Google Cloud Services
Purpose
This section explains Google Cloud services from a practical cloud engineering perspective and focuses on the services that power the current learning path.
How To Read This Section
These pages are not intended to cover the full Google Cloud catalog. They are intended to make the current provider path easier to understand by answering a focused set of questions.
- What problem does this service solve in Google Cloud?
- How does it fit into a project or pattern?
- What identity, networking, and observability assumptions come with it?
- How does it compare to the surrounding runtime and data choices?
That makes the section most useful when you read it as part of a project, not as a disconnected glossary.
What This Service Set Is Designed To Teach
The Google Cloud path emphasizes the services most useful for learning project-based application delivery, eventing, secret handling, monitoring, analytics, and later AI extension.
Service Categories
Identity and Access
Storage and Data
Compute and Application Hosting
Integration and Scheduling
Observability and Operations
AI and Agentic Platforms
- AI and Agentic Workloads
- Vertex AI
- Vertex AI Agent Builder
- Agent Development Kit
- Model Garden
- Model Armor
Security and Secrets
How The Categories Connect
Typical Google Cloud workloads in this site start with IAM and service accounts, then connect Cloud Run or Cloud Functions to Cloud Storage, Pub/Sub, and Secret Manager. Cloud Monitoring gives you the operational view, while BigQuery extends the path into analytics. Vertex AI and related AI services build on top of that same application foundation rather than replacing it.
How This Fits Into Cloud Engineering
Cloud engineers need service knowledge that supports design, deployment, and operations. This section helps you explain how Google Cloud services work together in a system and what tradeoffs they introduce.