Enterprise SQL Server Architecture • Cloud Database Platforms • Performance Engineering • Observability • AI-Driven Database Automation
Welcome to my SQL Server engineering portfolio.
I design, modernize, optimize, and automate mission-critical SQL Server and cloud database platforms. My work connects architecture, performance telemetry, execution evidence, operational automation, and root-cause analysis.
Monitoring shows that something happened. Engineering explains what happened, why it mattered, and what evidence supports the conclusion.
- Enterprise SQL Server Architecture
- SQL Server 2016, 2019, and 2022
- Azure SQL Database and Azure SQL Managed Instance
- SQL Server on AWS
- Always On Availability Groups
- Failover Cluster Instances
- High Availability and Disaster Recovery
- VLDB Index (Rebuild & Reorg) Administration and Maintenance Engineering
- Query Tuning and Execution-Plan Analysis
- Performance Engineering and Workload Analysis
- Database Observability and Operational Diagnostics
- AI-Driven Database Automation
- Evidence-Backed Root-Cause Analysis
- Cloud Migration and Database Modernization
| Project | Engineering Focus |
|---|---|
| Agentic AI RCA Toolkit for SQL Server | SQL Server flight recorder that correlates CPU, blocking, I/O, memory, TempDB, transaction-log, and connectivity evidence into evidence-backed root-cause analysis. |
| SQL Server VLDB Index Maintenance Framework | Queue-based maintenance orchestration for multi-terabyte SQL Server environments with AG-aware execution, resumable windows, observability, and recovery workflows. |
| SQL Server Agentic AI DBA Assessment Toolkit | AI-assisted SQL Server assessment toolkit for health checks, security reviews, performance analysis, prioritized findings, and executive-ready reporting. |
| SQL Server Observability and DBA SRE Toolkit | Enterprise observability and DBA SRE toolkit for monitoring, alerting, Always On health, blocking, TempDB, transaction-log, and operational diagnostics. |
| SQL Server CPU Saturation RCA | CPU troubleshooting and root-cause analysis covering scheduler pressure, wait statistics, workload correlation, and performance diagnostics. |
| SQL Server Engineering Portfolio | Central portfolio for SQL Server architecture, HA/DR, performance engineering, observability, cloud modernization, and database automation projects. |
These projects demonstrate engineering patterns designed to:
- Preserve diagnostic evidence before it disappears
- Correlate activity across multiple SQL Server performance domains
- Connect runtime parameters with matching execution plans
- Distinguish symptoms from contributing and root causes
- Reduce manual investigation time
- Make technical reasoning repeatable and auditable
- Automate high-risk operational workflows safely
- Produce technical and executive-ready evidence
- Improve the reliability of mission-critical database platforms
The Agentic AI RCA Toolkit connects investigation layers that are often analyzed separately:
Time window → Database scope → Sessions → Executions → Runtime parameters → Execution plans → Resource pressure → Causal relationships → Evidence-backed conclusion
The toolkit currently demonstrates:
- Target-database scoping for I/O and transaction-log evidence
- Scheduled Extended Events capture
- Persisted diagnostic evidence
- Captured actual-plan parsing
- Runtime-parameter extraction
- Execution-to-plan fallback linkage
- Multi-domain candidate generation and arbitration
- Timestamped HTML reporting
- Atomic deployment patterns for correction scripts
The repository also distinguishes clearly between demonstrated capability and remaining production-hardening requirements.
| Area | Technologies and Practices |
|---|---|
| Database Platforms | SQL Server, Azure SQL Database, Azure SQL Managed Instance, SQL Server on AWS |
| Architecture | Always On Availability Groups, Failover Cluster Instances, HA/DR, hybrid cloud |
| Performance | Query Store, execution plans, wait statistics, DMVs, workload analysis |
| Observability | Extended Events, blocking analysis, TempDB, I/O, memory, transaction-log diagnostics |
| Automation | T-SQL, PowerShell, SQL Agent, GitHub Actions, validation workflows |
| Engineering | Evidence persistence, causal correlation, safe remediation, auditable reporting |
| AI Enablement | AI-assisted assessment, diagnostic reasoning, evidence synthesis, report automation |
- Microsoft Azure Fundamentals — AZ-900
- Microsoft Azure Database Administrator Associate — DP-300
- AWS Certified Cloud Practitioner — CLF-C02
- Microsoft Certified IT Professional — SQL Server DBA
- Microsoft Certified Solutions Expert — SQL Server
- Microsoft Certified Solutions Associate — SQL Server
- Microsoft Certified Database Administrator — SQL Server
Modern database platforms should be reliable, observable, automated, secure, and scalable.
My focus is helping organizations transform traditional database operations into resilient, cloud-ready data platforms through architecture, performance engineering, operational automation, and evidence-backed diagnostics.
A strong database engineering practice should:
- Improve platform reliability and availability
- Reduce operational risk and unplanned downtime
- Accelerate incident detection and root-cause analysis
- Increase DBA and engineering productivity
- Strengthen security and operational governance
- Make engineering decisions explainable and auditable
- Support mission-critical workloads at scale
- 🌐 Portfolio: https://sql-it-techsolutions.com/
- 💼 LinkedIn: https://linkedin.com/in/louis-achungo
⭐ If this helped, consider starring the repo.
Louis Achungo
Principal SQL Server Architect | Performance Engineering | HA/DR | Azure SQL & AWS
Building reliable, observable, and scalable database platforms through architecture, automation, and operational excellence.