Twelve-factor application principles on Google Cloud - SkilBrill

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Google Cloud Development Twelve-factor application principles on Google Cloud

Google Cloud Development

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M1 — Cloud Application Development Lifecycle

How cloud runtimes differ from server deployment

Twelve-factor application principles on Google Cloud

Project structure, configuration and environments

Local development and emulator workflows

Container images and the distroless approach

Deployment strategies and rollback planning

M2 — Serverless Runtimes: Cloud Run and Functions

Cloud Run services, revisions and traffic splitting

Container contract: port, concurrency and health

Cloud Functions gen2 triggers and lifecycle

Cold starts, warm pools and concurrency settings

Authentication with Identity Platform and IAM

Choosing between Cloud Run and Functions

M3 — App Engine, Custom Runtimes and Migration

App Engine standard versus flexible

Custom runtimes and Dockerfile authoring

Scaling, instance class and automatic scaling

Migrating from App Engine to Cloud Run

Session and state handling

Blue-green and canary releases

M4 — Storage, Databases and Secrets in Application Code

Cloud Storage client access and signed URLs

Cloud SQL connector and connection pooling

Firestore for document workloads

Memorystore for caching and sessions

Secret Manager integration at startup

Retry, backoff and idempotent writes

M5 — Async Messaging and Integration

Pub/Sub publishing and subscription patterns

Ordering, deduplication and dead-letter queues

Workflows and orchestration for long-running tasks

Eventarc triggers from Google Cloud events

Third-party integration patterns

Backpressure and queue sizing

M6 — CI/CD with Cloud Build

Cloud Build triggers, substitutions and step ordering

Building, testing and pushing artefacts

Binary Authorization and deployment attestation

Infrastructure as code alongside application code

Environment promotion and approval gates

Rollback and release verification

M7 — Observability, Performance and Cost

Cloud Trace for latency analysis

Cloud Logging structured logging and correlation

Cloud Monitoring custom metrics and dashboards

Error Reporting and crash triage

Performance profiling and cold-start reduction

Cost per request and rightsizing concurrency

M8 — Capstone: Build and Operate an Application

Build a containerised API on Cloud Run

Add asynchronous processing with Pub/Sub

Persist data through Cloud SQL and Cloud Storage

Wire secrets and identity correctly

Ship through Cloud Build with canary release

Diagnose a injected latency regression

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