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

Datadog 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.

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.”

“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

Database Monitoring is now generally available for Microsoft customers using SQL Server or Azure Database Platforms.

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

Datadog 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.

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.”

“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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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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