Cloud Fundamentals
Regions, zones, the resource hierarchy, billing and the shared responsibility model — the vocabulary every other topic assumes.
Cloud Fundamentals
Learning Hub
25 topics explaining what each cloud service does, why it exists, and which certification tests it. Written in our own words — we explain concepts rather than paraphrasing documentation.
Cloud Fundamentals
The vocabulary and structure everything else assumes.
Compute
Virtual machines, containers and serverless runtimes.
Compute Engine
Virtual machines with full control over the operating system: machine types, images, disks, instance groups and autoscaling.
Compute
Cloud Run
Run a container without managing servers: request-driven scaling, scale to zero, and per-request billing.
Compute
Google Kubernetes Engine (GKE)
Managed Kubernetes: pods, deployments, services, autoscaling and the operational model that comes with a cluster.
Compute
Storage
Object, block and file storage, and choosing between them.
Cloud Storage
Object storage for files of any size: buckets, storage classes, lifecycle rules and access control.
Storage
Filestore and Shared File Storage
Managed network file storage for applications that need a real file system shared across many machines.
Storage
Databases
Relational, document and wide-column stores.
Cloud SQL
Managed relational databases — MySQL, PostgreSQL and SQL Server — with automated backups, replication and failover.
Databases
Spanner
A relational database that scales horizontally across regions while keeping strong consistency and SQL semantics.
Databases
Firestore
A serverless document database with flexible schemas, real-time synchronisation and offline support for client applications.
Databases
Bigtable
A wide-column NoSQL database built for very high throughput and low-latency lookups over enormous datasets.
Databases
Networking
Private networks, load balancing, DNS and content delivery.
Virtual Private Cloud (VPC)
Your private network in the cloud: subnets, routes, firewall rules, peering and shared VPC.
Networking
Load Balancing
Distributing traffic across backends: the global versus regional and proxy versus pass-through decisions that drive every selection question.
Networking
Content Delivery and Edge Caching
Serving content from locations close to users: cache keys, invalidation, and what should and should not be cached.
Networking
Cloud DNS
Managed name resolution: public and private zones, record types, forwarding and split-horizon design.
Networking
Security
Identity, encryption, and detecting what goes wrong.
Identity and Access Management (IAM)
Who can do what to which resource: members, roles, policy inheritance, service accounts and least privilege.
Security
Encryption and Key Management
Default encryption, customer-managed keys, rotation and secret handling — and the operational consequences of each choice.
Security
Security Posture and Threat Detection
Finding misconfigurations, detecting threats, understanding audit logs and running a detection and response capability.
Security
Data & Analytics
Warehousing, messaging and pipeline processing.
BigQuery
A serverless analytics warehouse: partitioning, clustering, the cost model, and how to make queries fast and cheap.
Data & Analytics
Pub/Sub and Messaging
Asynchronous messaging that decouples systems: topics, subscriptions, delivery guarantees, ordering and dead-letter handling.
Data & Analytics
Dataflow and Pipeline Processing
Batch and stream processing in one model: windowing, watermarks, late data and the trade-offs that make streaming hard.
Data & Analytics
AI & Machine Learning
Model fundamentals, the ML platform and generative AI.
Machine Learning Fundamentals
The concepts every ML question assumes: training, evaluation metrics, overfitting, and knowing when not to use machine learning.
AI & Machine Learning
Vertex AI and the ML Platform
Training, serving, pipelines and monitoring in one place — the MLOps surface that production machine learning needs.
AI & Machine Learning
Generative AI
Foundation models, prompting, grounding, retrieval augmentation and evaluation — and choosing the right technique for a failure.
AI & Machine Learning
DevOps
Build automation, delivery pipelines and release strategy.
CI/CD and Build Automation
How source code becomes a running service safely: pipelines, artefacts, quality gates and progressive rollout strategies.
DevOps
Monitoring
Metrics, logs, traces and reliability practice.
Monitoring, Logging and Observability
Metrics, logs and traces; designing alerts people actually act on; and the SLO vocabulary that reliability work is built around.
Monitoring