Google Cloud: Services & Architecture

Welcome

A practical guide to 51 core Google Cloud services — what they do, how they work, and when to use them.

≈12 hours to complete

About This Guide

Google Cloud Platform has over two hundred services. In practice, most engineers work with a few dozen and need solid familiarity with perhaps forty or fifty. This guide covers fifty-one — the ones that appear most often in production, form the backbone of real architectures, and cause the most trouble when chosen wrong.

Official documentation is comprehensive and current, but it does not tell you when not to use a service, how to navigate overlapping options, or what the practical failure modes look like. That is the gap this material fills.

Each service is covered with consistent structure: what it is, how it works, when to use it, when not to, common mistakes, and best practices. Where services overlap, the guide provides direct decision guidance rather than leaving that work to the reader.

Who This Is For

Software engineers, DevOps engineers, platform engineers, and cloud architects working with Google Cloud or preparing to. The material assumes you can read code, understand how web applications are built, and are comfortable with the basics of virtual machines and networking. No prior Google Cloud experience is required. Engineers with AWS or Azure background will find the comparable-services sections a useful translation layer.

Prerequisites

  • General familiarity with web applications — HTTP, servers, databases
  • Comfort with the command line and basic Linux concepts
  • A working understanding of what containers are, even without deep expertise
  • No prior Google Cloud experience required

The Google Cloud Landscape

Google Cloud Platform began with App Engine's public preview in 2008, after AWS had already established the market. That later start produced deliberate architectural choices: the global fiber network gives real cross-region latency advantages over public internet routing; the VPC model is global by default rather than regional; and BigQuery, Spanner, and Bigtable originated as internal Google infrastructure with no close equivalents elsewhere when they were designed.

At the same time, Google Cloud is not exotic. Its compute, storage, and database services follow the same patterns as every other major cloud. Engineers who know AWS or Azure will find the surface area familiar — the differences are naming, specific features, and a handful of genuinely distinctive services.

Architecture Philosophy

One theme runs through every chapter: the best architecture is the simplest one that meets the requirements. This runs against the instinct to choose sophisticated services because they feel safer or more impressive. Complexity is a real cost, and operational burden — patching, upgrades, incident response, capacity planning — is as real as compute spend even when it does not appear on invoices.

Start simple
Operational complexity is a cost. A service that requires less maintenance earns its place even if it is less powerful than the alternative.
Match tool to workload
Databases, compute, and storage services have specific performance envelopes. Using the wrong one does not just cost more — it creates problems that are hard to undo.
Anticipate sensibly
Design for ten times current load — that is prudent. Designing for ten thousand times is premature optimization that costs real money today.
Operational burden is cost
Every self-managed component carries maintenance cost. Managed services delegate that work. Treat the trade-off explicitly, not as a default.

Chapter Map

Chapter 1
Compute
Virtual machines, Kubernetes, serverless containers, functions, and App Engine — five services across the full control spectrum.
Chapter 2
Storage
Object storage, block storage, and shared file systems — three models that are not interchangeable.
Chapter 3
Databases
Seven databases spanning relational, document, wide-column, in-memory, analytical, and globally distributed models.
Chapter 4
Networking
VPC, firewall, load balancers, DNS, CDN, NAT, and Interconnect — seven services for private connectivity, traffic management, and edge delivery.
Chapter 5
Messaging & Async
Pub/Sub, Cloud Tasks, Cloud Scheduler, Workflows, and Eventarc — five services for the decoupling work that makes distributed systems tractable.
Chapter 6
Data & Analytics
Dataflow, Dataproc, Composer, Looker, and Datastream — five services for moving operational data into the analytical world.
Chapter 7
AI & Machine Learning
Vertex AI, the Gemini API, and the pre-trained AI APIs — three services covering the AI surface of Google Cloud.
Chapter 8
Security & Identity
Cloud Identity, IAM, Secret Manager, KMS, IAP, Cloud Armor, Security Command Center, and PAM — eight services forming the security posture of a Google Cloud organization.
Chapter 9
Developer Tools & CI/CD
Artifact Registry, Cloud Build, and Cloud Deploy — three services forming the native CI/CD pipeline on Google Cloud.
Chapter 10
Observability
Cloud Logging, Cloud Monitoring, and Cloud Trace — three services for the three pillars of observability.
Chapter 11
API Management
Apigee/API Gateway and Firebase — two umbrella entries that hide significant complexity.
Chapter 12
Architecting with Google Cloud
Decision frameworks, design patterns, SRE, security architecture, cost, and case studies — six topics on composing the services from chapters 1 through 11 into real production systems.

Disclaimer

This course is an independent educational project created and maintained by Sergey Okinchuk. It is provided for learning and reference purposes only.

No affiliation with Google. This course is not affiliated with, sponsored by, endorsed by, commissioned by, or in any way officially connected to Google LLC, Google Cloud, Alphabet Inc., or any of their subsidiaries or affiliates. All opinions, interpretations, and recommendations expressed are those of the author.

Trademarks. "Google", "Google Cloud", "Google Cloud Platform", "GCP", and the Google Cloud logo are trademarks of Google LLC. All other product names, service names, and logos referenced throughout this course are trademarks or registered trademarks of their respective owners. Use of these names and marks is for identification and educational purposes only and does not imply any endorsement, sponsorship, or partnership.

Logo and icon attribution. The Google Cloud cloud-mark and the per-service product icons used throughout this course are reproduced from official Google sources — the Google Cloud Icon Library and the Google Brand Resource Center — under nominative fair use for educational and editorial purposes. No modifications have been made to the official marks beyond resizing and standard rendering.

Accuracy and currency. Cloud platforms evolve rapidly. Features, pricing, regional availability, quotas, default behavior, and best practices described in this course reflect the author's understanding at the time of writing and may not be current. Always consult the official Google Cloud documentation as the authoritative source before making architectural, operational, or financial decisions.

No warranty. This material is provided "as is" without warranty of any kind, express or implied, including but not limited to warranties of accuracy, completeness, merchantability, or fitness for a particular purpose. The author makes no representations regarding the suitability of this material for any specific use, and accepts no liability for any loss, damage, cost, or claim arising from reliance on the content in production systems or otherwise.

Third-party content. Where this course references documentation, blog posts, conference talks, or other external sources, attribution is provided to the original authors. All quoted material remains the property of its respective rights-holders.