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Cloud Architecture

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Summary

PDF p.163

Cloud architecture encompasses various models and technologies, including serverless computing and microservices, which enhance scalability, efficiency, and flexibility. These innovations allow organizations to focus on application development without managing underlying infrastructure, while also introducing new security considerations.

In plain words

Supplementary — not from your PDF

Serverless computing runs your code only when an event triggers it, and the provider handles all the servers and scaling (AWS Lambda, Azure Functions). Microservices split an application into small independent services that can be scaled and updated separately. They're often deployed with Infrastructure as Code. Cloud-native services such as auto-scaling, CDNs, object storage and IAM change how systems are built.

Detailed explanation

PDF p.163
  • Serverless Computing
    • Definition: Cloud provider manages infrastructure, automatically allocating resources as needed and charging based on actual usage.
    • Applications: Includes chatbots, mobile backends, and event-driven processing.
    • Providers: Major providers include AWS Lambda, Google Cloud Functions, and Microsoft Azure Functions.
    • Benefits: Scalable, cost-effective, and easy to manage. Eliminates the need for server management and emphasizes event-driven orchestration.
  • Microservices
    • Definition: Architectural approach where applications are built as a collection of small, independent services, each focusing on a specific business capability.
    • Benefits: Modular design, easier scaling and updating, independent team development.
    • Challenges: Integration issues can arise when combining independent components.
    • Related Technologies: Often implemented using Infrastructure as Code (IaC) for consistent and repeatable deployments.
  • Transformational Changes
    • Cloud-Native Services: Enable dynamic scaling, innovation, and operational optimization.
    • Key Services
      • Elastic Compute and Auto-Scaling: Adjust computing power based on demand.
      • Content Delivery Networks (CDNs): Optimize web traffic by caching content.
      • Object Storage: Provides massive, unstructured data storage.
      • Identity and Access Management: Advanced security features and platform integration.
      • Containerization and Orchestration: Change how applications are deployed and managed.
      • AI and Machine Learning Services: Enhance data processing and analytics.
      • Serverless Databases and IoT Services: Support backend operations and big data analytics.

Important terms

taken from the text above
Serverless Computing
Cloud provider manages infrastructure, automatically allocating resources as needed and charging based on actual usage.
Providers
Major providers include AWS Lambda, Google Cloud Functions, and Microsoft Azure Functions.
Microservices
Architectural approach where applications are built as a collection of small, independent services, each focusing on a specific business capability.
Related Technologies
Often implemented using Infrastructure as Code (IaC) for consistent and repeatable deployments.
Cloud-Native Services
Enable dynamic scaling, innovation, and operational optimization.
Elastic Compute and Auto-Scaling
Adjust computing power based on demand.
Content Delivery Networks (CDNs)
Optimize web traffic by caching content.
Object Storage
Provides massive, unstructured data storage.
Identity and Access Management
Advanced security features and platform integration.
Containerization and Orchestration
Change how applications are deployed and managed.
AI and Machine Learning Services
Enhance data processing and analytics.
Serverless Databases and IoT Services
Support backend operations and big data analytics.
CDNs Content Delivery Networks

Examples & real-world scenarios

Supplementary — not from your PDF
  • A function that resizes each image uploaded to storage.
  • An online shop split into cart, payment and catalogue microservices.
  • A CDN caching images close to users.

Scenario

A company wants a chatbot back end that is idle most of the day. Serverless charges only for actual use, so it fits better than an always-on VM.

Common mistakes

Supplementary — not from your PDF
  • Thinking microservices have no downside. Integrating many independent parts is harder.
  • Assuming serverless removes the need to secure code and access.

Practical skills

Supplementary — not from your PDF
  • Explain when serverless or microservices fit a workload.

What I should remember

Key Points PDF p.163
  • Serverless Computing
    • Managed Infrastructure: No need for server management.
    • Event-Driven: Orchestration based on triggers.
    • Examples: AWS Lambda, Google Cloud Functions, Azure Functions.
  • Microservices
    • Modular Design: Independent services with specific responsibilities.
    • Scalability: Easier to scale and update.
    • Integration Challenges: Potential issues when combining components.
    • IaC: Ensures consistent deployments.
  • Transformational Changes
    • Elastic Compute: Dynamic scaling.
    • CDNs: Web traffic optimization.
    • Object Storage: Large-scale data storage.
    • Security: Advanced identity and access management.
    • Containerization: Modern application deployment.