Intermediate to Advanced|16 hours|38 lessons

Architecture and Scale

How to structure systems for scale. Architectural patterns, microservices patterns, the specialized data structures behind large-scale systems (geospatial indexing, probabilistic structures), and big data processing architectures. Built for senior engineers who need to design systems that actually hold up at scale, in interviews and in production.

Text-based, no videos

4 modules, 38 lessons

Lifetime access

What you'll learn

Choose between monolith, microservices, serverless, and event-driven with real reasoning

Apply CQRS and event sourcing, and know when their complexity is not justified

Design a service estate with discovery, gateways, sidecars, and a service mesh

Contain failure with circuit breakers and bulkheads, and migrate with strangler fig

Index geospatial data with geohash, quad trees, R-trees, S2, and H3

Use probabilistic structures: bloom and cuckoo filters, HyperLogLog, count-min sketch, MinHash

Compare merkle trees and skip lists, and know where each is used in production

Design data platforms with lakes, warehouses, lakehouses, lambda, kappa, and streaming engines

Curriculum

4 modules · 38 lessons

Architectural Patterns

The structural choices that shape a system: client-server, monolith, microservices, serverless, event-driven, P2P, hexagonal, CQRS, and event sourcing.

9 lessons

Client-Server Architecture20 minFREE Monolithic Architecture25 minFREE Microservices Architecture30 minFREE Serverless Architecture25 minFREE Event-Driven Architecture30 minFREE Peer-to-Peer (P2P)25 minFREE Hexagonal Architecture25 minFREE CQRS30 minFREE Event Sourcing30 minFREE

Microservices Patterns

The patterns that make a service estate workable: discovery, gateways, BFFs, sidecars, circuit breakers, bulkheads, strangler fig, and service mesh.

8 lessons

Service Discovery25 min API Gateway Pattern25 min Backend for Frontend (BFF)20 min Sidecar Pattern20 min Circuit Breaker Pattern25 min Bulkhead Pattern20 min Strangler Fig Pattern25 min Service Mesh30 min

Data Structures for Scale

The specialized structures behind large-scale systems: geospatial indexing with geohash, quad trees, R-trees, S2 and H3, and probabilistic structures from bloom filters to merkle trees.

12 lessons

Introduction20 min Geohash25 min Quad Trees25 min R-Trees25 min S2 and H330 min Bloom Filters30 min Cuckoo Filter25 min HyperLogLog30 min Count-Min Sketch25 min MinHash30 min Skip Lists25 min Merkle Trees25 min

Big Data Processing

How large-scale data is moved and processed: batch against stream, MapReduce, ETL, lakes, warehouses and lakehouses, lambda and kappa, and streaming engines.

9 lessons

Batch vs Stream Processing25 min MapReduce30 min ETL Pipelines25 min Data Lakes25 min Data Warehousing30 min Data Lakehouse25 min Lambda Architecture25 min Kappa Architecture25 min Streaming Engines30 min

About the Author

Sharon Sahadevan

AI Infrastructure Engineer

Building production GPU clusters on Kubernetes. H100s, large-scale model serving, and end-to-end ML infrastructure across Azure and AWS.

10+ years designing cloud-native platforms with deep expertise in Kubernetes orchestration, GitOps (Argo CD), Terraform, and MLOps pipelines for LLM deployment.

Author of KubeNatives, a weekly newsletter read by 3,000+ DevOps and ML engineers for production insights on K8s internals, GPU scheduling, and model-serving patterns.

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