22-25 April 2026

Torsten Strauß

Torsten Strauß (MVP) is the CEO of inside-sqlserver and Principal Microsoft SQL Consultant at Sarpedon Quality Lab. He is a multi-certified Microsoft expert with a strong focus on Azure DevOps, SQL Server performance optimization, and the development of scalable SQL Server and Azure SQL solutions. Torsten has delivered more than 330 technical sessions at national and international conferences and user groups. Since 2018, he has been recognized annually as a Microsoft Most Valuable Professional for Data Platform — and since 2025 also for Developer Technologies. He is a Microsoft Certified Trainer and has been listed among Sessionize’s Top Speakers for three consecutive years.

Torsten Strauß's Sessions

Analyzing Azure SQL Database Workloads with KQL Insights, Patterns, and PerformanceSQLBits 2026

Learn how to use KQL with Azure Monitor and Log Analytics to analyze Azure SQL workloads. Identify performance bottlenecks, high-impact queries, and usage patterns, and turn telemetry into actionable insights for tuning and cost optimization.

Bicep on Azure – Building Modular and Reusable Infrastructure as CodeSQLBits 2026

Learn how to design clean, modular, and reusable Azure infrastructure with Bicep. Explore parameters, modules, loops, and CI/CD integration with Azure DevOps, and see how to standardize and scale IaC using shared Bicep modules and ACR.

Deep Dive into Memory Grants:Diagnosing and Optimizing Query Performance in Azure SQL and SQL ServerSQLBits 2026

Dive deep into memory grants in SQL Server and Azure SQL. Learn how memory is requested and allocated, why spills to tempdb occur, and how over- and under-allocation impact performance. Apply practical tuning techniques, including Memory Grant Feedback.

End-to-End Database DevOps: Building Automated CI/CD Pipelines for Azure SQLSQLBits 2026

Learn how to implement database DevOps with a fully automated Azure DevOps YAML pipeline for Azure SQL. From schema drift detection to dacpac build, versioned artifacts, and safe deployments using reusable templates for scalable CI/CD.

Hybrid Data in Motion: Near Real-Time Mirroring from SQL Server 2025 into Microsoft FabricSQLBits 2026

Explore SQL Server 2025 on-premises mirroring into Microsoft Fabric OneLake for near real-time analytics without ETL. Learn setup, limitations, monitoring, and see mirrored data queried live via Fabric SQL Endpoint and Lakehouse notebooks.

Inside Data Compression in SQL Server and Azure SQL: Performance Gains, Trade-offs, and Engine InterSQLBits 2026

Learn how row and page compression work in SQL Server and Azure SQL, how they impact I/O and CPU, and when compression improves—or hurts—performance. Explore internals, limitations, and real-world scenarios to make informed tuning decisions.

Optimized Locking in SQL Server 2025: Internals, Contention, and ConcurrencySQLBits 2026

Explore SQL Server 2025 locking internals, including Optimized Locking and Lock After Qualification. Learn how these changes reduce contention, blocking, and deadlocks, and how to analyze and optimize concurrency using DMVs and Extended Events.

Performance Unleashed: Practical Tuning for Azure SQL and SQL ServerSQLBits 2026

Learn how to diagnose and optimize slow SQL Server and Azure SQL workloads. Explore real-world query tuning techniques, Intelligent Query Processing internals, and practical strategies to improve performance and reduce costs across cloud and on-premises environments.

Plan Cache Internals in SQL : Parameter Sensitive Plans, Cache Optimization, and MonitoringSQLBits 2026

plan reuse helps or hurts performance, and how parameter sniffing, plan pollution, and PSP Optimization impact real-world workloads. Diagnose issues using DMVs and Extended Events.

Streamlined Deployments: Robust YAML CI/CD Pipelines for Azure Data FactorySQLBits 2026

Learn how to build a YAML-based CI/CD pipeline for Azure Data Factory in Azure DevOps. From ARM validation and artifact generation to trigger management, approvals, and environment-specific deployments using proven DevOps patterns.