A defensible set of architectural and operating principles for regulated Databricks platforms on Azure.
These principles are designed for teams that must deliver at pace while remaining review-ready for security, risk, and audit stakeholders.
- Designing a new Databricks on Azure platform for regulated workloads.
- Remediating control, reliability, or operating-model weaknesses in an existing platform.
- Preparing for production readiness, internal assurance review, or regulatory audit.
- Build the target architecture: Architecture Principles -> Design Principles -> Platform Principles
- Strengthen control and assurance posture: Control Principles -> Assurance Principles
- Stabilize day-2 operations: Operating Principles -> Platform Principles
- Architecture Principles Covers platform structure, boundary definition, and scaling model. Use when selecting reference patterns, boundaries, and deployment shape. Outcome: a defensible architecture baseline.
- Platform Principles Covers ownership model, reliability posture, and cost discipline. Use when shaping platform services, onboarding model, and operations. Outcome: predictable delivery and sustainable scale.
- Design Principles Covers data product design quality, contracts, coupling, and evolution. Use when defining medallion flows, interfaces, and quality gates. Outcome: reusable and resilient data products.
- Assurance Principles Covers evidence, lineage, change accountability, and retention controls. Use when preparing for control testing and audit-readiness. Outcome: continuous assurance, not point-in-time compliance.
- Control Principles Covers policy enforcement, least privilege, and control ownership. Use when implementing access and governance operating controls. Outcome: stronger control posture without delivery slowdown.
- Operating Principles Covers reliability targets, change process, observability, and runbooks. Use when hardening production support and incident response. Outcome: stable and accountable day-2 operations.
Use these four checks before approving major platform decisions:
- Risk impact: does this reduce or increase control and compliance risk?
- Delivery impact: does this improve team throughput without weakening controls?
- Operability impact: can the platform run reliably under production load?
- Auditability impact: can we produce objective evidence for this decision later?
- Access model drifts over time and least-privilege erodes. Start with Least Privilege by Default.
- Data quality breaks downstream reporting and model outputs. Start with Quality Gates Are Part of the Workflow.
- Incident response is slow because ownership is unclear. Start with Operational Ownership Is Explicit.
- Audit readiness becomes a late manual exercise. Start with Evidence Is First-Class.
- Select the principle domain and unit principle that matches the problem.
- Assess current state against the principle's decision checks.
- Define target controls, ownership, and implementation sequence.
- Implement through approved platform patterns and change controls.
- Review outcomes quarterly and log compensating controls for any exceptions.
foundations/: hub artifacts and shared context (start here:foundations/principles.md)architecture/,design/,platform/,control/,assurance/,operating/Each domain has an index page plus individual unit principle files.
- Architecture Manifesto
- Solution architecture templates: https://github.com/paulkarikari/solution_architecture_templates