![]() ![]() For more information, see Diagnostic telemetry for export. Diagnostic settings are a feature that contains Resource Log categories (formerly known as Diagnostic Logs). Not to be confused with the Azure SQL Insights (preview) product, SQLInsights is a log inside Intelligent Insights, and is one of several packages of telemetry emitted by Azure diagnostic settings. It is deployed via a customer-managed VM using Telegraf as a collection agent that connects to SQL sources, collects data, and moves data into Log Analytics.Īzure SQL Analytics (preview) also requires Log Analytics to provide advanced insights into Azure SQL database activity.Īzure diagnostic telemetry is a separate, streaming source of data for Azure SQL Database and Azure SQL Managed Instance. Azure SQL Insights (preview) and Azure SQL Analytics (preview)īoth offerings use different pipelines to present data to a variety of endpoints for coming Azure SQL Database metrics.Īzure SQL Insights (preview) is project inside Azure Monitor that can provide advanced insights into Azure SQL database activity. See Monitoring using DMVs for scripts to monitor for a variety of performance issues in Azure SQL Database and Azure SQL Managed Instance. Outside of the Azure portal, the database engine has its own monitoring and diagnostic capabilities that Azure SQL Database and SQL Managed Instance leverage, such as query store and dynamic management views (DMVs). You can choose to configure the streaming export of these Intelligent Insights and other database resource logs and metrics to one of several destinations for consumption and analysis. Automatic tuning also is available in the Azure portal in the Overview pane of your Azure SQL Database under "Intelligent Performance".Īzure SQL Database and Azure SQL Managed Instance provide advanced monitoring and tuning capabilities backed by artificial intelligence to assist you in troubleshooting and maximizing the performance of your databases and solutions. You can also configure automatic tuning to implement these recommendations automatically, such as forcing a query execution plan to prevent regression, or creating and dropping nonclustered indexes based on workload patterns.Use the automatically collected information to identify queries and begin optimizing your workload performance. ![]()
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