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Distributed Systems Engineering: Building Scalable, Reliable & Secure Systems

Learn how production distributed systems actually work. CAP, consensus (Raft/Paxos), distributed data, scalability, reliability, Zero Trust,...

What You Will Learn

The most practical distributed systems course you can take for free. Twelve modules walk you from foundations (CAP, latency, fault tolerance) through networking (gRPC, retries, load balancing), event-driven systems (Kafka, NATS), distributed data (replication, sharding, quorums), consensus (Raft, etcd, leader election), scalability (autoscaling, caching, rate limiting), reliability engineering (circuit breakers, chaos), Zero Trust (SPIFFE/SPIRE, mTLS, OPA), observability (OpenTelemetry, tracing), Kubernetes cloud-native architecture, real failure scenarios (split brain, retry storms, cache stampede), and production system design. Architecture-first. Diagram-heavy. Hands-on labs every module. Built for engineers who operate real systems.

12 modules, 36+ hands-on labs, 50+ hours, Beginner to Advanced, 100% free.

  • Backend Engineers stepping into distributed systems work
  • Platform Engineers building internal developer platforms
  • DevOps Engineers operating distributed infrastructure
  • SREs responsible for production reliability
  • Software architects designing scalable systems
  • Engineers preparing for senior/staff-level system design
  • Beginners who want a structured foundation in modern distributed systems

Full Curriculum

  1. Module 1: Foundations of Distributed Systems

    What a distributed system actually is, why we build them, and the trade-offs that define every design decision after this point. 3 hours. 3 hands-on labs.

    • Define a distributed system from a production-engineering perspective
    • Understand why distributed systems replace monoliths and what it costs you
    • Internalise CAP and PACELC as decision frameworks, not academic theorems
    • Reason about latency, availability, fault tolerance, and consistency as a coupled system
    • Build the mental model that every later module depends on
  2. Module 2: Networking & Distributed Communication

    How services actually talk: TCP, HTTP/2, gRPC, service discovery, load balancing, retries, and the timeout discipline that keeps systems from melting. 4 hours. 3 hands-on labs.

    • Read a TCP/IP packet flow and explain what each layer does in production
    • Compare HTTP/1.1, HTTP/2, and gRPC and pick the right one per workload
    • Implement service discovery without inventing a worse DNS
    • Design retry, timeout, and load-balancing policies that survive load
    • Diagnose and prevent retry storms before they cause outages
  3. Module 3: Event-Driven & Asynchronous Systems

    How Kafka, RabbitMQ, NATS, and pub/sub patterns let services decouple in time and scale — and the failure modes that come with them. 4 hours. 3 hands-on labs.

    • Choose between message queues, pub/sub, and event streaming for a given workload
    • Reason about partitioning, ordering, and consumer groups in Kafka
    • Implement backpressure correctly so producers do not melt consumers
    • Design exactly-once semantics where you actually need them — and at-least-once where you do not
    • Diagnose the canonical event-pipeline outages: lag spikes, rebalances, and stuck consumers
  4. Module 4: Distributed Data Management

    How modern systems split, replicate, and reconcile data across many machines — replication, sharding, quorums, consistency models, and the distributed databases that implement them. 5 hours. 3 hands-on labs.

    • Pick between hash and range partitioning based on access patterns
    • Design replication strategies (single-leader, multi-leader, leaderless) and their failover behaviour
    • Apply quorum math (W + R > N) to choose consistency levels
    • Read a Cassandra / DynamoDB / PostgreSQL replication topology and predict its failure modes
    • Avoid the classic distributed-data anti-patterns: hot partitions, replication lag, write conflicts
  5. Module 5: Consensus & Coordination

    How distributed nodes agree — Raft, Paxos, leader election, distributed locking, and the etcd / ZooKeeper / Consul systems that production runs on. 5 hours. 3 hands-on labs.

    • Explain consensus as a problem and why it is fundamental to CP systems
    • Walk through Raft leader election, log replication, and safety in detail
    • Compare Raft and Paxos and pick between them in practice
    • Implement distributed locking correctly (with fencing tokens, not just SETNX)
    • Operate etcd, ZooKeeper, or Consul without taking down your cluster
  6. Module 6: Scalability Engineering

    Horizontal scaling, autoscaling, caching, CDNs, rate limiting — how production systems handle 10x and 100x traffic without 10x and 100x cost. 4 hours. 3 hands-on labs.

    • Design stateless services that scale horizontally without coordination
    • Pick the right caching strategy (cache-aside, write-through, write-back) for the workload
    • Configure Kubernetes HPA, VPA, and Cluster Autoscaler so they actually work
    • Implement distributed rate limiting that survives multi-region
    • Identify the scalability bottleneck before it becomes the outage
  7. Module 7: Reliability & Failure Engineering

    Circuit breakers, bulkheads, graceful degradation, and chaos engineering — how reliability is engineered, not hoped for. 4 hours. 3 hands-on labs.

    • Design retry policies that survive a downstream brownout
    • Implement circuit breakers and understand the half-open state
    • Apply bulkhead isolation to prevent noisy neighbours
    • Build graceful degradation paths that turn outages into reduced functionality
    • Run chaos experiments without breaking production
  8. Module 8: Distributed Security & Zero Trust

    How modern distributed systems authenticate workload-to-workload — mTLS, SPIFFE/SPIRE, OPA, and the Zero Trust patterns that replace network-perimeter security. 5 hours. 3 hands-on labs.

    • Explain Zero Trust as an architectural principle, not a product
    • Bootstrap mTLS between services with short-lived, automatically-rotated credentials
    • Use SPIFFE/SPIRE to issue cryptographic workload identity at scale
    • Enforce authorization with OPA / Rego at admission and at request time
    • Federate trust across clusters and clouds without leaking secrets
  9. Module 9: Observability & Debugging

    Distributed tracing, metrics, structured logging, correlation IDs, and the OpenTelemetry / Prometheus / Grafana / Jaeger stack that lets you debug systems you cannot SSH into. 4 hours. 3 hands-on labs.

    • Instrument a service with OpenTelemetry traces, metrics, and logs
    • Correlate a single request across many services via trace IDs
    • Build the four golden signals (latency, traffic, errors, saturation) in Prometheus
    • Read a distributed trace and identify where latency accrues
    • Build the runbook a 3am on-call engineer actually uses
  10. Module 10: Kubernetes & Cloud Native Distributed Systems

    How Kubernetes changes distributed-systems design — cluster architecture, service mesh, ingress, autoscaling, and the operational primitives that everything else now sits on top of. 5 hours. 3 hands-on labs.

    • Read a Kubernetes cluster architecture (control plane, kubelet, kube-proxy, etcd, CNI)
    • Use Services, Ingress, and Gateway API correctly for distributed workloads
    • Compare service meshes (Istio, Linkerd, Cilium) and pick one with eyes open
    • Run StatefulSets, PVCs, and storage classes for stateful workloads
    • Operate workloads with HPA, VPA, Karpenter, and PodDisruptionBudgets in production
  11. Module 11: Real-World Failure Scenarios

    Retry storms, cache stampedes, split brain, hot partitions, queue overload, DNS outages, service-discovery failures, cascading failures — the incidents that actually happen, and how to engineer them away. 5 hours. 3 hands-on labs.

    • Recognise the canonical distributed-systems failure modes by their telemetry signatures
    • Reproduce each failure in a controlled lab so the pattern is in your hands
    • Apply the architectural defences that make each failure hard or impossible
    • Write incident runbooks that an on-call engineer can actually use at 3am
    • Run a post-incident review that produces lasting improvements
  12. Module 12: Production System Design & Capstone

    Multi-region architecture, disaster recovery, capacity planning, real production tradeoffs — and a capstone that integrates every module into one system. 6 hours. 3 hands-on labs.

    • Design a multi-region architecture and reason about its failure modes
    • Plan disaster recovery: RPO, RTO, runbooks, tested restoration
    • Design for cost — capacity reservations, autoscaling, regional placement
    • Read production architecture diagrams (Kafka, Kubernetes, Netflix, Uber, Google) and identify the trade-offs
    • Design a complete production system as the capstone and defend the choices

Course Topics

Distributed Systems, Cloud Native, Kubernetes, Architecture, Scalability, Reliability, Zero Trust, SPIFFE, SPIRE, mTLS, Observability, OpenTelemetry, Raft, Consensus, Kafka, Service Mesh, Production Engineering, SRE, Platform Engineering

Instructor

Vishal Anand

Senior Product Engineer & Tech Lead

Senior Product Engineer and Tech Lead with hands-on experience building production distributed systems at scale. Creator of DRF API Logger (1.6M+ downloads) and the Mastering SPIFFE & SPIRE course. Teaches engineering from operational reality — no theory without code, no concepts without labs.

  • Creator of DRF API Logger — 1.6M+ downloads
  • Author of Mastering SPIFFE & SPIRE — comprehensive workload identity course
  • Author of Cloud Native Security Engineering — 16-module free course
  • Builds and operates production distributed systems

Frequently Asked Questions

Is this course beginner-friendly?

Yes. Module 1 builds the mental model from scratch, and every subsequent module begins with foundational concepts before going production-deep. You should be comfortable with basic programming and Linux command line; everything distributed-systems-specific is taught in the course.

How is this different from Designing Data-Intensive Applications?

DDIA is the canonical book on distributed-systems theory and the algorithms layer. This course focuses on the operational and production-engineering layer: how Kubernetes changes the game, how Zero Trust integrates, how to run real systems with observability, how failure scenarios actually unfold. Read DDIA alongside this course; the two complement each other.

Does the course require Kubernetes experience?

No. Modules 1–9 are platform-agnostic. Module 10 introduces Kubernetes from the ground up, and Modules 11–12 use Kubernetes as the deployment substrate. If you already operate Kubernetes, you can skim Module 10.

Is the course free?

Yes. Every module, every lab, and every diagram is 100% free and ad-free. No paywall, no signup wall.

How do the labs work?

Each lab includes a self-contained scenario you can reproduce on a laptop with Docker or kind (Kubernetes in Docker). Lab repos are linked from each module. Labs are 30–90 minutes each and produce concrete operational outputs you can show in interviews.

How does this course relate to the Mastering SPIFFE & SPIRE course?

They are complementary. Mastering SPIFFE & SPIRE goes deep on workload identity. Module 8 of this course introduces SPIFFE/SPIRE and Zero Trust at the level you need to design distributed-systems security. Take the SPIFFE & SPIRE course after Module 8 if you want the full identity-system depth.