Selected Work Link to heading

Below are examples of work I have done in large-scale enterprise environments over the past two decades.
Each project is presented in terms of problem, approach and impact — focusing on how configuration truth, drift analysis and operational insight improved stability and predictability.

No customer names or sensitive details are disclosed.

Stabilizing a Large-Scale Hosting Environment Link to heading

Summary:
Multi-year responsibility for a mission-critical Windows hosting platform with 1,000+ systems distributed across development, test and production stages.

The Problem:
Environments drifted constantly.
Defaults, inheritance and undocumented overrides created unpredictable behavior.
Deployments succeeded in one stage and failed in the next.
Teams operated on outdated or assumed truth, not reality.

Approach:

• Analyzed real runtime configuration across environments
• Identified hidden defaults and contradictory settings
• Exposed drift patterns and configuration mutations
• Introduced structured configuration validation
• Improved deployment models and layering logic
• Reduced reliance on outdated CMDB and documentation

Impact:

• Fewer unpredictable failures
• Improved deployment reliability
• Clear visibility into real configuration state
• Dramatic reduction in time spent diagnosing issues

Turning Configuration Chaos Into Predictable Behavior Link to heading

Summary:
Restructuring complex configuration landscapes with inconsistent layering, legacy overrides and unclear precedence rules.

The Problem:
Multiple teams modified configuration at different levels (local, global, inherited).
Runtime behavior no longer matched repository definitions.
Systems behaved differently despite “identical” configs.

Approach:

• Mapped configuration hierarchies and precedence rules
• Identified default propagation and fallback behavior
• Normalized settings to expose effective configuration
• Removed shadow configurations and legacy overrides
• Introduced deterministic configuration logic

Impact:

• Predictable behavior across environments
• Removal of silent configuration differences
• Stable and reproducible system behavior
• A clear model for understanding configuration truth

Discovery and Insight Across Heterogeneous Systems Link to heading

Summary:
Development of internal methodologies to analyze large numbers of Windows and Linux systems for configuration consistency and drift.

The Problem:
Different teams used different tools, scripts and conventions.
Configuration lived in files, databases, registries, environment variables and inherited defaults.
No central view of what systems were actually doing.

Approach:

• Designed a unified approach to collect, normalize and compare configuration
• Analyzed drift, anomalies and inconsistent patterns
• Created structural mapping of configuration data
• Identified relationships and dependencies not visible at first glance

Impact:

• A consistent view of configuration across heterogeneous environments
• Identification of systemic design issues
• Clear insights into how systems diverged over time
• Basis for long-term improvements in configuration discipline

Revealing Drift in Multi-Stage Environments Link to heading

Summary:
Focused analysis of differences between Development, Test, QA and Production systems.

The Problem:
Teams assumed systems were “identical” across stages.
Reality showed dozens of small but critical differences in configuration, defaults and inherited settings.

Approach:

• Collected real runtime configuration from each stage
• Compared settings structurally rather than textually
• Identified mutation hotspots and root causes
• Highlighted unexpected overrides and fallback behavior

Impact:

• Stable progression through the deployment pipeline
• Early detection of drift before it reached production
• Reduction in unexplained errors and environment-specific bugs
• Confidence in the integrity of each stage

Designing a Future-Oriented Configuration Management Model Link to heading

Summary:
Creating a conceptual and practical model for handling configuration truth in environments with complex layering, defaults, inheritance and drift.

The Problem:
Traditional configuration management tools could not reflect real-world behavior.
Teams needed a model that captured what systems actually do, not what repositories describe.

Approach:

• Developed a principles-based configuration model
• Focused on effective configuration and runtime truth
• Integrated drift detection and dependency awareness
• Built a foundation for validation before deployment
• Created a clear separation between intention and reality

Impact:

• Stronger configuration governance
• Better predictability in complex systems
• Basis for automation and refactoring initiatives
• A long-term path away from configuration chaos

If you’re interested in understanding what these approaches could mean for your environment, feel free to reach out.

Get in touch

Email me: starttalking@sh-soft.de