Essential DevOps Rules

DevOps engineering is one of those fields where theory meets brutal reality at 3 AM when your production systems decide to have an existential crisis. After years of being woken up by alerts, debugging cryptic error messages, and learning from spectacular failures, the DevOps community has distilled some fundamental truths that separate the seasoned professionals from those still figuring things out.

Here are 35 battle-tested rules that will accelerate your journey from DevOps beginner to someone who can sleep peacefully at night (most of the time).

The Automation & Monitoring Commandments

• Focus on the things that get you paged at 3 AM - those are the processes you should automate first

Why this matters: Your sleep schedule is the ultimate priority queue for automation tasks. If something is important enough to wake you up in the middle of the night, it’s important enough to automate properly.

Real-world application: Start by listing every incident that has pulled you out of bed in the past six months. Database connection failures? Disk space alerts? Failed deployments? These aren’t just inconveniences—they’re your roadmap to better automation.

Pro tip: Create a “3 AM automation backlog” and tackle the most frequent offenders first. Your future self (and your family) will thank you.

• Stop monitoring every CPU spike. Focus on what impacts your users and revenue

Why this matters: Monitoring everything is like having a car alarm that goes off every time someone walks by—eventually, you stop paying attention to the important alerts.

The principle: Monitor outcomes, not just outputs. A CPU spike that doesn’t affect user experience or business metrics is just noise. Focus on:

User-facing errors and response times
Revenue-impacting system failures
Customer experience metrics
Business-critical process completions

Implementation strategy: Start with your service level objectives (SLOs) and work backward. What metrics actually matter to your users and business? Build your monitoring around those, not around every system resource you can measure.

• An alert should require immediate action; otherwise, it should be a log or dashboard metric

Why this rule exists: Alert fatigue is real and dangerous. When everything is urgent, nothing is urgent.

The litmus test: Before creating any alert, ask yourself: “If this fired at 2 AM, would I need to get out of bed to fix it?” If the answer is no, it belongs in a dashboard or log, not in your alert system.

Best practices:

Critical alerts: Immediate action required (system down, data loss)
Warning alerts: Action needed within business hours
Info notifications: Dashboard metrics and trend analysis
Debug logs: Everything else

The Failure & Recovery Philosophy

• Your systems will break. Plan for failure, not perfection

The harsh reality: It’s not a matter of if your systems will fail, but when and how spectacularly. Embracing this mindset shifts your focus from preventing all failures (impossible) to handling them gracefully (achievable).

Design principles:

Assume every component will fail at the worst possible moment
Build redundancy at every critical layer
Design for graceful degradation rather than catastrophic failure
Create systems that fail fast and recover quickly

Cultural shift: Stop asking “How do we prevent this from failing?” and start asking “When this fails, how do we minimize impact and recover quickly?”

• Before you hit deploy, know exactly how to roll back. Test it too - a broken rollback plan is worse than no plan

The deployment paradox: The confidence to move forward comes from knowing you can move backward quickly.

Essential rollback requirements:

Document the exact rollback procedure before deployment
Test rollback procedures in staging environments
Automate rollback processes where possible
Set maximum rollback time limits (if you can’t roll back in X minutes, you need to fix forward)
Have a communication plan for rollback scenarios

Real-world example: Blue-green deployments, feature flags, and database migration strategies that work in both directions are your friends here.

• Run good post-mortems that focus on improving systems, not blaming people. Pointing fingers is fun until they point back at you

The post-mortem philosophy: Every failure is a learning opportunity disguised as a crisis.

Effective post-mortem structure:

Timeline of events (facts only, no blame)
Root cause analysis (usually multiple contributing factors)
System improvements to prevent similar issues
Process improvements to catch issues earlier
Action items with owners and deadlines

Cultural considerations: Create a blameless culture where people feel safe to report problems and mistakes. The goal is system improvement, not punishment.

The Security & Best Practices Doctrine

• Never store secrets in code repositories, even private ones

Why this is non-negotiable: Code repositories have a way of becoming less private over time. Developers change companies, repositories get forked, and backups exist in places you’ve forgotten about.

Secret management best practices:

Use dedicated secret management tools (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault)
Environment variables for containerized applications
Rotate secrets regularly and automatically
Audit secret access and usage
Never commit secrets, even in private repositories

The “even private repos” rule: Today’s private repository is tomorrow’s open-source project or security audit discovery.

• Complex security is bad security. If your security makes things harder, people will find ways around it

The usability principle: Security that’s difficult to use correctly will be used incorrectly or bypassed entirely.

Design principles:

Make secure practices the easiest option
Automate security wherever possible
Provide clear documentation and training
Regular security reviews and improvements
Balance security with developer productivity

Real-world impact: Overly complex authentication systems lead to password sharing. Difficult deployment processes lead to direct production access. Hard-to-use security tools lead to shadow IT solutions.

The Data & Backup Commandments

• Everyone says they have backups. The real question is: when did you last test them?

The backup reality check: Untested backups are just wishful thinking with storage costs.

Backup testing strategy:

Regular restore tests in isolated environments
Document and time your restore procedures
Test partial restores, not just full system restores
Verify data integrity after restoration
Test restores under time pressure (simulated emergency conditions)

The 3-2-1 rule: 3 copies of important data, on 2 different media types, with 1 copy offsite.

• Back up everything regularly, but also test your restore process

Beyond just having backups: The ability to restore quickly and correctly is often more important than the backup itself.

Comprehensive backup strategy:

Automated, regular backups of all critical data
Multiple restore points (hourly, daily, weekly, monthly)
Geographically distributed backup storage
Documented restore procedures for different scenarios
Regular disaster recovery drills

Testing scenarios: Complete system failure, partial data corruption, accidental deletion, and time-critical recovery situations.

The Development & Deployment Wisdom

• Treat Infrastructure Like Software. Test it, version it. DRY it.

Infrastructure as Code (IaC) principles: Your infrastructure should be as well-managed as your application code.

Best practices:

Version control all infrastructure definitions
Use automated testing for infrastructure changes
Apply DRY (Don’t Repeat Yourself) principles to infrastructure code
Code reviews for infrastructure changes
Automated deployment pipelines for infrastructure

Tools and approaches: Terraform, CloudFormation, Ansible, and other IaC tools should be treated with the same rigor as application development.

• Make small, frequent changes instead of large, infrequent ones

The deployment philosophy: Small changes are easier to test, deploy, and rollback. Large changes are exponentially more risky.

Implementation strategy:

Break large features into smaller, deployable chunks
Use feature flags to decouple deployment from release
Implement continuous integration and deployment
Maintain backward compatibility during transitions
Monitor and validate each small change before the next

Risk reduction: Small changes mean smaller blast radius when things go wrong.

• Build a proper test environment before touching production

The testing hierarchy: Development → Staging → Production, with proper data and configuration management at each level.

Test environment requirements:

Mirror production architecture as closely as possible
Use production-like data (anonymized/sanitized)
Implement the same monitoring and alerting
Test deployment and rollback procedures
Load testing and performance validation

Common pitfalls: “It works on my machine” syndrome, configuration drift between environments, and insufficient test data.

• Design systems assuming components will fail

Resilience by design: Build systems that continue operating even when individual components fail.

Design patterns:

Circuit breakers for external service calls
Retry logic with exponential backoff
Graceful degradation when services are unavailable
Health checks and automatic failover
Redundancy at every critical layer

Chaos engineering: Intentionally introduce failures to test system resilience.

The Knowledge & Learning Philosophy

• That error you’re seeing? Someone’s already solved it on Stack Overflow or GitHub issues

The debugging shortcut: Most problems you encounter have been solved by someone else. Learning to search effectively is a crucial skill.

Effective search strategies:

Use specific error messages and codes
Include relevant technology stack information
Check official documentation and GitHub issues
Look for recent solutions (technology changes quickly)
Understand the solution, don’t just copy-paste

Building knowledge: Keep a personal knowledge base of solutions to problems you’ve encountered.

• Learn to read logs like a detective. The answer is usually buried in there somewhere

Log analysis skills: Logs are the primary source of truth for system behavior and problems.

Log reading techniques:

Understand log levels and their meanings
Use log aggregation tools (ELK stack, Splunk, etc.)
Search for patterns, not just specific errors
Correlate logs across different systems and time periods
Understand normal vs. abnormal log patterns

Log management: Proper log collection, storage, and analysis infrastructure is essential.

• Teaching Others Accelerates Your Learning

The teaching principle: Explaining concepts to others forces you to understand them more deeply.

Benefits of teaching:

Identifies gaps in your own knowledge
Reinforces your understanding through repetition
Builds your professional reputation and network
Contributes to team knowledge and capabilities
Develops communication and leadership skills

Opportunities: Internal documentation, team presentations, mentoring, blog posts, and conference talks.

• Learn to Explain Technical Concepts to Non-Technical People

The communication bridge: DevOps engineers often need to communicate with business stakeholders who don’t have technical backgrounds.

Communication techniques:

Use analogies and metaphors
Focus on business impact, not technical details
Avoid jargon and acronyms
Use visual aids and diagrams
Provide concrete examples and scenarios

Business alignment: Understanding how technical decisions impact business outcomes is crucial for career advancement.

The Productivity & Professional Development Rules

• Context Switching Is the Productivity Killer

The focus principle: Constant interruptions and task switching dramatically reduce productivity and increase error rates.

Strategies for minimizing context switching:

Batch similar tasks together
Set dedicated time blocks for deep work
Use tools to manage interruptions (Slack status, calendar blocks)
Prioritize ruthlessly
Delegate or automate routine tasks

Team coordination: Establish team agreements about communication and interruption protocols.

• Physical Health Affects Technical Performance

The holistic approach: Your physical and mental health directly impact your ability to solve technical problems and make good decisions.

Health considerations:

Regular exercise and movement
Adequate sleep (especially important for on-call rotations)
Proper nutrition and hydration
Stress management techniques
Work-life balance

On-call health: Irregular sleep schedules and high-stress incidents can seriously impact health and performance.

• Build Your Professional Network Before You Need It

The networking principle: Professional relationships are most valuable when they’re not transactional.

Network building strategies:

Attend industry conferences and meetups
Contribute to open source projects
Participate in online communities
Share knowledge through writing and speaking
Maintain relationships with former colleagues

Long-term thinking: Invest in relationships when you don’t need anything, so they’re available when you do.

The Pragmatic Engineering Principles

• New tools are shiny, but boring tech pays the bills. Don’t rewrite your stable systems just because Docker has a new feature

The stability principle: Mature, proven technologies are often better choices than cutting-edge alternatives for critical systems.

Technology selection criteria:

Proven track record in production environments
Strong community support and documentation
Long-term maintenance and support commitments
Team expertise and learning curve
Business requirements and constraints

Innovation balance: Use new technologies for non-critical systems first, then gradually introduce them to more important systems.

• Don’t try to reinvent the wheel. Take what is already there and build on top of it

The efficiency principle: Standing on the shoulders of giants is faster and more reliable than building everything from scratch.

Implementation strategies:

Use established frameworks and libraries
Leverage cloud services for non-differentiating functionality
Adopt industry standard practices and patterns
Contribute to existing open source projects rather than creating new ones
Focus innovation on your unique business value

When to build vs. buy: Build when it’s your competitive advantage, buy/use when it’s commodity functionality.

• ‘But It Works on My Machine’ will not get you anywhere

The consistency principle: Development, testing, and production environments must be as similar as possible.

Solutions for environment consistency:

Containerization (Docker, etc.)
Infrastructure as Code
Standardized development environments
Automated environment provisioning
Configuration management tools

Cultural shift: Move from “works on my machine” to “works in all environments.”

The Documentation & Communication Essentials

• Version control isn’t just for code. Put your scripts, configs, and docs in git - you’ll need them when things break

The everything-in-git principle: If it’s important enough to create, it’s important enough to version control.

What belongs in version control:

Infrastructure as Code definitions
Configuration files and templates
Deployment and maintenance scripts
Documentation and runbooks
Database migration scripts
Monitoring and alerting configurations

Benefits: Change tracking, collaboration, rollback capabilities, and historical analysis.

• Write things down. Your 3 AM self won’t remember what your 3 PM self was thinking

The documentation imperative: Clear, up-to-date documentation is essential for system maintenance and knowledge transfer.

Documentation best practices:

Write runbooks for common procedures
Document troubleshooting steps
Keep architecture diagrams current
Record decision-making rationale
Maintain change logs and release notes

The 3 AM test: If you can’t follow your own documentation at 3 AM while half-asleep, it needs improvement.

Conclusion: The DevOps Mindset

These 25 rules represent hard-earned wisdom from the DevOps community. They’re not just technical guidelines—they’re a philosophy for building reliable, maintainable systems while preserving your sanity and career longevity.

The common threads running through all these rules are:

Pragmatism over perfection: Focus on what works reliably
Preparation over reaction: Plan for failure and have tested recovery procedures
Communication over isolation: Share knowledge and build relationships
Automation over manual processes: Reduce human error and improve consistency
Learning over knowing: Stay curious and adapt to new challenges

Remember, becoming a great DevOps engineer isn’t just about mastering tools and technologies—it’s about developing the judgment to make good decisions under pressure, the discipline to follow best practices even when they’re inconvenient, and the wisdom to know when to break the rules.

Your journey in DevOps will be filled with 3 AM wake-up calls, mysterious system failures, and moments of brilliant problem-solving. These rules won’t prevent all the challenges, but they’ll help you handle them with confidence and professionalism.

The best DevOps engineers aren’t the ones who never encounter problems—they’re the ones who solve problems efficiently, learn from failures, and build systems that fail gracefully. Follow these principles, and you’ll be well on your way to joining their ranks.

What rules would you add to this list? Share your hard-earned DevOps wisdom in the comments below.

Additional Hard-Learned DevOps Wisdom

• Observability is not just monitoring - it’s about understanding why your system behaves the way it does

The observability difference: Monitoring tells you what’s happening, observability tells you why it’s happening.

The three pillars of observability:

Metrics: What happened and when
Logs: Detailed context about events
Traces: How requests flow through your system

Implementation strategies:

Implement distributed tracing for microservices
Use structured logging with consistent formats
Create custom metrics for business logic
Build dashboards that tell a story, not just display data
Set up synthetic monitoring to catch issues before users do

Pro tip: If you can’t quickly answer “why is this happening?” when looking at an alert, you need better observability.

• Your staging environment will never be exactly like production, and that’s okay - just make it close enough to catch the big problems

The staging reality: Perfect staging environments are expensive and often impossible. Focus on catching the issues that matter most.

Staging environment priorities:

Same infrastructure patterns and scaling constraints
Production-like data volumes and types
Realistic network latency and connectivity
Similar security configurations
Proper load testing capabilities

What you can live with being different:

Exact data replication (use anonymized subsets)
Full geographic distribution
Complete third-party integrations
Exact scaling ratios

The 80/20 rule: Catch 80% of production issues with 20% of production’s complexity.

• Always have a “break glass” procedure for emergency access, but make sure it’s audited and time-limited

Emergency access philosophy: Plan for the scenario where your normal access methods fail during a critical incident.

Break glass requirements:

Documented emergency access procedures
Time-limited credentials (automatically expire)
Full audit logging of emergency access usage
Multiple approval requirements for activation
Regular testing of emergency procedures

Common scenarios requiring break glass:

Identity provider failures
Network connectivity issues
Automation system failures
Mass credential compromises

Post-incident requirements: Every break glass usage should trigger a post-mortem to understand why normal access failed.

• Performance problems are usually N+1 queries, memory leaks, or poor caching - start with the boring stuff before looking for exotic solutions

The performance debugging hierarchy: Most performance issues are caused by common, well-understood problems.

The usual suspects (in order of likelihood):

Database N+1 query problems
Memory leaks and garbage collection issues
Poor or missing caching strategies
Unoptimized database queries
Resource contention and locking
Network latency and timeout issues

Debugging methodology:

Start with application profiling and database query analysis
Check memory usage patterns and garbage collection metrics
Analyze cache hit rates and invalidation patterns
Look at resource utilization during peak loads
Only then consider exotic solutions or architectural changes

The exotic solution trap: Don’t immediately jump to microservices, caching layers, or architectural rewrites when the issue might be a missing database index.

• Your infrastructure should be cattle, not pets - if you can’t delete it and recreate it, you’re doing it wrong

The cattle vs. pets philosophy: Treat servers and infrastructure components as replaceable rather than unique and precious.

Cattle characteristics:

Automatically provisioned and configured
Identical and interchangeable
Easily replaced when they fail
No manual configuration or customization
Stateless or with externalized state

Pet elimination strategies:

Use Infrastructure as Code for all provisioning
Implement immutable infrastructure patterns
Externalize all persistent state
Automate configuration management
Practice regular infrastructure refresh cycles

Signs you have pets: Servers with names, manual configuration, “it works, don’t touch it” mentality, or fear of rebooting systems.

• Implement circuit breakers for all external dependencies - your system’s reliability can’t be worse than your weakest dependency

The circuit breaker pattern: Protect your system from cascading failures caused by unreliable external services.

Circuit breaker implementation:

Detect when external services are failing
Stop making requests to failing services temporarily
Allow occasional test requests to check if service has recovered
Implement fallback behavior when services are unavailable
Monitor and alert on circuit breaker state changes

Dependency reliability math: If you depend on three services that are each 99% reliable, your maximum reliability is 99% × 99% × 99% = 97%.

Fallback strategies:

Cached responses for read operations
Graceful degradation of functionality
Default values or simplified responses
Queue requests for later processing

• Log everything, but make sure your logs are searchable and structured - unstructured logs are just expensive noise

Structured logging principles: Logs should be machine-readable and consistently formatted.

Structured logging requirements:

Use consistent log formats (JSON is preferred)
Include correlation IDs for request tracing
Log at appropriate levels (DEBUG, INFO, WARN, ERROR)
Include relevant context (user ID, request ID, business context)
Avoid logging sensitive information

Log aggregation strategy:

Centralized log collection and storage
Powerful search and filtering capabilities
Log retention policies based on importance
Automated log analysis and anomaly detection
Integration with alerting systems

The searchability test: If you can’t quickly find relevant logs during an incident, your logging strategy needs improvement.

• Automate your compliance and security scanning - manual security reviews are too slow and inconsistent

Security automation philosophy: Security should be built into your pipeline, not bolted on afterward.

Automated security practices:

Static code analysis in CI/CD pipelines
Dependency vulnerability scanning
Container image security scanning
Infrastructure compliance checking
Automated penetration testing
Secret detection in code repositories

Compliance automation:

Policy as Code implementation
Automated compliance reporting
Continuous audit trails
Automated remediation for common issues
Regular compliance validation

The shift-left approach: Find and fix security issues as early in the development process as possible.

• Your on-call rotation should be sustainable - burned out engineers make bad decisions and more mistakes

On-call sustainability principles: Protecting your team’s well-being protects your system’s reliability.

Sustainable on-call practices:

Reasonable rotation schedules (1 week maximum)
Clear escalation procedures
Comprehensive runbooks and documentation
Post-incident recovery time
On-call compensation and time off

Burnout prevention:

Limit after-hours pages to true emergencies
Invest in system reliability to reduce incidents
Provide mental health resources and support
Regular on-call feedback and improvement sessions
Clear boundaries between on-call and regular work

The feedback loop: Use on-call experiences to prioritize system improvements and automation.

• Always have a rollback strategy, but also have a “fix forward” strategy - sometimes rolling back makes things worse

Deployment strategy diversity: Different types of problems require different response strategies.

When to roll back:

New deployment introduces critical bugs
Performance regressions are unacceptable
Security vulnerabilities are introduced
Data integrity is at risk

When to fix forward:

Database schema changes that can’t be reversed
Data migrations that have already completed
Dependencies that other systems rely on
Time-sensitive business requirements

Hybrid approaches:

Feature flags to disable problematic functionality
Blue-green deployments with traffic shifting
Canary releases with automatic rollback triggers
Database migration strategies that work in both directions

Cheers,

Sim