Robust process governance and DevOps automation were critical enablers for our successful Azure transformation. We couldn't simply lift-and-shift our existing development and operations behaviors to the cloud. Unleashing cloud's true potential required modernizing our processes and fostering a collaborative, iterative DevOps culture.
From a governance standpoint, we started by clearly defining our operating model "guardrails" through the Azure Cloud Adoption Framework. We established rigid policies and defined processes around:
Resource Provisioning and Configuration
Azure Blueprints to enforce resource consistency
Polices for approved resource types, naming, tagging
Infrastructure-as-code through ARM, Bicep, and Terraform
Automated provisioning with config management tools
Security Baseline and Compliance
Governance constructs like management groups
Policies for encryption, keyvault access, networking
Automating security benchmarks and compliance
Centralized identity and access management
Cost Management and Accountability
Tagging enforcement policies for cost views
Budgets, quotas and subscription governance
Consumption monitoring and forecasting processes
Showback/chargeback allocations to business units
Release and Deployment Processes
Environment segregation (DTAP) models
Approvals and gating for progressive exposure
Automated CICD pipelines and workflows
Repo and artifact governance, branch policies
With these security, cost, and deployment guardrails codified, it allowed self-service within an established framework - avoiding cloud "wild west" scenarios.
To operationalize a true DevOps way of working, we decomposed our legacy operations and engineering silos into blended product teams. Each team now owned the end-to-end delivery and operational support for their suite of cloud services.
Developers were now responsible for:
Configuring CI/CD pipelines
Automated testing and validating quality gates
Containerizing applications for immutable deploys
Monitoring and operational telemetry
While operations cloud engineers focused on:
Provisioning and securing cloud environments
Defining and automating deployment workflows
Incident response and chaos engineering
Observability pipelines and SRE practices
New cloud platform teams provided self-service provisioning and governance controls. Central enablement resources upskilled teams on DevOps automation and modern practices.
We tied it all together using Azure DevOps for version control, build/release management, and work tracking. Standardizing on DevOps toolchains like Terraform, Ansible, Docker, Prometheus and more.
Everything from application code to infrastructure configs were now versionable, repeatable, and subject to checks and testing. All deployments became automated model-driven workflows triggered from repos - no more manual "human" intervention.
Each deployable service was wrapped in telemetry. Immutable container deploys reduced whack-a-mole issues. Canary and progressive delivery patterns enabled gradual rollouts and automatic rollbacks based on validations and quality gates.
Our SRE playbooks and development rigor improved drastically through practices like:
Game days and chaos engineering
Blameless post-incident reviews
Automated provisioning and config enforcement
Continuous integration of security, cost, and compliance requirements
What was once a rigid, serialized process transformed into an agile, iterative DevOps model of continuous integration, continuous delivery, and continuous improvement. We tore down the walls between our former dev and ops groups to create unified, full-cycle product teams.
It required relentless upskilling, tool investments, and a philosophical shift in our approach. But our embrace of DevOps automation unlocked exponential gains in velocity and quality. Combined with our cloud-native architectural transformation, we re-invented how we build and run software.
Azure SRE Playbook for our optimized Azure cloud operating model:
SRE Philosophy & Culture
· Maximize service reliability and scale through automation
· Balance between development and operations mindsets
· Risk taking, learning from failures via blameless postmortems
· Chaos engineering to proactively find weaknesses
Service Ownership Model
· Product teams own end-to-end service delivery and support
· Embedded SRE representatives within each product team
· SRE enablement hub provides training and mentorship
Reliability Targets
· Error budget policies (e.g. 99.95% availability)
· SLO and SLI definitions per service
· Reliability treated as first-class requirement
Automated Monitoring & Observability
· Azure Monitor telemetry pipelines
· Distributed tracing with App Insights
· AIOps correlation and smart analytics
· Self-service dashboards and runbooks
Incident Response
· Alerting and on-call rotations
· Documented response playbooks
· War rooms and real-time collaboration
· Post-incident review process
Chaos Engineering & Game Days
· Scheduled game day exercises
· Applicative fault injection testing
· Chaos Mesh / Chaos Studio tooling
· Proactive capacity testing at scale
Automated Remediation & Self-Healing
· Configured auto-mitigation policies
· Self-healing cloud templates and runbooks
· Integration with Azure Logic Apps & Functions
Capacity Planning & Cost Optimization
· Application consumption forecasting models
· Container rightsizing and HPA policies
· Scheduled cost attribution analysis
· FinOps processes for spend accountability
Security and Compliance Automations
· Governance policies as code
· OSS vulnerability scanning
· CSPM and secure baseline deployments
· Automated security response runbooks
Continuous Improvement & Knowledge Sharing
· Blameless postmortem culture
· Documentation and knowledge base
· Communities of practice and training
· SRE:Dev program and rotations
11/05/2016
Sash Barige
Links
Cloud Strategy
Phase 1
Phase 2
Phase 3
Making it Happen
DevOps Rigor
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