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On this page
  • Overview
  • Core Services
  • Compute
  • Storage
  • Databases
  • Networking
  • DevOps & Management
  • Differences Between GCP, AWS, and Azure
  • 1. Market Position and History
  • 2. Service Philosophy and Strengths
  • 3. Management Interfaces and Tools
  • 4. Architectural Differences
  • 5. Certification and Learning Path
  • Well-Architected Framework
  • Getting Started with GCP
  • Use Cases
  • References
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  1. Cloud Platforms

Google Cloud

Google Cloud Platform (GCP) - Modern cloud infrastructure and services

Overview

Google Cloud Platform (GCP) is a leading cloud provider offering a wide range of infrastructure, platform, and software services. As of May 2025, GCP is known for its strengths in data analytics, machine learning, and Kubernetes, and is the third largest public cloud provider globally.

GCP provides IaaS, PaaS, and SaaS offerings to help organizations innovate, scale, and optimize costs.

Core Services

GCP organizes its services into several key categories:

Compute

  • Compute Engine: Virtual machines (VMs) in the cloud

  • Cloud Functions: Serverless event-driven compute

  • Google Kubernetes Engine (GKE): Managed Kubernetes clusters

  • App Engine: PaaS for deploying applications

Storage

  • Cloud Storage: Object storage

  • Persistent Disk: Block storage for VMs

  • Filestore: Managed file storage

  • Archive Storage: Low-cost archival storage

Databases

  • Cloud SQL: Managed relational databases

  • Cloud Spanner: Globally distributed SQL database

  • Firestore: NoSQL document database

  • Bigtable: Wide-column NoSQL database

  • BigQuery: Serverless data warehouse

Networking

  • VPC: Virtual Private Cloud networking

  • Cloud DNS: Managed DNS

  • Cloud CDN: Content Delivery Network

  • Cloud Load Balancing: Global and regional load balancing

  • Cloud Interconnect: Dedicated connectivity

DevOps & Management

  • Deployment Manager: Infrastructure as code

  • Cloud Monitoring: Observability and monitoring

  • Cloud Logging: Centralized log management

  • Cloud Build/Cloud Deploy: CI/CD services

Differences Between GCP, AWS, and Azure

1. Market Position and History

  • GCP: Launched in 2011, strong in analytics, AI/ML, and Kubernetes

  • AWS: First-mover, largest market share, broadest service portfolio

  • Azure: Enterprise integration, hybrid cloud, Microsoft ecosystem

2. Service Philosophy and Strengths

GCP

  • Data & AI: Industry-leading analytics and ML services (BigQuery, Vertex AI)

  • Kubernetes Leadership: Originator of Kubernetes, best-in-class GKE

  • Network Performance: Built on Google's global fiber network

  • Open Source: Strong open source and multi-cloud focus

AWS

  • Breadth of Services: Most extensive range of services

  • Global Infrastructure: Largest global footprint

  • Maturity: Most mature offerings

Azure

  • Enterprise Integration: Deep Microsoft integration

  • Hybrid Cloud: Azure Stack, Arc

  • Microsoft Ecosystem: .NET, Visual Studio, etc.

3. Management Interfaces and Tools

  • GCP: Google Cloud Console, gcloud CLI, Deployment Manager

  • AWS: AWS Console, CLI, CloudFormation

  • Azure: Azure Portal, CLI, ARM, Bicep

4. Architectural Differences

  • GCP: Data/AI-centric, open source, multi-cloud

  • AWS: Service-centric, broadest portfolio

  • Azure: Microsoft-centric, integrated platform

5. Certification and Learning Path

  • GCP: Data/AI/ML certifications, Professional Cloud Architect

  • AWS: Most recognized, specialized paths

  • Azure: Enterprise-focused certifications

Well-Architected Framework

GCP provides the Google Cloud Architecture Framework, a set of best practices for building secure, reliable, and efficient cloud solutions. It is organized around:

  1. Operational Excellence

  2. Security

  3. Reliability

  4. Performance Efficiency

  5. Cost Optimization

  6. Sustainability

Getting Started with GCP

  • GCP Free Tier: Try many GCP services for free

  • Google Cloud Training: Official learning paths

  • Architecture Framework: Review your architectures against best practices

  • Google Cloud Solutions Architects: Engage with GCP experts

Use Cases

GCP is particularly well-suited for:

  • Data Analytics & ML: BigQuery, Dataflow, Vertex AI

  • Global Web Applications: High availability, global distribution

  • Hybrid & Multi-Cloud: Anthos, open source focus

  • Enterprise Workloads: SAP, Oracle, Windows on GCP

  • Startups: Rapid scaling, cost optimization

References

PreviousTips & TricksNextServices

Last updated 4 days ago

☁️
GCP Official Documentation
GCP Architecture Center
Google Cloud Architecture Framework