Why use GCP?

The mental model: how GCP is organized

Core GCP services developers should know

1) Compute (where your code runs)

2) Storage (files and blobs)

3) Databases (store application data)

4) Messaging + async workloads

5) IAM (permissions)

A practical starter architecture (small, modern, scalable)

Getting hands-on: a simple path to ship something

Common mistakes (and how to avoid them)

When GCP really shines

Conclusion Google Cloud Platform (GCP) is Google’s suite of cloud services for building, deploying, and operating applications at scale. If you’ve used AWS or Azure, GCP will feel familiar but it also has some “Google-native” strengths like BigQuery, first-class Kubernetes support, and strong data/ML tooling. This post is a developer-focused introduction: what GCP is great at, the core services you should know, and a simple path to start shipping real projects. GCP is a strong choice if you care about: It’s also easy to start small (serverless) and grow into more complex architecture only when you need it. Before jumping into services, it helps to know the hierarchy: For most developers, a Project is where everything begins. Tip: Use separate projects for dev, staging, and prod if you can especially once billing and IAM start to matter. If you’re building a typical web backend, Cloud SQL (Postgres) is a great starting point. A common “serverless backbone” looks like: IAM controls who can do what. IAM feels annoying until it saves you from accidental production changes or surprise bills. If you want a “default” GCP stack for a new product: This setup is production-friendly without forcing you to operate servers. Here’s a practical 30–60 minute plan: The key is momentum: deploy something tiny first, then iterate. GCP is especially strong for: You don’t need to learn all of GCP to be productive. Pick a small core:

Cloud Run + Cloud SQL + Cloud Storage + IAM + Logging, and you can ship real services quickly. Templates let you quickly answer FAQs or store snippets for re-use. as well , this person and/or - Managed compute that scales cleanly (especially serverless)- Containers + Kubernetes (Google created Kubernetes)- Data workloads (analytics, warehousing, streaming)- Machine learning tooling (MLOps, training, model hosting)- Global infrastructure + networking - Organization (company/domain; optional for personal projects)- Folders (optional grouping)- Projects (the main unit for billing, IAM, and resources)- Resources (VMs, buckets, databases, etc.) - Cloud Run: Run containers without managing servers. Great default for APIs, web apps, and workers.- App Engine: PaaS-style deploys (less common in brand-new systems, but still used).- Compute Engine: Virtual machines (full control).- GKE (Google Kubernetes Engine): Managed Kubernetes for orchestration at scale. - If you can containerize it → start with Cloud Run- If you need orchestration across many services → consider GKE- If you need low-level control → use Compute Engine - Cloud Storage: Object storage for uploads, artifacts, backups, media, static assets. - User uploads (images/video)- Build artifacts (zips, containers metadata)- Data exports/imports- Static website assets behind CDN - Cloud SQL: Managed PostgreSQL / MySQL / SQL Server.- Firestore: NoSQL document database (great for event-driven/mobile/web apps).- Spanner: Globally distributed relational DB (powerful, enterprise-grade). - Pub/Sub: Event streaming and messaging.- Cloud Tasks: Managed task queues (great for background jobs triggered by HTTP). - Cloud Run (API) → Pub/Sub (events) → Cloud Run (worker) - Prefer least privilege over broad admin access- Use service accounts for apps and automation- Avoid giving everyone Owner (it’s convenient and also risky) - Cloud Run for your API (containerized)- Cloud SQL (Postgres) for relational data- Cloud Storage for file uploads- Secret Manager for secrets (DB passwords, API keys)- Cloud Logging + Error Reporting for observability- Cloud Build for CI/CD (optional, but very handy) - Create a GCP project- Enable billing (many services require it)- Install the Google Cloud SDK (gcloud)- Deploy a small container to Cloud Run- Add Cloud SQL only after your service is deployed and reachable - Using Owner everywhere → create scoped roles/service accounts.- Mixing regions randomly → pick a region near your users and keep services aligned.- No budgets/alerts → set a budget alert on day one.- Starting with Kubernetes too early → Cloud Run often covers your needs for a long time. - Analytics-heavy products (BigQuery is a standout)- Event-driven systems (Pub/Sub + Cloud Run)- Kubernetes-based platforms (GKE + ecosystem)- ML workflows (Vertex AI + data tooling)