consistency models used in real systems.
Consensus Algorithms (Raft/Paxos)
Learn how distributed systems agree on a single value despite failures — Raft's understandable approach and Paxos's elegant foundation.
Distributed Data Structures
The structural primitives that hold distributed systems together — consistent hashing with virtual nodes, Merkle trees, Gossip/SWIM, and the data structures behind ring-shaped coordination.
Message Queues & Stream Processing
Explore asynchronous communication patterns — publish/subscribe, message brokers, stream processing, and exactly-once semantics.
Microservices Architecture & Patterns
Design and reason about microservice systems — service decomposition, communication patterns, service discovery, and observability at scale.
Caching Strategies at Scale
The distributed-systems view of caching — multi-level hierarchies, stampede prevention, dogpile/bounding, Bloom filters as cache guards, and CDN invalidation as a hard problem.
Distributed Transactions & Saga Patterns
Coordinate state across services without a single ACID database — two-phase commit, the saga pattern, idempotency, and outbox tables in the real world.
Backpressure, Load Shedding & Concurrency Limits
Traffic shaping under overload — token buckets, adaptive concurrency, timeout budgets, hedged requests, and the laws that govern graceful degradation.
System Design Practice
The engineering craft of designing systems at scale — back-of-envelope math, capacity planning, trade-off analysis, and worked designs for canonical problems like rate limiters, URL shorteners, and chat services.
DevOps & Infrastructure
CI/CD, containers, orchestration, infrastructure as code, cloud, and observability.
Abrir página da disciplina →CI/CD Pipelines
Automate the software delivery lifecycle — continuous integration, continuous delivery/deployment, pipeline stages, and best practices.
Containerization with Docker
Master container fundamentals — images, containers, Dockerfile best practices, multi-stage builds, and container networking.
Orchestration with Kubernetes
Deploy, scale, and manage containerized applications with Kubernetes — pods, deployments, services, ingress, and cluster architecture.
Infrastructure as Code (Terraform)
Manage cloud infrastructure declaratively — Terraform state, providers, modules, and best practices for infrastructure reproducibility.
Secrets Management & Configuration
The operational lifecycle of secrets — Vault, KMS, SOPS, dynamic vs static, rotation, dynamic secret generation, and the fail state of `.env` files in production.
Monitoring, Logging & Observability
Build observable systems — metrics, logs, traces, alerting, and the three pillars of observability in production environments.
Cloud Platform Fundamentals
A vendor-neutral tour of cloud computing — compute, storage, networking, serverless, managed services, and the cost/operability trade-offs of each abstraction.
SLOs, Incident Response & On-Call
Run services you can trust in production — SLI/SLO/error budgets, alerting and paging, incident response, blameless postmortems, and the discipline of reliability engineering.
Release Engineering: Canary, Blue/Green, Feature Flags
What happens after the artefact ships — release strategies that contain blast radius, progressive delivery, feature-flag-driven decoupling, and the rollback that you wish you never need.
Cloud Cost Engineering & FinOps
Treating cloud spend as an engineering concern — unit economics, tagging strategy, right-sizing, committed use, spot/preemptible, and the multi-region cost trade-offs every platform team owns.
Chaos Engineering & Resilience Testing
SRE's sister discipline — fault injection, ChaosMesh, Gremlin, game days, blast-radius doubling, and the hypothesis-driven practice of breaking things on purpose.
Security
Authentication, encryption, network security, application security, and secure system design.
Abrir página da disciplina →Authentication & Authorization Models
Understand user identity and access control — password auth, JWT, OAuth2, SAML, RBAC, and modern auth protocols.
Identity & Access Management Patterns
RBAC vs ABAC, SCIM, OAuth scopes, OIDC flows, JWT validation pitfalls, token replay — the implementation patterns behind the auth models topic.
Cryptography & Encryption Basics
Cover the essential cryptographic primitives — symmetric and asymmetric encryption, hashing, digital signatures, and TLS.
Network Security & Firewalls
Deepen network security knowledge — firewall rules, security groups, network ACLs, WAFs, DDoS protection, and zero-trust architectures.
Secure System Design Principles
Apply security-by-design principles — threat modeling, least privilege, defense in depth, secrets management, and security reviews.
Threat Modeling (STRIDE, DREAD, Attack Trees)
Threat modeling as a practiced skill — STRIDE per element, DREAD for ranking, attack trees for adversary reasoning, and the data-flow diagram as the working surface.
Application Security (OWASP & Secure Coding)
Defense at the source — the OWASP Top 10, input validation, injection, XSS, CSRF, insecure deserialization, and the secure-coding habits that prevent vulnerabilities before they ship.
Supply Chain Security
The software supply chain is now an attack surface — SBOMs, signed artifacts, Sigstore/Cosign, dependency vetting, and SLSA as the maturity ladder.
Artificial Intelligence
Machine learning, neural networks, LLMs, retrieval-augmented generation, model serving, and the systems engineering behind production AI.
Abrir página da disciplina →AI Fundamentals
Learn the basic architecture of AI systems — model, prompt, context, and feedback — in a way that makes the workflow understandable.
Prompting & Evaluation
Practice the prompt loop that drives reliable AI output — define the goal, write the instruction, review the answer, and refine.
Machine Learning Fundamentals
Learn the core vocabulary and workflow of machine learning — supervised vs unsupervised learning, features and labels, train/test splits, and the overfitting trap.
Neural Networks & Deep Learning
Understand how neural networks learn — neurons, weights, activation functions, the forward pass, loss, backpropagation, and gradient descent.
LLMs & the Transformer
Understand how large language models actually work — tokens, embeddings, self-attention, context windows, and autoregressive generation.
Retrieval-Augmented Generation
Learn how RAG grounds LLM answers in your own documents — chunking, embeddings, vector retrieval, ranking, and cited generation.
Model Serving & Inference
Learn how trained models become fast, reliable production endpoints — batching, quantization, GPU inference, latency budgets, and autoscaling.
ML Operations
Learn the operational discipline that keeps ML systems correct in production — experiment tracking, data pipelines, evaluation, drift, and CI/CD for models.
Vector Databases
Learn how vector databases serve millions of embeddings — ANN indexes, HNSW and IVF, cosine similarity, and hybrid search.
Production LLM Systems
Learn what it takes to run LLM features in production — evals, prompt injection defenses, caching, cost control, observability, and governance.