>dr.kb< multiverse

grab a bowl ... 🌿🍯🔥💨

View on GitHub 
author: 51n5337 & #CLD
mission: CompTIA Cloud+ Certification
brief: karen wants demand forecast, dbs on GCP

…back

Q1

let's say in the stage of devsecops

a karen wants demand forecasting system that can produce periodic reports, and livestreaming dashboard. this is a service for their customers (e.g., subscription, or on-demand).
they have their dbs in GCP, and everything cloud-based info. they will set up whatever we want under the hood of GCP to launch this project.
our task has two phases:
- developing the demand forecasting system
- launch the project

tell me how would you handle from here... i'm your karen >wink!<

👁️⚡🌀
Okay Karen. Let’s get you that demand forecasting system.
We’ll build it smart, secure, and scalable—all inside GCP.

🧭 Phase 1: Development – The DevSecOps Vibe

Step 1: Architecture & Service Selection

We’ll use a microservices + event-driven design for flexibility and real-time vibes.

Core Components:

Why this mix?

Step 2: DevSecOps Pipeline Setup

# This is our vibe:
1. Code → Git repos (Cloud Source Repositories)
2. Security scanning → Cloud Build + Container Analysis
3. CI/CD → Cloud Build with automated tests
4. Infrastructure → Terraform (IaC)
5. Secrets → Secret Manager (no hardcoded creds)
6. Monitoring → Cloud Monitoring + Error Reporting

Security from Day 1:

🚀 Phase 2: Launch – Making It Production Ready

Step 1: Environment Strategy

We’ll use blue-green deployment for zero-downtime launches:

Step 2: Monitoring & Observability

Step 3: Disaster Recovery Plan

💬 What I’d Tell Karen:

“Karen, we’re building you a serverless-first, event-driven forecasting system that:

We’ll have a working prototype in 2-3 weeks, full production rollout in 6-8 weeks.

Your team will get: ✅ Secure customer portal
✅ Forecasting accuracy metrics
✅ Usage-based billing alignment
✅ 24/7 monitoring and alerting

Sound like the vibe you’re looking for?”

🎯 The GCP Service Map Would Look Like:

Data Sources → Pub/Sub → Dataflow → BigQuery
                             ↓
Cloud Functions → Vertex AI → Forecasts
                             ↓
                    Firestore → Looker Dashboard
                             ↓
                  Customer Portal (Cloud Run)

How does that architecture vibe with your vision, Karen? 😄📊

Ready to whiteboard this together?

Q2

can you discuss this situation connecting to the service models (IaaS, PaaS, etc)

👁️⚡🌀
Absolutely. Let’s map Karen’s demand forecasting system directly to the cloud service models.

🎯 Karen’s System Through the Service Model Lens

IaaS (Infrastructure as a Service)

What we’re NOT using much of: Raw VMs, manual OS management Why: Too much operational overhead for a data-focused app

Potential IaaS elements:

PaaS (Platform as a Service) - OUR PRIMARY VIBE

What we’re heavily using:

Why PaaS dominates: We focus on business logic (forecasting algorithms) while Google handles runtime, scaling, and maintenance.

SaaS (Software as a Service)

What we’re leveraging:

Why: Maximum efficiency - we’re consuming business intelligence and AI as services rather than building from scratch.

FaaS (Function as a Service)

Our secret weapon:

Vibe: “Code that sparks when data arrives, then vanishes” ⚡

🏗️ Shared Responsibility in Karen’s Project

Layer Our Responsibility Google’s Responsibility
Data Forecasting models, business logic, data quality Storage durability, availability
Application Our code, API design, security configs Runtime execution, auto-scaling
Platform Service configuration, IAM policies OS patching, infrastructure maintenance
Infrastructure (Minimal) Resource sizing, cost optimization Physical security, hardware, networking

🎭 The Business Vibe for Karen:

“Karen, we’re building you a PaaS-first architecture because:

It’s like renting a fully-staffed restaurant kitchen instead of building one from scratch - you just bring the recipes.”

🔄 Service Model Decision Flow for This Project:

Raw Data → PaaS (Pub/Sub) → PaaS (Dataflow) → PaaS (BigQuery) 
                                     ↓
                    FaaS (Cloud Functions) → SaaS (Vertex AI) → Forecasts
                                     ↓
                            SaaS (Looker) → Dashboard
                                     ↓
                          PaaS (Cloud Run) → Customer Portal

The pattern: Start with PaaS, use SaaS for specialized capabilities, avoid IaaS unless absolutely necessary.

Does this clarify how the cloud service models directly shape Karen’s solution? 🧩☁️

Q3

so Customer Portal (Cloud Run) is just a web interface?
like we can present the dashboard, the forecast, and the report here?

👁️⚡🌀
EXACTLY! You’re getting the full picture now.

🎯 Cloud Run as the “Front Door”

What Cloud Run Does:

What It Contains:

Customer Portal (Cloud Run)
├── Dashboard Tab → Embeds Looker dashboard
├── Forecasts Tab → Displays Vertex AI predictions  
├── Reports Tab → Shows generated PDF/Excel reports
└── Account Settings → Manages user preferences

🔄 How Data Flows Through the Portal:

User Opens Portal (Cloud Run)
         ↓
Cloud Run Checks Auth + Loads UI
         ↓
User Clicks "Dashboard" → Cloud Run embeds Looker
User Clicks "Forecasts" → Cloud Run calls Vertex API  
User Clicks "Reports" → Cloud Run fetches from Cloud Storage

🎭 The Service Model Magic:

💡 Key Insight:

Cloud Run doesn’t do the heavy lifting - it orchestrates specialized services that do.

This is cloud-native architecture: composing specialized services rather than building monolithic apps.

🚀 Real-World Analogy:

Think of Cloud Run as a restaurant host:

So yes - Cloud Run presents everything, but delegates the hard work to specialists. 🎯

This is exactly how modern cloud applications are built.