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Datadog Extends Monitoring for Microsoft SQL Server and Microsoft Azure Database Platforms

The expanded support provides visibility into query-level metrics and detailed explain plans so all Microsoft customers can benefit from Database Monitoring

Datadog, Inc. the monitoring and security platform for cloud applications, announced expanded monitoring for Microsoft SQL Server and Microsoft Azure database platforms. The announcement builds on Datadog’s Database Monitoring product, which launched in August of last year.

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With this expanded support, engineers and database administrators can quickly pinpoint and address database performance issues such as costly and slow queries, incorrect indexes in SQL Server or Azure databases and bottlenecks in their applications.

“We launched Database Monitoring last year because we wanted to help our customers reduce database costs, troubleshoot performance inefficiencies and increase collaboration between engineers and database administrators,” said Omri Sass, group product manager of Application Performance Monitoring at Datadog. “By adding support for SQL Server and Azure database services, Microsoft users are better able to accomplish these goals and discover and implement the right database improvements while saving time communicating and reconciling information.”

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“Microsoft Azure SQL Database and SQL Managed Instance are fully managed database services that feature built-in security controls, automated maintenance and are always up to date,” said Ramnik Gulati, senior director, product marketing, data & AI at Microsoft. “The expanded support of Datadog’s Database Monitoring product further strengthens this collaboration by providing Microsoft customers with deep insights into their managed and self-hosted SQL Server, PostgreSQL and MySQL databases, enabling them to build and scale workloads with confidence.”

Datadog Database Monitoring for Microsoft SQL Server and Azure database platforms includes the following features:

  • Valuable Query Metrics: View metrics such as average latency, total execution time and number of rows queried in order to identify problematic queries and use historical query performance data to track long-term trends.
  • Explain Plan Analysis: Visualize differences between multiple explain plans for individual queries to identify hotspots and seamlessly pivot from explain plans to related metrics in order to understand how inefficiencies impact performance.
  • Centralized Query, Database and Infrastructure Metrics: View and monitor query-level and host-level metrics together to better understand how resource constraints affect database performance.

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