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feat: basics cloud build run #303

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b3a728e
feat: GCP project proposal and billing service pages added
nabinelnino Aug 26, 2025
bf45782
feat: adding new section for GCP's cloud build and cloud run
nabinelnino Dec 17, 2025
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117 changes: 117 additions & 0 deletions docs/software_development/Remote_Development/Google_Cloud_Platform/cloud_build.md
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# Basics of Cloud Build and Cloud Run on Google Cloud Platform (GCP)

## Introduction

When building production ready applications, if you want a simple, reliable way to **build**, **deploy**, and **run** software/application without managing servers or complex infrastructure, Google Cloud Platform (GCP) provides managed services that help achieve this, even for users who are new to cloud computing or production setup.

Two key services that work especially well together are **Cloud Build** and **Cloud Run**. This document explains what they are, why they are useful, and how they fit into a typical application workflow.

---

## What Is Cloud Build?

**Cloud Build** is a fully managed GCP service that automatically builds your application from source code.

The simple workflow of Cloud Build is as follow:

* Takes your code (for example, from GitHub)
* Runs predefined build steps (such as installing dependencies, running tests, or building a Docker image)
* Produces build artifacts, such as container images

You do **not** need to provision or maintain build servers, GCP manages all the infrastructure behind the scenes.

### Common Use Cases

* Building Docker images from source code
* Running tests during code changes and pushes to github
* Automating builds when code is pushed to a repository

### Key Benefits of Cloud Build

* **Fully managed**: No servers or build machines to maintain
* **Scalable**: Builds run in parallel when needed
* **CI-friendly**: Easily integrates into continuous integration (CI) pipelines

---

## What Is Cloud Run?

**Cloud Run** is a serverless platform for running containerized applications (such as Docker images).

With Cloud Run, you deploy a container image, and GCP automatically:

* Provisions compute resources
* Scales your application up and down
* Handles networking and HTTPS

You only focus on your application code and how it responds to requests.

### Important Characteristics

* Runs any container that listens on an HTTP port
* Scales to zero when there is no traffic
* Automatically scales up when traffic increases

### Key Benefits of Cloud Run

* **No server management**: Infrastructure is fully abstracted
* **Cost-efficient**: Pay only when your service is handling requests
* **Fast deployment**: Deploy new versions in seconds
* **Language-agnostic**: Works with Python, Java, Go, Node.js, and more

---

## Why Use Cloud Build and Cloud Run Together?

Cloud Build and Cloud Run complement each other and form a simple **build-and-deploy pipeline**.

### Typical Workflow

1. A developer pushes code to a repository
2. Cloud Build automatically:

* Builds a container image
* Runs tests or validation steps
* Stores the image in a artifact registry
3. The built image is deployed to Cloud Run
4. Cloud Run serves the application to users

---

## Why Do We Need These Services?

Traditional application deployment often requires:

* Managing virtual machines
* Configuring servers and networking
* Manually scaling applications

Cloud Build and Cloud Run remove much of this operational burden.


This is especially valuable for:

* Small teams or individual developers
* Research and prototype projects
* Production systems that need reliability without heavy DevOps overhead

---

## Benefits for General Users

Even without deep cloud expertise, users benefit from:

* **Simplicity**: Clear separation between building code and running services
* **Reproducibility**: Same build process works locally and in the cloud
* **Scalability**: Applications automatically handle traffic changes
* **Security**: Built-in authentication, authorization, and isolation
* **Faster iteration**: Code changes can reach users quickly

---


## Conclusion

Cloud Build and Cloud Run are core GCP services designed to simplify modern application development and deployment. By automating builds and removing the need to manage servers, they help users focus on delivering functionality and value.

For teams new to GCP, these services provide a gentle entry point into cloud-native development while still being powerful enough for production-grade workloads.
118 changes: 118 additions & 0 deletions ...ware_development/Remote_Development/Google_Cloud_Platform/gcp_billing_report.md
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# Monthly Reports from GCP Project

## **1. Overview**

This document explains how to **generate monthly billing reports** from **Google Cloud Platform (GCP)**.
These reports help us track **cloud costs per service**, **resource usage trends**, and **budget planning** for the AIRCHECK project.

The final output will include:

- **Monthly cost summaries**

- **Per-service cost breakdown**

- **Visualizations** of spending trends

- Insights for the **monthly meeting**

----------

## **2. Prerequisites**

Before you start, make sure you have:

- **GCP account access** with **Billing Admin** or **Viewer** role

- Access to the **GCP Billing Account**

----------

### **Step 1 — Open the Billing Reports Page**

1. Go to **GCP Console**

2. Click the **menu (☰)** in the top-left corner.

3. Select **Billing** → **Reports**.


----------

### **Step 2 — Select Time Range**

- Choose **Current Month** to see **this month’s costs** so far.

- Choose **Previous Month** to compare against last month.

- You can also select **Custom Range** to analyze any specific period.


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### **Step 3 — Group Costs by Service and SKU**

In the **Billing Reports** page, there’s a **“Group By”** dropdown.
Select:

- **Service** → Shows costs per GCP product, e.g., Vertex AI, BigQuery, GCS, Dataproc, Cloud Run, etc.

- **SKU** → Shows costs at a **more detailed level**, like:

- “Cloud Storage (Standard Storage Regional, Long-Term Physical Storage)”

- “BigQuery Storage (per GB)”

- “Compute Engine (N2 Instance Core for region(s), E2 Instance Core for region(s) ”


You can also **combine** grouping:

- **Group by Service + SKU** → Gives both the **high-level service** and its **detailed cost components**.


----------

### **Step 4 — Understanding What You See**

When you group **by Service**:

- You’ll see **total costs per GCP service**

- Example:

- Vertex AI → **$200**

- BigQuery → **$75**

- GCS → **$40**


When you group **by SKU**:

- You’ll see **specific resource costs** within a service

- Example (Vertex AI):

- Training hours → **$150**

- Prediction requests → **$50**


When you **group by Service + SKU**:

- You’ll see a **hierarchical view**:

`Vertex AI → Training vCPUs → $150 → Prediction API Requests → $50 BigQuery → Active Storage → $40 → Query Processing → $35`


----------


### **Step 5 — Download the Report**

You can export the billing report directly:

- Click **Download CSV**

- Choose **Current Month** or **Previous Month**

131 changes: 131 additions & 0 deletions ...ftware_development/Remote_Development/Google_Cloud_Platform/project_proposal.md
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# **GCP Project Proposal**

----------

## **1. Introduction**

This proposal outlines the process of **creating a new GCP project**, selecting the **required services**, and **estimating costs**. The document will also provide **official GCP links** for each service to help project members explore details and pricing.

----------

## **2. Project Objectives**

- Set up a **scalable GCP environment** to support data processing, ML training, and web deployment.

- Select **appropriate GCP services** based on project needs.

- Estimate **costs** for each service using **GCP Pricing Calculator**.

- Optimize resource allocation to **stay within budget**.


----------

## **3. Getting Started with GCP Project**

To create a new project in GCP:

1. Go to **GCP Console**

2. Click **“Select a Project”** → **“New Project”**

3. Enter:

- **Project Name** → e.g., `aircheck`

- **Organization** → Your organization name (if any)

- **Billing Account** → Link a billing account

4. Enable required APIs (e.g., GCS bucket, BigQuery, Dataproc, etc.)

5. Set up **IAM roles** for team members.


**Please refer this for detail information:** [GCP Create Project Documentation](https://cloud.google.com/resource-manager/docs/creating-managing-projects)

----------


## **4. Estimating GCP Costs**

GCP provides a **Pricing Calculator** to estimate project costs based on resource usage.

### **Steps to Estimate Costs**

1. Open **[GCP Pricing Calculator](https://cloud.google.com/products/calculator)**

2. Select the required services (e.g., Cloud Storage, Compute Engine, BigQuery, Dataproc)

3. Enter expected usage details:

- **Region**

- **Location**

- **Storage class**

- **Total amount of storage**


5. You can add additional services by clicking **“Add to Estimate”** and selecting the required services.
4. Download the cost estimate as a **PDF** for reporting.


**Example Cost Estimation:**


| Service | Resource Type | Monthly Estimate | Annual Estimate |
|---------|---------------|:----------------:|:---------------:|
| Vertex AI | 4 vCPUs, 16GB RAM, 50 hrs | **$200** | **$2,400** |
| Dataproc | 3 nodes, 4 vCPUs each | **$300** | **$3,600** |
| BigQuery | 1 TB storage, 50GB queries | **$50** | **$600** |
| GCS | 500GB storage | **$20** | **$240** |
| Cloud Run | 1 instance, 10k requests | **$15** | **$180** |
| **Total** | — | **$585** | **$7,020** |
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## **5. Service Selection Strategy**

Before selecting GCP services, follow these steps:

1. **Identify the Use Case** → e.g., data processing, ML model training, hosting dashboards.

2. **Map Required Services** → Choose GCP products based on needs.

3. **Check Pricing** → Use the GCP pricing calculator for initial estimates.

4. **Optimize Costs** → Use autoscaling, serverless options, and resource quotas to avoid overuse.

5. **Monitor Spending** → Set up GCP Budgets & Alerts to track costs.


----------

## **6. Useful References**

- [GCP Free Tier](https://cloud.google.com/free)

- [GCP Pricing Calculator](https://cloud.google.com/products/calculator?hl=en)

- [BigQuery Documentation](https://cloud.google.com/bigquery/docs)

- [GKE Documentation](https://cloud.google.com/kubernetes-engine/docs)

- [Cloud Storage Documentation](https://cloud.google.com/storage/docs)


----------

## **7. Next Steps**

- Finalize required GCP services based on the project architecture.

- Use GCP Pricing Calculator to finalize detailed cost estimates.

- Set up billing alerts and budgets to control costs.

- Document the infrastructure plan for future scaling.


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