How Azure SQL Database's built-in AI automatically detects performance regressions, applies index recommendations, and prevents threats — without DBA intervention.
An e-commerce platform's Azure SQL Database experiences weekly performance degradations during peak traffic. The DBA team spends 4-6 hours per incident manually analyzing execution plans, identifying missing indexes, and applying fixes — often after customers have already experienced slow page loads and abandoned carts.
Automatic tuning is enabled via the Azure portal or T-SQL. Azure SQL begins monitoring query performance baselines and tracking execution plan changes continuously.
When a query's execution time increases significantly from its baseline, Intelligent Insights generates a diagnostic log entry identifying the root cause: plan regression, missing index, increased locking, or parameter sniffing.
Azure SQL automatically creates recommended indexes, monitors their impact, and drops them if they don't improve performance — preventing index bloat while maintaining optimal query plans.
When a query plan regresses, Azure SQL automatically forces the last known good plan using Query Store, restoring performance without DBA involvement.
Azure Defender for SQL uses ML to detect anomalous query patterns indicative of SQL injection, unusual data access, and brute force attacks — sending real-time alerts to the security team.
Let's discuss how AI-enhanced SQL Server & T-SQL Development can deliver similar results for your organization.
Start the Conversation