From Beginner to Expert
Table of Contents
- Introduction to Google Cloud Platform
- GCP Core Concepts and Architecture
- Compute Services
- Storage and Database Services
- Networking Services
- Security and Identity
- Data Analytics and Big Data
- Machine Learning and AI
- DevOps and CI/CD
- Serverless and Event-Driven Architecture
- Containerization and Orchestration
- Monitoring and Operations
- Cost Management and Optimization
- Hybrid and Multi-Cloud
- Advanced Architectures and Best Practices
1. Introduction to Google Cloud Platform
What is Google Cloud Platform?
Google Cloud Platform (GCP) is a suite of cloud computing services that provides infrastructure, platform, and software services for building, deploying, and scaling applications.
graph TB
A[Google Cloud Platform] --> B[Infrastructure as a Service - IaaS]
A --> C[Platform as a Service - PaaS]
A --> D[Software as a Service - SaaS]
A --> E[Function as a Service - FaaS]
B --> F[Compute Engine]
B --> G[Virtual Private Cloud]
B --> H[Cloud Storage]
C --> I[App Engine]
C --> J[Cloud Functions]
C --> K[Cloud Run]
D --> L[Google Workspace]
D --> M[BigQuery]
D --> N[Cloud AI APIs]
E --> O[Cloud Functions]
E --> P[Cloud Run]GCP Global Infrastructure
graph TB
A[GCP Global Infrastructure] --> B[Regions]
A --> C[Zones]
A --> D[Points of Presence - PoPs]
A --> E[Network Edge Locations]
B --> F[Independent Geographic Areas]
B --> G[Multiple Zones per Region]
B --> H[Data Sovereignty]
C --> I[Isolated Fault Domains]
C --> J[Low-latency Connectivity]
C --> K[High Availability]
D --> L[Global Network]
D --> M[CDN Edge Caching]
D --> N[Premium Network Tier]GCP Service Categories
mindmap
root((GCP Services))
Compute
Compute Engine
App Engine
Cloud Functions
Cloud Run
GKE
Storage
Cloud Storage
Persistent Disk
Filestore
Archive Storage
Database
Cloud SQL
Firestore
BigQuery
Cloud Spanner
Bigtable
Networking
VPC
Cloud Load Balancing
Cloud CDN
Cloud DNS
AI/ML
Vertex AI
AutoML
Vision API
Natural Language API
Analytics
BigQuery
Dataflow
Pub/Sub
Cloud ComposerGCP vs Other Cloud Providers
graph LR
A[Cloud Providers] --> B[Google Cloud Platform]
A --> C[Amazon Web Services]
A --> D[Microsoft Azure]
B --> E[Strengths]
E --> F[Data Analytics]
E --> G[Machine Learning]
E --> H[Kubernetes]
E --> I[Network Performance]
B --> J[Key Differentiators]
J --> K[BigQuery]
J --> L[TensorFlow Integration]
J --> M[Live Migration]
J --> N[Sustained Use Discounts]2. GCP Core Concepts and Architecture
GCP Resource Hierarchy
graph TB
A[Organization] --> B[Folders]
B --> C[Projects]
C --> D[Resources]
A --> E[Company/Domain Level]
B --> F[Departments/Teams]
C --> G[Applications/Environments]
D --> H[VMs, Storage, etc.]
I[IAM Inheritance] --> J[Organization Policies]
I --> K[Folder Policies]
I --> L[Project Policies]
I --> M[Resource Policies]GCP Identity and Access Management (IAM)
graph LR
A[IAM Components] --> B[Members]
A --> C[Roles]
A --> D[Policies]
A --> E[Resources]
B --> F[Google Accounts]
B --> G[Service Accounts]
B --> H[Google Groups]
B --> I[Domains]
C --> J[Primitive Roles]
C --> K[Predefined Roles]
C --> L[Custom Roles]
J --> M[Owner]
J --> N[Editor]
J --> O[Viewer]GCP Billing and Resource Management
graph TB
A[Billing Account] --> B[Payment Methods]
A --> C[Billing Profiles]
A --> D[Projects]
D --> E[Resources]
E --> F[Usage Tracking]
F --> G[Cost Allocation]
H[Cost Management] --> I[Budgets]
H --> J[Alerts]
H --> K[Billing Export]
H --> L[Cost Optimization]
I --> M[Threshold Alerts]
J --> N[Email Notifications]
K --> O[BigQuery Export]
L --> P[Rightsizing Recommendations]Service Accounts and Authentication
sequenceDiagram
participant App
participant SA as Service Account
participant IAM
participant GCP as GCP Service
App->>SA: Request Access Token
SA->>IAM: Authenticate with Key
IAM->>SA: Return Access Token
SA->>App: Provide Token
App->>GCP: API Call with Token
GCP->>IAM: Validate Token
IAM->>GCP: Token Valid
GCP->>App: Service Response3. Compute Services
Compute Engine
graph TB
A[Compute Engine] --> B[Virtual Machine Instances]
A --> C[Machine Types]
A --> D[Images]
A --> E[Instance Groups]
A --> F[Load Balancing]
C --> G[Standard]
C --> H[High-Memory]
C --> I[High-CPU]
C --> J[Custom]
C --> K[Preemptible]
D --> L[Public Images]
D --> M[Custom Images]
D --> N[Community Images]
E --> O[Managed Instance Groups]
E --> P[Unmanaged Instance Groups]VM Instance Lifecycle
stateDiagram-v2
[*] --> PROVISIONING
PROVISIONING --> STAGING
STAGING --> RUNNING
RUNNING --> STOPPING
STOPPING --> STOPPED
STOPPED --> RUNNING
RUNNING --> TERMINATED
STOPPED --> TERMINATED
TERMINATED --> [*]
RUNNING --> SUSPENDING
SUSPENDING --> SUSPENDED
SUSPENDED --> RUNNINGApp Engine
graph TB
A[App Engine] --> B[Standard Environment]
A --> C[Flexible Environment]
B --> D[Sandboxed Runtime]
B --> E[Automatic Scaling]
B --> F[Free Tier]
B --> G[Language Runtimes]
C --> H[Docker Containers]
C --> I[Custom Runtimes]
C --> J[SSH Access]
C --> K[Background Processes]
G --> L[Python]
G --> M[Java]
G --> N[Node.js]
G --> O[Go]
G --> P[PHP]Cloud Functions
graph LR
A[Cloud Functions] --> B[HTTP Triggers]
A --> C[Cloud Storage Triggers]
A --> D[Pub/Sub Triggers]
A --> E[Firestore Triggers]
A --> F[Firebase Triggers]
G[Function Execution] --> H[Event Source]
H --> I[Function Code]
I --> J[Runtime Environment]
J --> K[Response/Action]
L[Supported Runtimes] --> M[Node.js]
L --> N[Python]
L --> O[Go]
L --> P[Java]
L --> Q[.NET]Cloud Run
graph TB
A[Cloud Run] --> B[Serverless Containers]
A --> C[Auto-scaling]
A --> D[Pay-per-use]
A --> E[Portable]
B --> F[Any Language/Runtime]
B --> G[Container Images]
B --> H[HTTP/gRPC Services]
C --> I[0 to N instances]
C --> J[Request-driven]
C --> K[Concurrency Control]
E --> L[Kubernetes Compatibility]
E --> M[Anthos Integration]4. Storage and Database Services
Cloud Storage
graph TB
A[Cloud Storage] --> B[Storage Classes]
A --> C[Buckets]
A --> D[Objects]
A --> E[Access Control]
B --> F[Standard]
B --> G[Nearline]
B --> H[Coldline]
B --> I[Archive]
F --> J[Frequent AccessHigh Performance]
G --> K[Monthly AccessLower Cost]
H --> L[Quarterly AccessVery Low Cost]
I --> M[Annual AccessLowest Cost]
E --> N[IAM]
E --> O[ACLs]
E --> P[Signed URLs]
E --> Q[Bucket Policies]Cloud Storage Features
graph LR
A[Cloud Storage Features] --> B[Lifecycle Management]
A --> C[Versioning]
A --> D[Cross-Region Replication]
A --> E[Transfer Service]
A --> F[CDN Integration]
B --> G[Automatic Transitions]
B --> H[Deletion Rules]
C --> I[Object Versions]
C --> J[Version Control]
D --> K[Geographic Redundancy]
D --> L[Disaster Recovery]
E --> M[Data Import/Export]
E --> N[Scheduled Transfers]Persistent Disk
graph TB
A[Persistent Disk] --> B[Standard Persistent Disk]
A --> C[SSD Persistent Disk]
A --> D[Local SSD]
A --> E[Regional Persistent Disk]
B --> F[Lower CostStandard Performance]
C --> G[Higher CostHigh Performance]
D --> H[Highest PerformanceTemporary Storage]
E --> I[Regional ReplicationHigh Availability]
J[Features] --> K[Snapshots]
J --> L[Encryption]
J --> M[Resizing]
J --> N[Multi-attach]Cloud SQL
graph TB
A[Cloud SQL] --> B[Supported Databases]
A --> C[Features]
A --> D[High Availability]
A --> E[Security]
B --> F[MySQL]
B --> G[PostgreSQL]
B --> H[SQL Server]
C --> I[Automated Backups]
C --> J[Point-in-time Recovery]
C --> K[Read Replicas]
C --> L[Automatic Scaling]
D --> M[Regional Persistence]
D --> N[Failover Replicas]
E --> O[Encryption at Rest]
E --> P[Private IP]
E --> Q[IAM Integration]Firestore (NoSQL)
graph LR
A[Firestore] --> B[Document Model]
A --> C[Real-time Updates]
A --> D[Multi-region]
A --> E[ACID Transactions]
B --> F[Collections]
F --> G[Documents]
G --> H[Subcollections]
C --> I[Live Synchronization]
C --> J[Offline Support]
D --> K[Global Distribution]
D --> L[Strong Consistency]
E --> M[Atomicity]
E --> N[Consistency]
E --> O[Isolation]
E --> P[Durability]BigQuery
graph TB
A[BigQuery] --> B[Serverless Data Warehouse]
A --> C[SQL Analytics]
A --> D[Machine Learning]
A --> E[Data Integration]
B --> F[Petabyte Scale]
B --> G[No Infrastructure Management]
B --> H[Auto-scaling]
C --> I[Standard SQL]
C --> J[Columnar Storage]
C --> K[Parallel Processing]
D --> L[BigQuery ML]
D --> M[Built-in ML Functions]
E --> N[Data Transfer Service]
E --> O[Streaming Inserts]
E --> P[Federated Queries]Cloud Spanner
graph TB
A[Cloud Spanner] --> B[Global Consistency]
A --> C[Horizontal Scaling]
A --> D[SQL Interface]
A --> E[ACID Transactions]
B --> F[External Consistency]
B --> G[Global Transactions]
B --> H[TrueTime API]
C --> I[Automatic Sharding]
C --> J[Regional/Multi-regional]
C --> K[Hotspotting Prevention]
D --> L[Standard SQL]
D --> M[Schema Migrations]
E --> N[Strong Consistency]
E --> O[Serializable Isolation]5. Networking Services
Virtual Private Cloud (VPC)
graph TB
A[VPC Network] --> B[Subnets]
A --> C[Firewall Rules]
A --> D[Routes]
A --> E[VPC Peering]
B --> F[Regional Subnets]
B --> G[IP Address Ranges]
B --> H[Private Google Access]
C --> I[Ingress Rules]
C --> J[Egress Rules]
C --> K[Target Tags]
C --> L[Service Accounts]
D --> M[System Routes]
D --> N[Custom Routes]
D --> O[Default Route]
E --> P[Cross-VPC Communication]
E --> Q[Transitive Peering]VPC Network Architecture
graph LR
A[VPC Network] --> B[Subnet Aus-central1]
A --> C[Subnet Bus-east1]
A --> D[Subnet Ceurope-west1]
B --> E[VM Instance 1]
B --> F[VM Instance 2]
C --> G[VM Instance 3]
D --> H[VM Instance 4]
I[Firewall Rules] --> J[Allow HTTP]
I --> K[Allow SSH]
I --> L[Allow Internal]
M[Cloud Router] --> N[Cloud VPN]
M --> O[Cloud Interconnect]Load Balancing
graph TB
A[Load Balancing] --> B[Global Load Balancers]
A --> C[Regional Load Balancers]
B --> D[HTTP/HTTPS]
B --> E[SSL Proxy]
B --> F[TCP Proxy]
C --> G[Network Load Balancer]
C --> H[Internal Load Balancer]
I[Load Balancer Components] --> J[Frontend]
I --> K[Backend Services]
I --> L[Health Checks]
I --> M[URL Maps]
J --> N[IP Address]
J --> O[Port]
J --> P[Protocol]Cloud CDN
graph TB
A[Cloud CDN] --> B[Edge Locations]
A --> C[Origin Servers]
A --> D[Caching Policies]
A --> E[Security Features]
B --> F[Global Distribution]
B --> G[Low Latency]
B --> H[High Bandwidth]
C --> I[Compute Engine]
C --> J[Cloud Storage]
C --> K[External Origins]
D --> L[Cache Keys]
D --> M[TTL Settings]
D --> N[Cache Invalidation]
E --> O[SSL/TLS]
E --> P[Cloud Armor]
E --> Q[Origin Authentication]Cloud Interconnect and VPN
graph TB
A[Hybrid Connectivity] --> B[Cloud VPN]
A --> C[Cloud Interconnect]
B --> D[Site-to-Site VPN]
B --> E[IPSec Tunnels]
B --> F[Dynamic Routing]
C --> G[Dedicated Interconnect]
C --> H[Partner Interconnect]
G --> I[Physical Connection]
G --> J[Higher Bandwidth]
G --> K[Lower Latency]
H --> L[Service Provider]
H --> M[Flexible Capacity]
H --> N[Faster Provisioning]
O[On-Premises] --> P[Cloud Router]
P --> Q[GCP VPC]6. Security and Identity
Identity and Access Management (IAM)
graph TB
A[IAM Policy] --> B[Members]
A --> C[Roles]
A --> D[Conditions]
B --> E[Users]
B --> F[Service Accounts]
B --> G[Groups]
B --> H[Domains]
C --> I[Primitive Roles]
C --> J[Predefined Roles]
C --> K[Custom Roles]
I --> L[Owner]
I --> M[Editor]
I --> N[Viewer]
J --> O[Compute Admin]
J --> P[Storage Admin]
J --> Q[BigQuery User]
D --> R[Time-based]
D --> S[IP-based]
D --> T[Device-based]Security Best Practices
mindmap
root((Security Best Practices))
Identity Management
Service Accounts
Least Privilege
MFA Implementation
Regular Audits
Network Security
VPC Firewall Rules
Private Google Access
Cloud NAT
VPN/Interconnect
Data Protection
Encryption at Rest
Encryption in Transit
Key Management
Data Loss Prevention
Compliance
Security Command Center
Cloud Audit Logs
Binary Authorization
Policy IntelligenceCloud KMS (Key Management Service)
graph LR
A[Cloud KMS] --> B[Key Rings]
B --> C[Crypto Keys]
C --> D[Key Versions]
E[Key Operations] --> F[Encrypt]
E --> G[Decrypt]
E --> H[Sign]
E --> I[Verify]
J[Key Types] --> K[Symmetric]
J --> L[Asymmetric]
K --> M[AES-256]
L --> N[RSA]
L --> O[Elliptic Curve]
P[Integration] --> Q[Cloud Storage]
P --> R[Compute Engine]
P --> S[BigQuery]
P --> T[Application Layer]Cloud Identity and Access Management
sequenceDiagram
participant User
participant IAM
participant Resource
participant Audit
User->>IAM: Authentication Request
IAM->>IAM: Verify Identity
IAM->>User: Authentication Success
User->>Resource: Access Request
Resource->>IAM: Authorization Check
IAM->>IAM: Evaluate Policies
IAM->>Resource: Authorization Result
Resource->>User: Access Granted/Denied
Resource->>Audit: Log Access AttemptSecurity Command Center
graph TB
A[Security Command Center] --> B[Asset Discovery]
A --> C[Vulnerability Assessment]
A --> D[Threat Detection]
A --> E[Compliance Monitoring]
B --> F[Resource Inventory]
B --> G[Configuration Changes]
B --> H[Asset Classification]
C --> I[Web Security Scanner]
C --> J[Container Analysis]
C --> K[Binary Authorization]
D --> L[Anomaly Detection]
D --> M[Threat Intelligence]
D --> N[Event Timeline]
E --> O[Policy Violations]
E --> P[Compliance Reports]
E --> Q[Remediation Guidance]7. Data Analytics and Big Data
GCP Analytics Services
mindmap
root((Analytics Services))
Data Ingestion
Pub/Sub
Dataflow
Transfer Service
Dataprep
Data Storage
BigQuery
Cloud Storage
Bigtable
Cloud SQL
Data Processing
Dataflow
Dataproc
Cloud Composer
Dataprep
Data Visualization
Data Studio
Looker
Jupyter Notebooks
Machine Learning
Vertex AI
AutoML
BigQuery MLPub/Sub Messaging
graph LR
A[Publishers] --> B[Pub/Sub Topics]
B --> C[Subscriptions]
C --> D[Subscribers]
A --> E[Applications]
A --> F[IoT Devices]
A --> G[Services]
B --> H[Message Ordering]
B --> I[Message Filtering]
B --> J[Dead Letter Topics]
C --> K[Push Subscriptions]
C --> L[Pull Subscriptions]
D --> M[Cloud Functions]
D --> N[App Engine]
D --> O[Compute Engine]
D --> P[External Systems]Dataflow (Apache Beam)
graph TB
A[Dataflow] --> B[Batch Processing]
A --> C[Stream Processing]
A --> D[Unified Programming Model]
B --> E[ETL Jobs]
B --> F[Data Transformation]
B --> G[Historical Analysis]
C --> H[Real-time Analytics]
C --> I[Event Processing]
C --> J[Windowing]
D --> K[Apache Beam SDK]
D --> L[Templates]
D --> M[SQL Interface]
N[Data Sources] --> O[Pub/Sub]
N --> P[Cloud Storage]
N --> Q[BigQuery]
N --> R[Kafka]
S[Data Sinks] --> T[BigQuery]
S --> U[Cloud Storage]
S --> V[Bigtable]
S --> W[Pub/Sub]BigQuery Architecture
graph TB
A[BigQuery] --> B[Dremel Engine]
A --> C[Colossus Storage]
A --> D[Jupiter Network]
A --> E[Borg Orchestration]
B --> F[SQL Query Engine]
B --> G[Columnar Storage]
B --> H[Tree Architecture]
C --> I[Distributed File System]
C --> J[Data Replication]
C --> K[Automatic Sharding]
D --> L[High-speed Network]
D --> M[Petabit Bandwidth]
E --> N[Resource Management]
E --> O[Auto-scaling]
P[Query Execution] --> Q[Root Server]
Q --> R[Intermediate Servers]
R --> S[Leaf Servers]
S --> T[Storage Layer]Data Lake Architecture
graph TB
A[Data Sources] --> B[Ingestion Layer]
B --> C[Storage Layer]
C --> D[Processing Layer]
D --> E[Analytics Layer]
A --> F[Databases]
A --> G[Applications]
A --> H[IoT Streams]
A --> I[File Systems]
B --> J[Pub/Sub]
B --> K[Transfer Service]
B --> L[Dataflow]
B --> M[Third-party Tools]
C --> N[Cloud Storage]
C --> O[Raw Data Zone]
C --> P[Processed Data Zone]
C --> Q[Curated Data Zone]
D --> R[Dataflow]
D --> S[Dataproc]
D --> T[Cloud Functions]
D --> U[Dataprep]
E --> V[BigQuery]
E --> W[Data Studio]
E --> X[Vertex AI]
E --> Y[Looker]Cloud Composer (Apache Airflow)
graph TB
A[Cloud Composer] --> B[Workflow Orchestration]
A --> C[Apache Airflow]
A --> D[Managed Service]
B --> E[DAGs - Directed Acyclic Graphs]
B --> F[Task Dependencies]
B --> G[Scheduling]
B --> H[Monitoring]
C --> I[Python-based]
C --> J[Extensible]
C --> K[Rich UI]
C --> L[Integration]
D --> M[Auto-scaling]
D --> N[High Availability]
D --> O[Security]
D --> P[Monitoring]
Q[Workflow Example] --> R[Extract Data]
R --> S[Transform Data]
S --> T[Load to BigQuery]
T --> U[Generate Reports]8. Machine Learning and AI
GCP AI/ML Services
graph TB
A[GCP AI/ML Stack] --> B[Pre-trained APIs]
A --> C[AutoML]
A --> D[Vertex AI]
A --> E[TensorFlow]
B --> F[Vision API]
B --> G[Natural Language API]
B --> H[Translation API]
B --> I[Speech-to-Text API]
B --> J[Text-to-Speech API]
C --> K[AutoML Vision]
C --> L[AutoML Natural Language]
C --> M[AutoML Tables]
C --> N[AutoML Translation]
D --> O[Unified ML Platform]
D --> P[Model Training]
D --> Q[Model Deployment]
D --> R[Feature Store]
E --> S[Open Source Framework]
E --> T[Custom Models]
E --> U[Research]Vertex AI Platform
graph LR
A[Vertex AI] --> B[Data Preparation]
A --> C[Model Training]
A --> D[Model Evaluation]
A --> E[Model Deployment]
A --> F[Model Monitoring]
B --> G[Vertex AI Workbench]
B --> H[Data Labeling]
B --> I[Feature Store]
C --> J[Custom Training]
C --> K[AutoML Training]
C --> L[Hyperparameter Tuning]
D --> M[Model Evaluation Metrics]
D --> N[Fairness Indicators]
E --> O[Batch Prediction]
E --> P[Online Prediction]
E --> Q[Edge Deployment]
F --> R[Model Drift Detection]
F --> S[Performance Monitoring]ML Workflow on GCP
sequenceDiagram
participant DS as Data Scientist
participant VW as Vertex Workbench
participant VA as Vertex AI
participant BQ as BigQuery
participant CS as Cloud Storage
DS->>VW: Create Notebook
VW->>BQ: Query Training Data
BQ-->>VW: Return Dataset
VW->>CS: Store Preprocessed Data
DS->>VA: Submit Training Job
VA->>CS: Access Training Data
VA->>VA: Train Model
VA->>CS: Save Model Artifacts
DS->>VA: Deploy Model
VA->>VA: Create Endpoint
DS->>VA: Make Predictions
VA-->>DS: Return PredictionsAutoML Workflow
graph TB
A[AutoML Workflow] --> B[Data Upload]
B --> C[Data Preprocessing]
C --> D[Feature Engineering]
D --> E[Model Architecture Search]
E --> F[Hyperparameter Optimization]
F --> G[Model Training]
G --> H[Model Evaluation]
H --> I[Model Deployment]
B --> J[CSV, Images, Text, Video]
C --> K[Data Validation]
C --> L[Missing Value Handling]
D --> M[Automated Feature Selection]
E --> N[Neural Architecture Search]
F --> O[Automated Tuning]
G --> P[Distributed Training]
H --> Q[Performance Metrics]
I --> R[REST API Endpoint]BigQuery ML
graph LR
A[BigQuery ML] --> B[Data Preparation]
A --> C[Model Creation]
A --> D[Model Evaluation]
A --> E[Model Prediction]
B --> F[SQL Queries]
B --> G[Feature Engineering]
B --> H[Data Splitting]
C --> I[CREATE MODEL]
C --> J[Linear Regression]
C --> K[Logistic Regression]
C --> L[K-means Clustering]
C --> M[Deep Neural Networks]
C --> N[Time Series Forecasting]
D --> O[ML.EVALUATE]
D --> P[Performance Metrics]
E --> Q[ML.PREDICT]
E --> R[Batch Predictions]
E --> S[Real-time Scoring]9. DevOps and CI/CD
Cloud Build CI/CD
graph TB
A[Cloud Build] --> B[Build Triggers]
A --> C[Build Configuration]
A --> D[Build Steps]
A --> E[Artifacts]
B --> F[GitHub Integration]
B --> G[Cloud Source Repositories]
B --> H[Bitbucket Integration]
B --> I[Manual Triggers]
C --> J[cloudbuild.yaml]
C --> K[Dockerfile]
C --> L[Build Templates]
D --> M[Build Image]
D --> N[Run Tests]
D --> O[Deploy Application]
D --> P[Custom Steps]
E --> Q[Container Registry]
E --> R[Artifact Registry]
E --> S[Cloud Storage]CI/CD Pipeline Architecture
sequenceDiagram
participant Dev as Developer
participant SCM as Source Control
participant CB as Cloud Build
participant CR as Container Registry
participant GKE as GKE Cluster
participant Mon as Monitoring
Dev->>SCM: Git Push
SCM->>CB: Trigger Build
CB->>CB: Run Tests
CB->>CB: Build Container
CB->>CR: Push Image
CB->>GKE: Deploy Application
GKE->>Mon: Application Metrics
Mon->>Dev: Deployment StatusInfrastructure as Code with Deployment Manager
graph TB
A[Deployment Manager] --> B[Templates]
A --> C[Configurations]
A --> D[Deployments]
A --> E[Resources]
B --> F[Jinja2 Templates]
B --> G[Python Templates]
B --> H[Reusable Components]
C --> I[YAML Configuration]
C --> J[Parameters]
C --> K[Environment Variables]
D --> L[Create Deployment]
D --> M[Update Deployment]
D --> N[Delete Deployment]
D --> O[Preview Changes]
E --> P[Compute Instances]
E --> Q[Load Balancers]
E --> R[Storage Resources]
E --> S[Network Components]Cloud Source Repositories
graph LR
A[Cloud Source Repositories] --> B[Git Hosting]
A --> C[Integration]
A --> D[Security]
A --> E[Collaboration]
B --> F[Private Git Repositories]
B --> G[Branch Management]
B --> H[Code History]
C --> I[Cloud Build Triggers]
C --> J[IDE Integration]
C --> K[Cloud Shell Editor]
D --> L[IAM Integration]
D --> M[Audit Logging]
D --> N[Encryption]
E --> O[Code Reviews]
E --> P[Team Permissions]
E --> Q[Mirroring]Binary Authorization
graph TB
A[Binary Authorization] --> B[Attestation]
A --> C[Policy Enforcement]
A --> D[Continuous Verification]
B --> E[Build Attestation]
B --> F[Vulnerability Scan]
B --> G[Quality Gate]
B --> H[Manual Approval]
C --> I[Deployment Policy]
C --> J[Cluster Admission Controller]
C --> K[Image Verification]
D --> L[Runtime Monitoring]
D --> M[Policy Violations]
D --> N[Audit Trail]
O[Build Process] --> P[Container Image]
P --> Q[Vulnerability Scanning]
Q --> R[Attestation Creation]
R --> S[Policy Check]
S --> T[Deployment Decision]10. Serverless and Event-Driven Architecture
Serverless Computing Stack
graph TB
A[Serverless on GCP] --> B[Cloud Functions]
A --> C[Cloud Run]
A --> D[App Engine]
A --> E[Workflows]
B --> F[Event-driven Functions]
B --> G[HTTP Functions]
B --> H[Background Functions]
C --> I[Containerized Serverless]
C --> J[Any Language/Runtime]
C --> K[Request-driven Scaling]
D --> L[Platform as a Service]
D --> M[Zero Server Management]
D --> N[Integrated Services]
E --> O[Workflow Orchestration]
E --> P[Service Integration]
E --> Q[Error Handling]Event-Driven Architecture
graph LR
A[Event Sources] --> B[Event Triggers]
B --> C[Serverless Functions]
C --> D[Event Processing]
D --> E[Output Actions]
A --> F[Cloud Storage]
A --> G[Pub/Sub]
A --> H[Firestore]
A --> I[HTTP Requests]
B --> J[Cloud Functions Triggers]
B --> K[Eventarc]
B --> L[Cloud Run Events]
C --> M[Function Execution]
C --> N[Container Startup]
C --> O[Auto-scaling]
E --> P[Database Updates]
E --> Q[API Calls]
E --> R[File Operations]
E --> S[Notifications]Cloud Workflows
stateDiagram-v2
[*] --> StartWorkflow
StartWorkflow --> DataValidation
DataValidation --> ProcessData : Valid
DataValidation --> ErrorHandling : Invalid
ProcessData --> CallAPI
CallAPI --> SaveResults : Success
CallAPI --> RetryLogic : Failure
RetryLogic --> CallAPI : Retry
RetryLogic --> ErrorHandling : Max Retries
SaveResults --> SendNotification
SendNotification --> [*]
ErrorHandling --> LogError
LogError --> [*]Eventarc
graph TB
A[Eventarc] --> B[Event Sources]
A --> C[Event Routing]
A --> D[Event Targets]
A --> E[Event Filtering]
B --> F[Cloud Storage]
B --> G[Pub/Sub]
B --> H[Audit Logs]
B --> I[Custom Applications]
C --> J[Cloud Events Format]
C --> K[Event Delivery]
C --> L[Retry Logic]
D --> M[Cloud Run Services]
D --> N[Cloud Functions]
D --> O[GKE Services]
D --> P[Workflows]
E --> Q[Attribute Filtering]
E --> R[Path Filtering]
E --> S[Content-based Routing]11. Containerization and Orchestration
Google Kubernetes Engine (GKE)
graph TB
A[GKE Cluster] --> B[Control Plane]
A --> C[Node Pools]
A --> D[Networking]
A --> E[Storage]
B --> F[API Server]
B --> G[etcd]
B --> H[Scheduler]
B --> I[Controller Manager]
C --> J[Standard Nodes]
C --> K[Preemptible Nodes]
C --> L[Spot Nodes]
C --> M[Auto-scaling]
D --> N[VPC-native Networking]
D --> O[Service Mesh]
D --> P[Network Policies]
E --> Q[Persistent Volumes]
E --> R[Storage Classes]
E --> S[CSI Drivers]GKE Cluster Types
graph LR
A[GKE Cluster Types] --> B[Standard Cluster]
A --> C[Autopilot Cluster]
B --> D[Full Control]
B --> E[Node Management]
B --> F[Custom Configuration]
B --> G[Lower Cost for Predictable Workloads]
C --> H[Simplified Management]
C --> I[Google-managed Nodes]
C --> J[Optimized Configuration]
C --> K[Pay-per-Pod Pricing]
L[Autopilot Benefits] --> M[Security Hardening]
L --> N[Resource Optimization]
L --> O[Reduced Operational Overhead]
L --> P[SLA Guarantees]Container Registry and Artifact Registry
graph TB
A[Container Image Management] --> B[Container Registry]
A --> C[Artifact Registry]
B --> D[Docker Images]
B --> E[Basic Image Storage]
B --> F[Vulnerability Scanning]
C --> G[Multi-format Support]
C --> H[Regional Repositories]
C --> I[Enhanced Security]
C --> J[Fine-grained Access Control]
G --> K[Docker Images]
G --> L[Maven Artifacts]
G --> M[npm Packages]
G --> N[Python Packages]
O[Integration] --> P[Cloud Build]
O --> Q[GKE]
O --> R[Cloud Run]
O --> S[Compute Engine]Istio Service Mesh
graph TB
A[Istio on GKE] --> B[Data Plane]
A --> C[Control Plane]
A --> D[Features]
B --> E[Envoy Sidecars]
B --> F[Proxy Traffic]
B --> G[Telemetry Collection]
C --> H[Pilot]
C --> I[Citadel]
C --> J[Galley]
D --> K[Traffic Management]
D --> L[Security]
D --> M[Observability]
D --> N[Policy Enforcement]
K --> O[Load Balancing]
K --> P[Circuit Breaking]
K --> Q[Canary Deployments]
L --> R[mTLS]
L --> S[Authentication]
L --> T[Authorization]Kubernetes Deployment Strategies
graph LR
A[Deployment Strategies] --> B[Rolling Update]
A --> C[Blue-Green]
A --> D[Canary]
A --> E[A/B Testing]
B --> F[Gradual Replacement]
B --> G[Zero Downtime]
B --> H[Easy Rollback]
C --> I[Parallel Environments]
C --> J[Instant Switching]
C --> K[Resource Intensive]
D --> L[Progressive Traffic Shift]
D --> M[Risk Mitigation]
D --> N[Metrics-based Decisions]
E --> O[Feature Flag Integration]
E --> P[User Segmentation]
E --> Q[Performance Comparison]12. Monitoring and Operations
Google Cloud Operations Suite
graph TB
A[Cloud Operations Suite] --> B[Cloud Monitoring]
A --> C[Cloud Logging]
A --> D[Cloud Trace]
A --> E[Cloud Profiler]
A --> F[Cloud Debugger]
A --> G[Error Reporting]
B --> H[Metrics Collection]
B --> I[Alerting]
B --> J[Dashboards]
B --> K[Uptime Monitoring]
C --> L[Log Aggregation]
C --> M[Log Analysis]
C --> N[Log-based Metrics]
C --> O[Log Routing]
D --> P[Distributed Tracing]
D --> Q[Latency Analysis]
D --> R[Performance Insights]
E --> S[Performance Profiling]
E --> T[Resource Usage Analysis]
F --> U[Live Debugging]
F --> V[Code Inspection]
G --> W[Error Tracking]
G --> X[Error Analysis]Monitoring Architecture
graph LR
A[Applications] --> B[Metrics Export]
B --> C[Cloud Monitoring]
C --> D[Dashboards]
C --> E[Alerting]
A --> F[Log Generation]
F --> G[Cloud Logging]
G --> H[Log Analysis]
G --> I[Log-based Alerts]
A --> J[Trace Data]
J --> K[Cloud Trace]
K --> L[Performance Analysis]
E --> M[Notification Channels]
M --> N[Email]
M --> O[SMS]
M --> P[Slack]
M --> Q[PagerDuty]Site Reliability Engineering (SRE)
mindmap
root((SRE Principles))
Service Level Objectives
SLI - Service Level Indicators
SLO - Service Level Objectives
SLA - Service Level Agreements
Error Budgets
Monitoring
Golden Signals
Alerting
Dashboards
Runbooks
Reliability
Fault Tolerance
Disaster Recovery
Capacity Planning
Performance Optimization
Automation
Deployment Automation
Incident Response
Self-healing Systems
Chaos EngineeringAlerting and Incident Response
sequenceDiagram
participant System
participant Monitoring
participant Alerting
participant OnCall
participant Response
System->>Monitoring: Emit Metrics/Logs
Monitoring->>Monitoring: Evaluate Conditions
Monitoring->>Alerting: Trigger Alert
Alerting->>OnCall: Notify Engineer
OnCall->>Response: Incident Response
Response->>System: Mitigation Actions
Response->>Monitoring: Verify Resolution
Monitoring->>Alerting: Clear Alert13. Cost Management and Optimization
GCP Cost Management Tools
graph TB
A[Cost Management] --> B[Billing Console]
A --> C[Cloud Asset Inventory]
A --> D[Recommender]
A --> E[Pricing Calculator]
B --> F[Cost Breakdown]
B --> G[Usage Reports]
B --> H[Budget Alerts]
B --> I[Export to BigQuery]
C --> J[Resource Inventory]
C --> K[Cost Attribution]
C --> L[Asset Tracking]
D --> M[Rightsizing Recommendations]
D --> N[Idle Resource Detection]
D --> O[Sustained Use Discounts]
D --> P[Committed Use Discounts]
E --> Q[Architecture Cost Estimation]
E --> R[Service Comparison]
E --> S[Regional Pricing]Cost Optimization Strategies
mindmap
root((Cost Optimization))
Resource Management
Rightsizing VMs
Preemptible Instances
Sustained Use Discounts
Committed Use Discounts
Storage Optimization
Lifecycle Policies
Storage Class Selection
Data Compression
Duplicate Elimination
Network Optimization
Regional Placement
CDN Usage
Egress Minimization
Premium vs Standard Tier
Automation
Auto-scaling
Scheduled Scaling
Resource Cleanup
Policy EnforcementBilling Account Structure
graph TB
A[Billing Account] --> B[Organizations]
B --> C[Folders]
C --> D[Projects]
D --> E[Resources]
F[Cost Control] --> G[Budgets]
F --> H[Alerts]
F --> I[Quotas]
F --> J[Billing Export]
G --> K[Project-level Budgets]
G --> L[Service-level Budgets]
G --> M[Custom Budgets]
H --> N[Threshold Alerts]
H --> O[Forecasted Alerts]
H --> P[Pub/Sub Notifications]
I --> Q[API Quotas]
I --> R[Resource Quotas]
J --> S[BigQuery Export]
J --> T[Cloud Storage Export]Pricing Models
graph LR
A[GCP Pricing Models] --> B[On-Demand]
A --> C[Preemptible/Spot]
A --> D[Sustained Use Discounts]
A --> E[Committed Use Discounts]
A --> F[Reserved Capacity]
B --> G[Pay-as-you-go]
B --> H[No upfront commitment]
B --> I[Highest per-unit cost]
C --> J[Up to 80% savings]
C --> K[Can be interrupted]
C --> L[Fault-tolerant workloads]
D --> M[Automatic discounts]
D --> N[Usage-based]
D --> O[No upfront payment]
E --> P[1 or 3-year terms]
E --> Q[Up to 57% savings]
E --> R[Flexible usage]
F --> S[BigQuery slots]
F --> T[Guaranteed capacity]
F --> U[Predictable costs]14. Hybrid and Multi-Cloud
Anthos Platform
graph TB
A[Anthos] --> B[Anthos GKE]
A --> C[Anthos Config Management]
A --> D[Anthos Service Mesh]
A --> E[Anthos Identity Service]
B --> F[GKE on Google Cloud]
B --> G[GKE on AWS]
B --> H[GKE on Azure]
B --> I[GKE on VMware]
C --> J[Policy Management]
C --> K[Configuration Sync]
C --> L[GitOps Workflow]
D --> M[Service-to-Service Security]
D --> N[Traffic Management]
D --> O[Observability]
E --> P[Identity Federation]
E --> Q[Single Sign-On]
E --> R[Access Policies]Hybrid Architecture
graph TB
subgraph "On-Premises"
A[Data Center]
B[Legacy Applications]
C[Databases]
D[Anthos on VMware]
end
subgraph "Google Cloud"
E[GKE Clusters]
F[Cloud Services]
G[BigQuery]
H[Cloud Storage]
end
subgraph "Other Clouds"
I[AWS EKS]
J[Azure AKS]
K[Edge Locations]
end
A --> L[Cloud Interconnect]
L --> E
D --> M[Anthos Control Plane]
M --> E
M --> I
M --> J
N[Anthos Config Management] --> D
N --> E
N --> I
N --> JMulti-Cloud Deployment
graph LR
A[Application] --> B[Anthos Deployment]
B --> C[Google Cloud]
B --> D[AWS]
B --> E[Azure]
B --> F[On-Premises]
C --> G[GKE Cluster]
D --> H[EKS Cluster]
E --> I[AKS Cluster]
F --> J[VMware Cluster]
K[Centralized Management] --> L[Policy Enforcement]
K --> M[Security Management]
K --> N[Monitoring]
K --> O[Updates]
L --> G
L --> H
L --> I
L --> JCloud Interconnect Options
graph TB
A[Cloud Interconnect] --> B[Dedicated Interconnect]
A --> C[Partner Interconnect]
A --> D[Cloud VPN]
B --> E[Physical Connection]
B --> F[10 Gbps or 100 Gbps]
B --> G[Lowest Latency]
B --> H[Highest Throughput]
C --> I[Service Provider Connection]
C --> J[Flexible Bandwidth]
C --> K[Faster Provisioning]
C --> L[SLA Guarantees]
D --> M[IPsec VPN Tunnels]
D --> N[Internet-based]
D --> O[Quick Setup]
D --> P[Lower Bandwidth]
Q[Use Cases] --> R[Data Migration]
Q --> S[Hybrid Workloads]
Q --> T[Disaster Recovery]
Q --> U[Compliance Requirements]15. Advanced Architectures and Best Practices
Microservices Architecture
graph TB
A[Client Applications] --> B[API Gateway]
B --> C[Load Balancer]
C --> D[User Service]
C --> E[Product Service]
C --> F[Order Service]
C --> G[Payment Service]
D --> H[Cloud SQL]
E --> I[Firestore]
F --> J[Cloud Spanner]
G --> K[Cloud SQL]
L[Event Bus] --> M[Pub/Sub]
D --> M
E --> M
F --> M
G --> M
N[Monitoring] --> O[Cloud Monitoring]
N --> P[Cloud Trace]
N --> Q[Cloud Logging]Event-Driven Serverless Architecture
sequenceDiagram
participant User
participant LB as Load Balancer
participant CR as Cloud Run
participant PS as Pub/Sub
participant CF as Cloud Functions
participant BQ as BigQuery
participant ST as Cloud Storage
User->>LB: HTTP Request
LB->>CR: Route Request
CR->>PS: Publish Event
PS->>CF: Trigger Function
CF->>BQ: Insert Data
CF->>ST: Store Files
CF->>PS: Publish Result
PS->>CR: Notify Completion
CR->>LB: Response
LB->>User: Final ResponseData Processing Pipeline
graph LR
A[Data Sources] --> B[Pub/Sub]
B --> C[Dataflow]
C --> D[BigQuery]
C --> E[Cloud Storage]
F[Batch Sources] --> G[Cloud Storage]
G --> H[Dataflow Batch]
H --> D
I[External APIs] --> J[Cloud Functions]
J --> B
D --> K[Data Studio]
D --> L[Looker]
D --> M[Vertex AI]
N[Orchestration] --> O[Cloud Composer]
O --> C
O --> H
O --> JHigh Availability Architecture
graph TB
subgraph "Region 1"
A[Zone A]
B[Zone B]
C[Zone C]
A --> D[GKE Cluster]
B --> E[GKE Cluster]
C --> F[GKE Cluster]
end
subgraph "Region 2"
G[Zone A]
H[Zone B]
I[Zone C]
G --> J[GKE Cluster]
H --> K[GKE Cluster]
I --> L[GKE Cluster]
end
M[Global Load Balancer] --> D
M --> E
M --> F
M --> J
M --> K
M --> L
N[Cloud Spanner] --> O[Multi-region]
P[Cloud Storage] --> Q[Multi-region]Disaster Recovery Strategy
graph TB
A[DR Strategies] --> B[Backup and Restore]
A --> C[Pilot Light]
A --> D[Warm Standby]
A --> E[Multi-site Active-Active]
B --> F[Low Cost]
B --> G[Higher RTO/RPO]
B --> H[Periodic Backups]
C --> I[Core Components Running]
C --> J[Quick Scale-up]
C --> K[Moderate Cost]
D --> L[Scaled-down Replica]
D --> M[Faster Recovery]
D --> N[Higher Cost]
E --> O[Full Redundancy]
E --> P[Instant Failover]
E --> Q[Highest Cost]
R[RTO Targets] --> S[Minutes]
R --> T[Hours]
R --> U[Days]
V[RPO Targets] --> W[Near Zero]
V --> X[Minutes]
V --> Y[Hours]Security Best Practices
mindmap
root((Security Best Practices))
Identity & Access
Principle of Least Privilege
Service Account Management
Multi-factor Authentication
Regular Access Reviews
Network Security
VPC Security
Firewall Rules
Private Google Access
VPN/Interconnect
Data Protection
Encryption at Rest
Encryption in Transit
Key Management
Data Classification
Application Security
Container Security
Binary Authorization
Secret Management
Security Scanning
Compliance
Audit Logging
Compliance Monitoring
Policy Enforcement
Incident ResponseWell-Architected Framework
graph TB
A[Google Cloud Architecture Framework] --> B[Operational Excellence]
A --> C[Security, Privacy & Compliance]
A --> D[Reliability]
A --> E[Cost Optimization]
A --> F[Performance Optimization]
B --> G[Monitoring & Alerting]
B --> H[Incident Response]
B --> I[Change Management]
B --> J[Automation]
C --> K[Identity Management]
C --> L[Data Protection]
C --> M[Network Security]
C --> N[Compliance Controls]
D --> O[Fault Tolerance]
D --> P[Disaster Recovery]
D --> Q[Scalability]
D --> R[Testing]
E --> S[Resource Optimization]
E --> T[Cost Monitoring]
E --> U[Pricing Models]
E --> V[Waste Reduction]
F --> W[Latency Optimization]
F --> X[Throughput Optimization]
F --> Y[Resource Selection]
F --> Z[Global Distribution]Conclusion
This comprehensive guide covers Google Cloud Platform from beginner to expert level, including:
Key Learning Path
graph LR
A[GCP Fundamentals] --> B[Core Services Mastery]
B --> C[Architecture Design]
C --> D[Specialized Services]
D --> E[Advanced Patterns]
E --> F[Expert Practices]
A --> G[Cloud Concepts]
A --> H[GCP Console]
A --> I[Basic Services]
B --> J[Compute, Storage, Network]
B --> K[Security & IAM]
B --> L[Monitoring]
C --> M[Multi-tier Apps]
C --> N[Microservices]
C --> O[Event-driven]
D --> P[AI/ML]
D --> Q[Big Data]
D --> R[DevOps]
E --> S[Anthos]
E --> T[Multi-cloud]
E --> U[Advanced Security]
F --> V[Optimization]
F --> W[Best Practices]
F --> X[Innovation]GCP Certifications Path
graph TB
A[GCP Certifications] --> B[Associate Level]
A --> C[Professional Level]
B --> D[Cloud Digital Leader]
B --> E[Associate Cloud Engineer]
C --> F[Professional Cloud Architect]
C --> G[Professional Cloud Developer]
C --> H[Professional Cloud DevOps Engineer]
C --> I[Professional Data Engineer]
C --> J[Professional Cloud Security Engineer]
C --> K[Professional Cloud Network Engineer]
C --> L[Professional Machine Learning Engineer]Next Steps
- Hands-on Practice: Use GCP Free Tier and Qwiklabs
- Build Projects: Create real-world applications
- Community Engagement: Join GCP communities and events
- Continuous Learning: Stay updated with new services
- Certification: Validate your skills with GCP certifications
- Specialization: Focus on specific domains (AI/ML, Data, Security)
Key Takeaways
- Start Small: Begin with core services and gradually expand
- Practice Regularly: Hands-on experience is crucial
- Design for Scale: Consider scalability from the beginning
- Security First: Implement security best practices early
- Cost Awareness: Monitor and optimize costs continuously
- Stay Current: GCP evolves rapidly with new features
The cloud journey is continuous – embrace learning, experimentation, and innovation with Google Cloud Platform!
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