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Databases & messaging · #azuresqldatabase
Stop over-provisioning for peak loads. Learn how Azure SQL Database Hyperscale decouples compute from storage to handle bursty traffic and automatic storage growth.
A practical guide to enabling Azure SQL Database Query Store, querying execution metrics via DMVs, detecting plan regressions, and managing retention and overhead.
Minimal geo-resilient design for a single Azure SQL Database using active geo-replication and an auto-failover group, with requirements, trust boundaries, operational checks, failure modes and when to change the design.
Automatic Tuning in Azure SQL Database lets the engine detect and fix regressions with minimal DBA effort. Learn how to enable, verify, and monitor the feature, plus key trade‑offs and a practical example.
Hyperscale separates compute from storage, giving you seconds-fast scaling, minute-fast restores, and storage-free read replicas — but asynchronous HA and unbundled billing mean it isn't right for every workload.
Automatic Tuning enabled but no performance gains? Diagnose Query Store state, per-option settings, and the recommendation lifecycle in sys.dm_db_tuning_recommendations to find why nothing is being applied.
Hyperscale decouples compute from storage, enabling near-instant vertical scaling and high log-rate throughput for large Azure SQL workloads.
Azure SQL Database can detect plan regressions and force the last known good plan automatically. Here’s how FORCE_LAST_GOOD_PLAN works, how to audit it, and where it falls short.
Learn how to solve unpredictable query performance in Azure SQL Database caused by parameter sniffing using the OPTION (RECOMPILE) hint to stabilize latency.