Microsoft SQL Server monitoring automates the collection of key metrics and relevant events.
It’s the first step in optimizing the performance of your data platform.
Aggregating that performance data in a powerful system with intuitive dashboards and critical alerts allows fast diagnosis of potential problems in your server environment. The SentryOne Platform continuously gathers the most actionable performance metrics and displays them in a convenient and logical manner, helping you proactively perform analyses.
Having a bird’s-eye view of your entire system helps you understand the current conditions of your environment. Historical data helps you measure performance improvements against the previous state.
SentryOne’s Advisory Conditions allow you to define thresholds, create baselines, and set alerts. This customization filters out common noise. SentryOne's extended coverage provides insights across virtual, on-premises, cloud, or hybrid environments.
Failing to monitor SQL Server performance might leave you spinning your wheels when you're trying to solve urgent problems. In some cases, you might have to replicate a scenario in order to capture the data you need to solve the issue. Replicating a particular scenario can be time-consuming—or impossible.
Threshold alerts help you mitigate issues before they reach critical levels. When you're researching complex performance issues, Performance Monitor provides quick access to the problem-solving data.
How do you know for certain the moment data starts slowing down? Baselines reveal normal performance across various conditions, allowing you to measure server performance before and after a particular incident. You can discover and address issues before your users notice a performance problem.
Performance issues can pop up unexpectedly. If you are capturing this data through a monitoring solution, you can easily “rewind” to see what exactly happened. This saves you the time and frustration of trying to solve a problem without a complete picture of what went wrong.
Day-to-day SQL monitoring without any automation is tedious. You could spend hours every day just gathering data you need, such as event and job history logs from multiple servers and instances, with historical information.
After gathering the data, you'll still need to format and present that data in a logical, consumable fashion—which means you'll have even less time for solving problems.
DBAs who try to perform these tasks manually often abandon daily monitoring altogether.
The result is a perpetual cycle of reactive troubleshooting. The reactive stance reduces performance over the long
Your best defense against under-performing databases is monitoring your entire data platform stack, from SQL Server to hypervisor hosts. This includes analytics platforms such as SQL Server Analysis Services (SSAS), as well as big data platforms such as Microsoft Analytics Platform System (APS). Cloud platforms such as Microsoft Azure and Amazon Web Services are also a part of your overall data estate. All of these can be continuously monitored—and should be. If you're looking at only SQL Server, you could be missing visibility into what's causing performance issues in other areas of your data platform.
Conducting SQL Server monitoring with manual methods or a multiple-tool “Frankensystem” could mean wasted resources and inadequate results.
It’s possible to monitor your systems without any tools—but that will require building your own scripts and manually pulling datasets from disparate locations. You'll likely spend more time capturing and managing data than you will spend solving problems. Don't get bogged down with administrative data collection when you’re needed to manage the overall health and performance of your data platform.
Are you using multiple performance analytics products to monitor your system? You've likely encountered the most glaring downside of this approach—disconnected data. Using various products also means multiple pricing structures, systems, and interfaces. And you'll need to make correlations in your mind—or export data from all the tools, and build your own system to combine all that data in a way that makes sense.