deploying-on-gcp

# GCP Patterns

Build applications and infrastructure using Google Cloud Platform services with appropriate service selection, architecture patterns, and best practices.

## Purpose

This skill provides decision frameworks and implementation patterns for Google Cloud Platform (GCP) services across compute, storage, databases, data analytics, machine learning, networking, and security. It guides service selection based on workload requirements and demonstrates production-ready patterns using Terraform, Python SDKs, and gcloud CLI.

## When to Use

Use this skill when:

- Selecting GCP compute services (Cloud Run, GKE, Cloud Functions, Compute Engine, App Engine)
- Choosing storage or database services (Cloud Storage, Cloud SQL, Spanner, Firestore, Bigtable, BigQuery)
- Designing data analytics pipelines (BigQuery, Pub/Sub, Dataflow, Dataproc, Composer)
- Implementing ML workflows (Vertex AI, AutoML, pre-trained APIs)
- Architecting network infrastructure (VPC, Load Balancing, CDN, Cloud Armor)
- Setting up IAM, security, and cost optimization
- Migrating from AWS or Azure to GCP
- Building multi-cloud or GCP-first architectures

## Core Concepts

### GCP Service Categories

**Compute Options:**
- **Cloud Run:** Serverless containers for stateless HTTP services (auto-scale to zero)
- **GKE (Google Kubernetes Engine):** Managed Kubernetes for complex orchestration
- **Cloud Functions:** Event-driven functions for simple processing
- **Compute Engine:** Virtual machines for full OS control
- **App Engine:** Platform-as-a-Service for web applications

**Storage & Databases:**
- **Cloud Storage:** Object storage with Standard/Nearline/Coldline/Archive tiers
- **Cloud SQL:** Managed PostgreSQL/MySQL/SQL Server (up to 96TB)
- **Cloud Spanner:** Global distributed SQL with 99.999% SLA
- **Firestore:** NoSQL document database with real-time sync
- **Bigtable:** Wide-column NoSQL for time-series and IoT (petabyte scale)
- **AlloyDB:** PostgreSQL-compatible with 4x performance