Developing in GCP Training

1. Best Practices

a.    Automation

i.    Code Repository
ii.    CI/CD Infrastructure
iii.    Test-driven development
iv.    Dependency Management
v.    Automated Deployment

b.    Loose Coupling

i.    Asynchronous Function Calls
ii.    Stateless Structures
iii.    Internal Load balancing
iv.    Message processing

c.    Error Handling

i.    Try/Catch on function calls
ii.    Use Error responses to adapt
iii.    Create circuit breakers

d.    Microservice Architecture

i.    Use Service Boundaries
ii.    Access services via API calls
iii.    Data and databases controlled by the service

e.    Security

i.    Categorize data
ii.    Implement controls based on data categories
iii.    Use Identity federation and user management tools

2. Google Cloud Environment

a.    Projects
b.    Identity
c.    Tools

i.    Console
ii.    Cloud Shell
iii.    Google Cloud APIs
iv.    Google APIs

d.    Application Environments

i.    Google Compute Engine
ii.    Google App Engine
iii.    Google Container Engine
iv.    Google Cloud Functions

3. Data

a.    Data models

i.    Cloudy Approach - nothing local
ii.    Unstructured and bulk - Google Cloud Storage
iii.    Structured Data - Data Storage Engines

b.    Data Storage Patterns

i.    Google Cloud Storage (Lab)
ii.    Data Storage Engines

4. Relational (SQL)

a.    Cloud SQL
b.    Spanner

5. Non-Relational

a.    Datastore (Lab)
b.    BigTable

6. Compute

a.    Google App Engine (covered in Datastore Lab)
b.    Google Cloud Functions (Lab)
c.    Google Container Engine (Lab)
d.    Google Compute Engine (Lab)

7. Implementing Best Practices

a.    Loose Coupling

i.    Pub/Sub
ii.    Internal Load Balancing

b.    Security