Cloud Giants: AWS, Azure & Google Cloud – Features, Use Cases & Comparisons

Key Concepts and Services

1. Amazon Web Services (AWS)

AWS is the market leader in cloud computing, offering over 200 fully featured services from data centers globally. Its core strengths lie in scalability, reliability, and a vast ecosystem of tools for developers, startups, and enterprises.

Core AWS Services

mindmap
  root((AWS Services))
    Compute
      EC2: Virtual servers (Elastic Compute Cloud)
      Lambda: Serverless functions
    Storage
      S3: Object storage (e.g., hosting static websites)
      EBS: Block storage for EC2
    Databases
      RDS: Managed relational databases (MySQL, PostgreSQL)
      DynamoDB: NoSQL database
    Networking
      VPC: Virtual Private Cloud (isolated networks)
      Route 53: DNS and domain management
    AI/ML
      SageMaker: Machine learning platform
      Rekognition: Image/video analysis
    Security
      IAM: Identity and Access Management
      KMS: Key Management Service

How AWS Works: A Real Example

Scenario: Ncell uses AWS to handle 10M+ daily transactions.

• Compute: EC2 instances process billing and customer queries.
• Storage: S3 stores customer data (encrypted via KMS).
• AI: SageMaker predicts network congestion using historical data.
• Networking: Route 53 routes traffic globally with low latency.

2. Microsoft Azure

Azure is Microsoft’s cloud platform, tightly integrated with Windows, Office 365, and enterprise tools like Active Directory. It excels in hybrid cloud solutions (combining on-premises and cloud) and AI/ML for businesses.

Core Azure Services

mindmap
  root((Azure Services))
    Compute
      Virtual Machines: IaaS (like AWS EC2)
      Azure Functions: Serverless (like AWS Lambda)
    Storage
      Blob Storage: Object storage (like S3)
      Azure SQL Database: Managed relational DB
    Networking
      Virtual Network: Isolated cloud networks
      Azure DNS: Domain management
    AI/ML
      Azure Cognitive Services: Pre-built AI models (e.g., speech recognition)
      Synapse Analytics: Big data processing
    Security
      Azure AD: Identity management
      Key Vault: Secrets management

How Azure Works: A Real Example

Scenario: Khalti uses Azure for secure payments.

• Compute: Virtual Machines handle transaction processing.
• AI: Cognitive Services detects fraud via facial recognition.
• Hybrid Cloud: Azure Arc connects on-premises Khalti servers to cloud backups.

3. Google Cloud Platform (GCP)

GCP is Google’s cloud offering, leveraging its global fiber network, AI expertise (TensorFlow), and big data tools. It’s preferred for data analytics, AI, and Kubernetes-based workloads.

Core GCP Services

mindmap
  root((GCP Services))
    Compute
      Compute Engine: Virtual machines (like AWS EC2)
      Cloud Functions: Serverless (like AWS Lambda)
    Storage
      Cloud Storage: Object storage (like S3)
      Firestore: NoSQL database
    Databases
      Cloud SQL: Managed relational DB
      BigQuery: Serverless data warehouse
    Networking
      Virtual Private Cloud: Isolated networks
      Cloud Load Balancing: Traffic distribution
    AI/ML
      Vertex AI: Unified ML platform
      TensorFlow Enterprise: Custom ML models
    Security
      Cloud IAM: Identity management
      Secret Manager: Encryption keys

How GCP Works: A Real Example

Scenario: YouTube (Google) uses GCP for video processing.

• Compute: Compute Engine renders videos globally.
• AI: Vertex AI recommends videos via ML.
• Networking: Global Load Balancer distributes traffic.

In the Real World

1. eSewa (Nepal):

   • Uses AWS Lambda for serverless payment processing to handle spikes during Dashain/Tihar.
   • S3 stores transaction logs securely.

2. Daraz (Alibaba Group):

   • Relies on AWS EC2 for dynamic scaling during sales (e.g., 11.11).
   • Route 53 ensures fast global delivery.

3. NTC (Nepal Telecom):

   • Deploys Azure Virtual Machines for VoIP services.
   • Azure AD manages employee access.

4. WhatsApp (Meta):

   • Runs on Google Cloud for message encryption and scaling.
   • BigQuery analyzes user behavior for ads.

Comparison of AWS, Azure, and GCP

Worked Example: Choosing a Cloud for a Nepalese Bank

Scenario: A bank in Kathmandu wants to launch a mobile app with:

• 10K+ daily transactions
• KYC (Know Your Customer) via AI
• Hybrid cloud (some data on-premises)

Solution:

1. Compute: Use Azure Virtual Machines (for Windows-based legacy systems).
2. AI: Azure Cognitive Services for KYC (facial recognition).
3. Database: Azure SQL Database for transactions.
4. Hybrid Cloud: Azure Arc to connect on-premises servers.
5. Cost: Pay only for used resources (Azure’s pay-as-you-go).

Why Not AWS/GCP?

• AWS lacks tight Windows integration.
• GCP is weaker in hybrid cloud tools.

Exam Tip

1. Memorize the 3 Pillars:

   • AWS = Scalability (EC2, S3, Lambda)
   • Azure = Enterprise/Windows (Active Directory, Hybrid Cloud)
   • GCP = AI/Data (BigQuery, Vertex AI)

2. Compare Services:

   • AWS EC2 ↔ Azure VMs ↔ GCP Compute Engine
   • AWS S3 ↔ Azure Blob Storage ↔ GCP Cloud Storage

3. Real-World Links:

   • Ncell → AWS for VoIP
   • Khalti → Azure for payments
   • YouTube → GCP for video processing

4. Diagrams in Exams:

   • Draw a layered model of AWS/Azure/GCP services (Compute → Storage → Networking → AI).
   • Sketch a sequence diagram of how a user’s request flows (e.g., Daraz order → AWS EC2 → S3).

5. Common Pitfalls:

   • Don’t confuse IaaS (EC2, VMs) with PaaS (Heroku, App Engine).
   • Remember: Azure AD ≠ AWS IAM (different identity models).