Next-Gen DevOps & Cloud: The 2025 Architect’s Checklist

Code Style & Readability

1. Naming Conventions

☑️ 1.1 Consistency in Case Styles: Use a uniform case style throughout your codebase (e.g., snake_case for variables, PascalCase for classes, kebab-case for DNS names and URLs).

• Bad Practice:

resourcegroupname: myapp-resources
vpcname: prod-vpc

• Good Practice:

resource_group_name: myapp_resources
vpc_name: prod_vpc

☑️ 1.2 Prefixes to Denote Scope: Prefix names with context (e.g., tf_var_, k8s_svc_) to improve clarity and avoid conflicts.

• Bad Practice: myapp-network
• Good Practice: prod_vpc_myapp_network

☑️  1.3 Avoid Over-Contextualizing: Avoid overly verbose names; use clear, concise terms.

• Bad Practice: prod_vpc_myapp_network_security_group_internal
• Good Practice: prod_vpc_sg_internal

☑️ 1.4 Semantic Versioning: Implement semantic versioning (e.g., v1.0.0, v1.1.0, v1.0.1) for modules and scripts to track changes effectively.

• Bad Practice: Incrementing versions randomly (e.g., v5.6 to v10.3).
• Good Practice: Increment versions logically (v1.0.0 -> v1.1.0 for new features; v1.0.0 -> v1.0.1 for bug fixes).

☑️ 1.5 Case Sensitivity: Be mindful of case sensitivity in certain technologies (e.g., Linux file systems, specific API endpoints)

☑️ 1.6 Standardization: Create organization-wide naming guidelines using .editorconfig to ensure consistency across teams and repositories.

• Example Style Guide:

Naming Guide:
- Variables: snake_case
- Classes: PascalCase
- Functions: camelCase
- Constants: UPPERCASE

☑️ 1.7 Naming for API and IAM roles: Follow naming conventions for API endpoints and IAM roles to ensure consistency.

• Example API Endpoints: /users/{userId}/orders
• Example IAM roles: prod-app-ec2-role

☑️ 1.8 Naming Patterns for Logs: Standardize log naming conventions to ensure clarity and consistency.

• Example: Logs can include environment, service name, and severity: prod-auth-service-ERROR.log.

☑️1.9 Tooling:

• Linters: Enforce naming conventions and style guides using linters such as yamllint, Pylint, shellcheck, Checkstyle (for Java), and HCL Prettier (for Terraform).
• CI Workflows: Integrate linters and formatters into CI pipelines (e.g., GitHub Actions, GitLab CI) to catch issues before they reach production.

Example: Use yamllint in GitHub Actions to validate YAML files in pull requests.

name: YAML Lint
on: [push, pull_request]
jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - name: Run yamllint
      run: yamllint .

Example of eslint configuration in package.json:

"eslintConfig": {
         "rules": {
           "camelcase": ["error"],
           "no-underscore-dangle": ["error"]
      }
   }

• Key Takeaways for Code Style & Readability:
  • Use consistent naming conventions, modular code, and clear documentation for maintainable codebases.
  • Automate code checks with linters, formatters, and CI pipelines.

2. Code Clarity and Organization

☑️ 2.1 Structure Code into Modules: Separate reusable code into well-defined modules (e.g., modules/vpc/, modules/security_groups/, modules/database/) for easy maintenance and reusability.

• Bad Practice:

/main.tf
/variables.tf

• Good Practice:

/modules/vpc/main.tf
/modules/security_groups/main.tf
/environments/staging/
/environments/production/

☑️ 2.2 Single Responsibility Principle (SRP): Adhere to SRP, ensuring each module or script has one clear, well-defined purpose.

☑️ 2.3 Actionable Comments: Use actionable comments (e.g., TODO:, FIXME:) to indicate pending tasks and use “why” comments to explain the reasoning behind decisions, not just what the code is doing.

☑️ 2.4 Declarative Patterns: Use declarative logic (e.g., state-based configuration) in IaC tools like Terraform, Pulumi, or CloudFormation.

• Bad Practice:

for idx in range(5): create_security_group(idx)

• Good Practice:

resource "aws_security_group" "example" {
  count = 5
  name  = "sg-${count.index}"
}

☑️ 2.5 Pipeline Modularity: Build modular CI/CD pipelines (e.g., plan, build, deploy stages).

• Bad Practice: All jobs in a single pipeline stage.
• Good Practice: Separate stages such as plan, build, and deploy for clarity, use pipeline templates for common stages, and share code between jobs using git submodules or shared libraries in Jenkins.

☑️ 2.6 Pipeline Structure: Structure pipeline YAML files for large projects:

pipelines/
  build.yaml
  deploy.yaml
  test.yaml

Visual Example: Use a Mermaid.js diagram to illustrate a modular pipeline structure.

graph LR
  A[Code Commit] --> B(Build);
  B --> C{Unit Tests};
  C -- Pass --> D(Security Scan);
  C -- Fail --> E[Stop Pipeline];
  D --> F{Integration Tests};
  F -- Pass --> G(Deploy to Staging);
  F -- Fail --> E
  G --> H(Manual Approval);
  H -- Approved --> I(Deploy to Production);
  H -- Rejected --> E
  I --> J[Monitoring]
  E--> K[Notification]

☑️ 2.7 Git Branching: Follow Git branching strategies (e.g., Git flow, trunk-based development) for streamlined collaboration and code management.

☑️ 2.8 Tooling:

• Formatters: Use formatters (e.g., Prettier, Black, terraform fmt) for consistent code formatting.
• Linters: Use linters (e.g., yamllint, tfsec) to catch code style issues before formatting.
• Key Takeaways for Code Style & Readability:
  • Use consistent naming conventions, modular code, and clear documentation for maintainable codebases.
  • Automate code checks with linters, formatters, and CI pipelines.

3. Documentation in Code

☑️ 3.1 Comprehensive README.md: Include clear instructions, dependencies, usage examples, and configuration details in README.md files, placed both at the root level and within key subdirectories to provide context.

• Example Template: Adopt Markdown templates for consistency in READMEs:

# Project Name
## Overview
## Prerequisites
## Deployment Steps
## Contact

• Bad Practice:

# My Project
Run `terraform apply`.

• Good Practice:

# My Project
## Prerequisites
- Terraform >= 1.3.0
- AWS CLI configured

## Usage
1. Clone the repo.
2. Run `terraform init`.
3. Apply: `terraform apply`.

## Inputs
| Name         | Description         | Default     |
|--------------|---------------------|-------------|
| region       | AWS region          | us-east-1   |

☑️ 3.2 Environment Variables and Secrets: Document environment variables, secrets, and configuration options for each component.

☑️ 3.3 Architecture Diagrams: Create clear, visual diagrams (e.g., C4 model, PlantUML) to illustrate system architecture and the flow of traffic, including interactions between components such as load balancers, application servers, databases, and microservices.

☑️ 3.4 Docs-as-Code: Automate documentation updates using docs-as-code tools (e.g., mkdocs, Docusaurus) and store the documentation in the same repository as the code.

☑️ 3.5 Changelog: Track changes using a detailed CHANGELOG.md file, adhering to semantic versioning principles.

• Example Template: Adopt Markdown templates for consistency in CHANGELOGs:

# Changelog

    ## [v1.1.0] - 2025-01-01

    ### Features
    - Added new feature X

    ### Bug Fixes
    - Fixed bug Y

☑️ 3.6 Documentation Accessibility: Make documentation easily accessible in the development workflow by storing it in a central portal with a search feature.

• Use diagrams to complement written documentation.

☑️ 3.7 Visual Workflow Tools: Document CI/CD pipelines visually using Mermaid.js within Markdown files to provide a clear overview.

• Key Takeaways for Documentation in Code:
  • Maintain clear, comprehensive documentation using README.md, architecture diagrams, and docs-as-code tools.
  • Use Markdown templates for consistency.

Functional Requirements & Error Handling

1. Functional Requirements

☑️ 1.1 Define SLIs/SLOs: Define clear Service Level Indicators (SLIs, e.g., latency, error rate) and Service Level Objectives (SLOs, e.g., “API latency < 200ms, 99.9% of the time”) to measure performance and reliability.

• Bad Practice: No clear performance metrics.
• Good Practice: Define, “API latency < 200ms, 99.9% of the time.”

☑️ 1.2 Derive SLIs from KPIs: Understand how to derive SLIs from business KPIs.

• Example: For “cart abandonment rate,” monitor SLIs like API success rates, latency, and retry counts for /add-to-cart and /checkout endpoints.

☑️ 1.3 SLIs/SLOs Examples: Expand on SLI/SLO derivation with additional KPIs like “customer acquisition rate” or “average query latency”.

• Example: For “customer acquisition rate”, track SLIs like the success rate and latency of the registration or signup API. For “average query latency” monitor the SLIs like the query execution time and resources consumption.

☑️ 1.4 Capacity Planning: Perform capacity planning to forecast future usage and scaling needs based on traffic patterns and resource consumption. Use tools like AWS Compute Optimizer, Azure Advisor, or GCP Recommender to predict resource requirements.

• Example: Use AWS Compute Optimizer to predict future EC2 needs based on traffic patterns.

☑️ 1.5 Idempotency: Ensure idempotency in workflows to avoid errors during re-execution (e.g., applying the same Terraform configuration multiple times does not result in unwanted changes).

☑️ 1.6 Business Impact Analysis (BIA): Conduct a Business Impact Analysis (BIA) to prioritize critical infrastructure based on business value and potential risks.

• Example: In a multi-region application, prioritize database availability over caching for disaster recovery.

☑️ 1.7 Handling Different Types of Traffic: Understand the implications of different types of traffic (e.g., regular web traffic, high-volume API calls) and configure resources accordingly.

☑️ 1.8 Correlation between KPIs and Metrics: Correlate business KPIs to system metrics for a holistic view.

☑️ 1.9 Resiliency Testing: Test for resiliency using Chaos Engineering tools (e.g., Gremlin, Chaos Monkey) to ensure systems can handle unexpected failures.

1. Error Handling & Logging

☑️ 2.1 Centralized Logging: Use centralized logging systems (e.g., ELK Stack, CloudWatch, Datadog) to monitor events across environments and aggregate logs for analysis using tools like Splunk or SumoLogic.

• Alternative tools: Pair Grafana Loki with tools like ELK stack for centralized logging comparisons.
• Bad Practice: Logging locally without aggregation.
• Good Practice:

{
  "timestamp": "2025-01-01T12:00:00Z",
  "level": "error",
  "message": "Database connection failed",
  "correlation_id": "12345",
  "request_id": "req-1234"
}

☑️ 2.2 Standardized Log Levels: Use standardized log levels (e.g., DEBUG, INFO, ERROR, FATAL) to classify logs and control verbosity. Adjust log levels for production vs. debugging (e.g., use INFO in production, DEBUG in development).

☑️ 2.3 Standardized Error Codes: Use standardized error codes (e.g., DB_CONN_ERR, API_TIMEOUT) to categorize and identify issues efficiently.

• Bad Practice:

{
  "error": "something went wrong"
}

• Good Practice:

{
  "code": "DB_CONN_ERR",
  "message": "Database connection failed."
}

☑️ 2.4 Structured Error Handling: Implement structured error handling with specific exception handling.

try:
    db.connect()
except:
    print("Error")

• Good Practice:

try:
    db.connect()
except DatabaseError as e:
   logger.error(f"Database connection failed: {e}")

☑️ 2.5 Tracing: Use tracing tools (e.g., Jaeger, Zipkin) for distributed systems to trace requests end-to-end.

☑️ 2.6 Actionable Alerts: Automate alerts with actionable context (e.g., dashboards, runbooks, remediation playbooks). Group alerts to reduce noise and enable easier triage.

• Example: Include links to dashboards, runbooks, or remediation guides in alert messages.

☑️ 2.7 Request Context: Include request context in logs (e.g., request IDs, user IDs) to improve troubleshooting of specific use cases.

☑️ 2.8 Error Metrics: Add metrics on the amount of errors to detect service degradation faster.

• Example: Track error budget utilization to align with SLIs/SLOs.

☑️ 2.9 Correlation IDs: Use correlation IDs to connect logs and traces for easier debugging.

☑️ 2.10 Error Visualization: Use visualization tools like Grafana Loki to display error trends and easily identify patterns.

☑️ 2.11 Alert Prioritization: Categorize alerts by severity levels (e.g., P1 for outages, P2 for degraded performance) to effectively manage incidents.

☑️ 2.12 Log Security: Ensure sensitive information is redacted or masked in logs to maintain security.

• Key Takeaways for Error Handling:
  • Use centralized logging systems for all environments.
  • Adopt correlation IDs for streamlined debugging.
  • Automate alerts with actionable context linked to dashboards.

Testing

1. Unit Testing

☑️ 3.1 Isolated Testing: Test individual modules and scripts in isolation by mocking resources and dependencies. Use fixtures or environment variables instead of hardcoding credentials.

• Bad Practice: Hardcoding credentials in tests.
• Good Practice: Use fixtures or environment variables:

export AWS_ACCESS_KEY_ID=mockAccessKey

☑️ 3.2 Edge Cases: Cover edge cases (e.g., invalid inputs, boundary conditions) to ensure robust functionality.

☑️ 3.3 Property-Based Testing: Utilize property-based testing techniques to generate a wide variety of test cases and ensure comprehensive coverage.

☑️ 3.4 Testing Frameworks: Use testing frameworks (e.g., pytest-mock and factory_boy for Python, Mocha, Chai, and Sinon.js for Node.js) and include examples on how to use them, such as how to configure pytest fixtures.

• Python Example:

import pytest
from unittest.mock import patch
from my_module import my_function

@pytest.fixture
def mock_external_api():
     with patch('my_module.external_api_call') as mock:
         mock.return_value = {"status": "ok"}
         yield mock

def test_my_function_with_mocked_api(mock_external_api):
     result = my_function()
     assert result == "success"

☑️ 3.5 Code Analysis Tools: Use code analysis tools (e.g., SonarQube, Code Climate) to improve code quality and identify issues early.

☑️ 3.6 Coverage: Maintain at least 80% code coverage for critical workflows.

☑️ 3.7 Enhanced Testing Metrics: Track metrics like MTTR (Mean Time to Recovery) and test flakiness to ensure a high quality testing system.

☑️ 3.8 Tooling: Use testing frameworks (e.g., Terratest, Inspec, pytest).

2. End-to-End Testing

☑️ 2.1 Integration Testing: Verify API connectivity, service discovery, and dependencies between services.

• Example: Use Postman or similar tools to validate API-to-database flows.

☑️ 2.2 Infrastructure Testing: Use tools like Test Kitchen or Packer to test infrastructure setups.

• Example: Use Kitchen-Terraform to validate Terraform configurations before applying them in production.

☑️ 2.3 Staging Environments: Validate deployments in staging environments that closely mimic production.

☑️ 2.4 Dynamic Test Environments: Use dynamic test environments with tools like Docker Compose or Kubernetes Namespaces for isolated testing.

☑️ 2.5 User Flows: Test user flows by simulating actions of real end users.

3. Load & Stress Testing

☑️ 3.1 Performance Testing: Use load testing tools (e.g., k6, JMeter, Locust) to test application performance under normal and peak loads.

• Alternative tools: Mention alternatives like Artillery or Locust for distributed load testing.

☑️ 3.2 Production-like Environments: Test in production-like environments to gather accurate performance metrics and identify bottlenecks.

☑️ 3.3 Soak Testing: Perform soak testing to check for memory leaks and resource stability during prolonged use.

☑️ 3.4 Infrastructure Testing: Use infrastructure testing tools (e.g., Terraform Compliance, Checkov) to validate configurations and infrastructure quality.

☑️ 3.5 Chaos Engineering: Incorporate chaos engineering in testing strategies, creating random failures (e.g., random resource unavailability) to assess resiliency of the system.

• Example: Simulate node failures with Gremlin while monitoring SLIs, and integrating monitoring tools like Prometheus to track metrics.

☑️ 3.6 Distributed Testing: Implement distributed load testing using tools like k6 Cloud or Artillery to simulate load from multiple locations.

☑️ 3.7 Test Flakiness Reduction: Develop a strategy for identifying flaky tests using test retry analysis and flaky test dashboards in CI tools like Jenkins or GitHub Actions.

☑️ 3.8 Test Orchestration: Utilize tools like Testcontainers for managing test dependencies in Java or Node.js applications.

• Key Takeaways for Testing:
  • Implement a robust testing strategy by using unit, integration, and load testing.
  • Use chaos engineering to test resiliency.
  • Track metrics like MTTR and reduce test flakiness.

Security

☑️ 4.1 Shift Security Left: Integrate security scanning into the CI/CD pipeline, ensuring security checks are part of the development process.

• Example: Integrate Trivy, Snyk or similar tools to scan Docker images for vulnerabilities during builds.

☑️ 4.2 Vulnerability Scanning: Regularly scan containers and images for vulnerabilities using tools like Trivy, Aqua Security, or Snyk.

☑️ 4.3 Third-Party Dependencies: Scan third-party dependencies using tools like OWASP Dependency-Check to identify vulnerabilities.

• Example: Integrate tools like OWASP Dependency-Check to identify vulnerabilities in third-party libraries in your Java applications.

☑️ 4.4 IaC Security: Use IaC security policies to prevent misconfigurations (e.g., Checkov, Terraform Sentinel, OPA).

• Policy-as-Code: Implement tools like OPA (Open Policy Agent) for automating IaC security and compliance checks.
•  Example OPA Policy: Restrict S3 buckets to block public access:

package s3_policies

deny[msg] {
  input.bucket.public_access == true
  msg = "Public access is prohibited for all S3 buckets."
}

☑️ 4.6 Access Control: Audit permissions regularly to ensure least privilege access, using Role-Based Access Control (RBAC) where appropriate.

• Example of using Granular IAM Policies:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject"
      ],
      "Resource": [
        "arn:aws:s3:::example-bucket/*"
      ]
    }
  ]
}

☑️ 4.7 Secrets Management: Automate secrets management using Vault, AWS Secrets Manager, or Azure Key Vault, ensuring secrets are never stored in code or configuration files. Implement secrets rotation.

☑️ 4.8 Dynamic Secrets: Generate secrets dynamically using tools like Vault Transit Engine or AWS Parameter Store, ensuring they are not stored in static locations.

☑️ 4.9 Securing CI/CD Pipelines: Secure CI/CD secrets using tools like HashiCorp Vault or GitHub Secrets to avoid exposure.

☑️ 4.10 Compromised Secret Detection: Regularly check for compromised secrets using secret scanning tools.

☑️ 4.11 Network Security: Implement Network Security Groups (NSG) to isolate infrastructure services.

☑️ 4.12 API Security: Implement API security measures to protect backend access.
☑️ 4.13 Supply Chain Security: Use tools like sigstore/cosign for signing container images to ensure integrity and prevent tampering.
☑️ 4.14 Automated Incident Response: Include examples of automated remediation for detected vulnerabilities.

☑️ 4.15 Compliance Examples: Use OPA Gatekeeper to enforce compliance with GDPR by blocking deployments with missing data residency annotations.

• Key Takeaways for Security:
  • Prioritize security by shifting security left, using automated scans, and implementing least privilege principles.
  • Secure secrets, and apply multi-cloud policies for consistent protection.

Performance Optimization

☑️ 5.1 CDN Usage: Use a Content Delivery Network (CDN) (e.g., Cloudflare, Akamai, AWS CloudFront) for faster static content delivery and reduced latency.

• Example: Use Cache-Control headers for HTTP responses to enable efficient browser caching.

☑️ 5.2 Database Optimization: Optimize database performance with indexing, connection pooling, and query optimization.

• Example of Detecting Slow Queries: Enable MySQL slow query logs:

SET GLOBAL slow_query_log = 'ON';
SET GLOBAL long_query_time = 2;

☑️ 5.3 Caching Strategies: Implement caching at multiple layers (e.g., browser, CDN, application, database) using tools like Redis, Cloudflare, or memcached.

• Example: Use Redis to cache frequently accessed data and reduce the load on the database.

☑️ 5.4 Network Analysis: Analyze network traffic for potential bottlenecks using tools like Wireshark, tcpdump, or Netdata.☑️ 5.5 Resource Utilization: Analyze CPU and memory utilization at the service level to identify areas for optimization.

☑️ 5.6 Data Compression: Use data compression techniques to reduce bandwidth consumption and improve data transfer speed.

☑️ 5.7 Real-Time Profiling: Use tools like Datadog Profiler or Pyroscope to capture live performance bottlenecks in real time.

☑️ 5.8 Granular Profiling: Profile at function or API endpoint levels to pinpoint performance bottlenecks.

☑️ 5.9 Cost-Performance Trade-offs: Optimize performance while considering cost impacts (e.g., using Graviton instances on AWS for better price-performance).

☑️ 5.10 Auto-Scaling with Predictive Models: Implement predictive auto-scaling based on historical patterns using tools like Kubernetes Vertical Pod Autoscaler (VPA) and AWS Auto Scaling Predictive Scaling.

• Horizontal Scaling: Offers better fault tolerance but may require architectural redesigns.
• Vertical Scaling: Is simpler but has hardware limitations.

☑️ 5.11 Latency Buckets: Use Histogram metrics for monitoring API latency distributions in detail.

• Key Takeaways for Performance Optimization:
  • Implement caching at multiple layers, analyze network traffic, and optimize database queries.
  • Use profiling tools to identify performance bottlenecks.
  • Balance horizontal and vertical scaling trade-offs.
  • Use predictive auto-scaling to optimize performance.

Cost Management

1. Cost Optimization Practices

☑️ 1.1 Cloud Cost Tools: Use tools like AWS Cost Explorer, Azure Cost Management, or GCP Billing to track and analyze cloud spending.

☑️ 1.2 FinOps Principles: Implement FinOps principles to align engineering and financial strategies, ensuring cost-aware decisions are made at all stages of development and operations.

• FinOps in Action:
• For example, a company reduced costs by using spot instances for non-critical workloads, tagging resources for better visibility, and reserving instances for predictable loads.

☑️ 1.3 Commitment-Based Discounts: Leverage commitment-based discounts (e.g., reserved instances, savings plans) to reduce overall costs.

☑️ 1.4 Reserved Instances Comparison: Evaluate reserved instance discounts across multiple providers (e.g., AWS, Azure, GCP) for cost savings.

☑️ 1.5 Regular Cost Reviews: Conduct regular cost reviews to identify and eliminate waste (e.g., unused resources, inefficient spending patterns).

☑️ 1.6 Spot Instances: Use spot instances or preemptible VMs for non-critical workloads to save up to 90%.

☑️ 1.7 Cost-Aware Autoscaling: Implement cost-aware autoscaling policies that dynamically adjust resources based on traffic and cost considerations.

☑️ 1.8 Idle Resource Cleanup: Automate the identification and deletion of idle or unused resources with tools like Lambda functions, Azure Automation, or GCP Cloud Functions.

• Example: Use Azure Automation to identify and delete unused disk snapshots.
• Example: Use GCP Recommender for identifying unused Compute Engine VMs.
• Example: Use Azure Advisor to optimize underutilized virtual machines.

☑️ 1.9 Idle Resource Tracking Example: Identify unused cloud assets using tools like Cloud Custodian for cost optimization.

☑️ 1.10 Dynamic Resource Scheduling: Use the Kubernetes Cluster Autoscaler to dynamically scale nodes based on workload demand and reduce cost.

2. Tagging and Tracking

☑️ 2.1 Automated Tagging: Implement automated tagging policies using governance tools (e.g., AWS Tag Editor, Azure Policy, GCP Resource Manager) to ensure resources are consistently tagged.

• Example: Automatically add Environment=Production tags to all production resources.

☑️ 2.2 Cost Allocation: Use tagging to track cost allocation across teams, projects, or environments, enabling cost analysis and accountability.

☑️ 2.3 Tagging Enforcement: Enforce a predefined tagging strategy to ensure consistency and compliance across all resources.

☑️ 2.4 Cost Alerts: Set up budget alerts using tools like AWS Budgets or Google Cloud Budgets to notify stakeholders when spending exceeds defined thresholds.

☑️ 2.5 Cost Optimization Reports: Generate monthly cost allocation reports for stakeholders using tools like AWS Budgets or Azure Cost Management, allowing for transparency.

• Key Takeaways for Cost Management:
  • Use cloud cost tools, implement FinOps principles and automate idle resource cleanup.
  • Leverage discounts (RI, spot instances), and enforce resource tagging.

Documentation

☑️ 7.1 Living Documentation: Ensure runbooks are living documents, updated to reflect the latest changes in infrastructure and processes. Include incident details in runbooks for better troubleshooting.

☑️ 7.2 User-Friendly Language: Use simple, user-friendly language and terminology in all documentation to ensure broader accessibility and understanding across teams.

☑️ 7.3 Technical Writing Tips:

• Use action verbs for procedural instructions.
• Use consistent formatting with tools like Vale.

☑️ 7.4 Automated Documentation: Automate documentation updates with CI/CD pipelines to ensure documentation remains consistent with the code and configurations.

☑️ 7.5 Diagrams: Emphasize using diagrams to complement written documentation, providing visual context and enhancing comprehension.

☑️ 7.6 Centralized Documentation: Store documentation in the same repository as the code and make documentation accessible in the development workflow by storing it in a central portal with a search feature.

☑️ 7.7 Multi-Audience Documentation: Create documentation tailored for different audiences:

• Engineer Audience: Focus on technical specifics like input parameters for IaC modules.
• Manager Audience: Highlight SLAs, system capabilities, and business benefits.

☑️ 7.8 Visual Workflow Tools: Document CI/CD pipelines visually using Mermaid.js within Markdown files to provide a clear overview.

☑️ 7.9 Interactive Documentation: Use tools like Swagger or Postman Collections for API documentation to allow for interactive testing and usage.

☑️ 7.10 Real-Time Updates: Use tools like GitBook to auto-sync Markdown-based documentation updates in real-time.

☑️ 7.11 API Versioning: Include examples of documenting API versioning strategies:

Example:

GET /api/v1/users
POST /api/v1/orders

☑️ 7.12 Interactive Playbooks: Create interactive playbooks with tools like RunDeck to standardize operational procedures.

☑️ 7.13 Version Control for Docs: Version-control all documentation using Git, ensuring traceability and collaborative updates.

Example Template: Adopt Markdown templates for consistency in CHANGELOGs:

## [v1.1.0] - 2025-01-01

### Features
- Added new feature X

### Bug Fixes
- Fixed bug Y

• Key Takeaways for Documentation:
  • Maintain clear, concise, and living documentation with diagrams and interactive playbooks.
  • Version control all documentation and use Markdown templates.

Scalability and Reusability

☑️ 8.1 Horizontal and Vertical Scaling: Design infrastructure for both horizontal and vertical scaling, using auto-scaling groups and dynamic resource allocation—be ready for growth in all directions.

☑️ 8.2 Horizontal Scaling vs. Vertical Scaling:

• Horizontal scaling offers better fault tolerance but may require architectural redesigns—let’s understand the pros and cons.
• Vertical scaling is simpler but has hardware limitations—we should acknowledge the limits.

☑️ 8.3 Auto-Scaling Policies: Set dynamic autoscaling policies based on workload metrics to ensure resources are adjusted automatically—let’s automate the scaling process.

☑️ 8.4 Identify Bottlenecks: Identify and resolve bottlenecks before scaling infrastructure—we need to know where we are getting stuck before scaling.

☑️ 8.5 Reusable IaC Modules: Create shared repositories for reusable IaC modules (e.g., Terraform modules, Pulumi components), making it easier to share and reuse common infrastructure patterns across projects— we should build once and use many times.

• Example: Use a common Terraform module for creating VPCs across all projects.
• Use Terraform modules from the Terraform Registry—let’s leverage the community.

☑️ 8.6 Cloud Native Autoscaling: Leverage the cloud provider’s native autoscaling capabilities for cost efficiency and performance – it simplifies the autoscaling.

☑️ 8.7 Reusable CI/CD Pipelines: Build reusable CI/CD pipeline templates for consistent and efficient deployments—let’s make the deployment process easy.

• Use pre-built modules and reusable code components from a shared repository—we should use pre-built parts as much as possible.

☑️ 8.8 Pre-Built CI/CD Modules: Integrate pre-built CI/CD modules from tools like Spacelift or Terraform Cloud for quicker setups—saves time and effort.

☑️ 8.9 API Rate Limits: Implement API rate limits to control usage and improve stability—let’s keep our APIs safe and available.

• Example: Use AWS Lambda with an API Gateway Usage Plan to enforce limits dynamically.

Example: Restrict each API client to 1000 requests per minute.

{
  "UsagePlan": {
    "name": "basic-usage-plan",
    "description": "basic usage plan",
    "apiStages": [
      {
        "apiId": "your-api-id",
        "stage": "prod"
      }
    ],
    "throttle": {
      "burstLimit": 100,
      "rateLimit": 100
    },
    "quota": {
      "limit": 1000,
      "period": "MINUTE"
    }
  }
}

☑️ 8.10 Scaling Databases: Scale relational databases using read replicas and partitioning strategies to improve read and write performance – let’s make sure our databases can handle the load.

☑️ 8.11 Event-Driven Scaling: Implement event-driven scaling for microservices using serverless technologies (e.g., AWS Lambda or Azure Functions):—it’s a good way to scale based on events.

• Example: Automatically scale storage based on S3 events or database triggers.

• Key Takeaways for Scalability and Reusability:
  • Design for horizontal and vertical scaling and use reusable IaC modules.
  • Implement API rate limiting and adopt event-driven scaling for microservices
  • Use a shared repository for pre-built CI/CD modules.