What is our primary use case?
For example, when production CPU is at 95%, I identify which query is consuming excessive CPU by checking Foglight. I then review the execution plan in SQL Server Management Studio and verify the indexes, statistics, and whether the query has recently changed. I coordinate with the development team if query modification is required and monitor CPU after the remediation.
I have also monitored blocking in Foglight. When Foglight shows significant blocking, I identify the head blocker first rather than identifying and killing the session. I check the blocking session ID, blocked session ID, SQL statement, transaction duration, database involved, application, and locks to determine whether the transaction is expected or abnormal. If necessary, after appropriate approval, I terminate the blocking session and investigate the underlying causes.
Foglight for Databases is deployed in a hybrid environment in our organization. We monitor the databases running on-premise infrastructure as well as the databases hosted in AWS. This is useful for our DBA team because we can have centralized monitoring access with different environments instead of using separate monitoring solutions for each platform. We primarily use Foglight for Databases for database health checks, performance monitoring, alerts, troubleshooting, and historical performance analysis across these environments.
How has it helped my organization?
Foglight for Databases has positively impacted our database operation mainly by improving monitoring and troubleshooting efficiency. It provides a centralized view of database health checks and performance, which helps us identify issues such as high CPU, memory blocking, and slow running queries more quickly. It has also helped us reduce the time spent on the initial investigation during production incidents because we can quickly identify the affected database based on the relevant performance metrics, with historical data being useful for trend analysis and root cause investigation.
I estimate Foglight for Databases can save around 20 to 30 minutes during the initial investigation for production database issues because it gives me a centralized view of CPU, memory, blocking, waits, sessions, and query activity. During a production incident, Foglight for Databases helped me quickly identify that the issue was related to high resource utilization and blocking sessions. Instead of checking multiple metrics separately, I start with the Foglight for Databases dashboard to identify the affected database and session, then validate the details in SSMS.
What is most valuable?
Foglight for Databases features like monitoring information and dashboard presentation are prominent in the product. Feature capabilities I usually use include database health check monitoring, real-world performance monitoring, CPU memory, disk space issues monitoring, block and deadlock detection, SQL query performance analysis, alert and notification, historical performance trend analysis, database availability monitoring, capability and storage monitoring, and centralized monitoring for multiple database environments.
The features I use most are real-time database performance monitoring and troubleshooting, which give me a centralized view of important metrics such as CPU, disk space monitoring, blocking sessions, waits, and query performance. It adds the most value during a production incident because I can quickly identify which database is responsible for the SQL activity that caused the issue, validating the findings in SSMS. The historical performance data is also useful for identifying trends and comparing performance before and after changes. Overall, it helps me reduce the time required to identify the root cause of any database performance issue.
I use Foglight for Databases's real-time activity and performance monitoring capability during database operations, particularly for production troubleshooting. For example, if an application suddenly becomes slow, I use Foglight for Databases to check the current database activity, active sessions, CPU utilization, waits, blockings, and resource investigation in SQL.
I use Foglight for Databases Angular UI to monitor database activity and identify long-running or resource-intensive queries. For example, if a query starts running significantly longer than the normal execution plan time, Foglight for Databases can bring it to our attention. I review the query details along with CPU, I/O, waits, and blocking information, then validate the issue in SSMS using the execution plan and DMVs.
Foglight for Databases allows us to drill down from a high-level database server alert into more detailed performance information. For example, if I see something unusual in a high-level alert, I first validate the alert and identify which server or database is affected. I then use Foglight for Databases's drill-down capability to move from the overall health view into the specific performance that's causing the alert.
One useful capability of Foglight for Databases is that it provides centralized monitoring across multiple database platforms.
I assess Foglight for Databases's capability to quickly diagnose emerging database issues as very useful for day-to-day production operations. This ability to start with a high-level alert and drill down into database activity, sessions, queries, blocking, and CPU waits shortens the initial troubleshooting process. It's especially important in production environments where sudden performance issues can quickly affect application users.
My impression is that Foglight for Databases provides useful visibility across different layers of the environment, particularly database and infrastructure metrics that are relevant to DBA operations. Having information about database performance, server resources, CPU, memory, blocking, and query activity in a centralized monitoring platform is invaluable.
What needs improvement?
Foglight for Databases is useful for database monitoring and troubleshooting, but there are a few areas where it could be improved. I would appreciate more customization in the dashboard and simpler configuration of alerts. It would help to easily customize the dashboard based on the specific requirements for different DBA teams and environments. Another useful improvement would be more intelligent alert correlation and root cause analysis so that related alerts can be grouped and the tool can clearly indicate the issues.
For how long have I used the solution?
I have used Foglight for Databases over the last 12 months.
What do I think about the stability of the solution?
Foglight for Databases is stable in my experience.
What do I think about the scalability of the solution?
Foglight for Databases handles growth reasonably well, especially when additional database instances need to be brought under centralized monitoring. As our database environment changes, we can add monitoring instances and continue using the same monitoring and alert approach.
How are customer service and support?
I have not personally interacted with Foglight for Databases customer support team frequently, but my experience with them has been positive. Whenever we need assistance, the support process is handled through the appropriate organizational support channels.
Which solution did I use previously and why did I switch?
Before Foglight for Databases, we relied more heavily on native database and infrastructure monitoring tools such as SQL Server Management Studio, SQL Server agent alerts, and Windows manual checks. The biggest improvement for DBA operations was the ability to quickly identify and drill down into performance issues and use historical information for troubleshooting and trend analysis.
What was our ROI?
I have seen a return on investment using Foglight for Databases for our database operations.
Which other solutions did I evaluate?
I was not involved in the formal evaluation process, so I do not know which competing solutions were evaluated before Foglight for Databases was selected. I understand that the key requirements were centralized database monitoring, performance visibility, proactive alerting, troubleshooting capability, and support for the organization's database environment.
What other advice do I have?
Governance and security are important considerations when using AI capabilities in database monitoring. AI-driven recommendations must follow the same access control and security policy as the underlying monitoring platform. I particularly look for strong role-based access control, secure handling for database and performance information, auditability, and clear visibility into how AI-generated recommendations are produced and what data they use.
I find the output from Foglight for Databases most valuable as a starting point for troubleshooting rather than as a replacement for DBA analysis. For production issues, I always validate the recommendations against SQL Server metrics, execution plans, DMVs, logs, and the actual application behavior before taking corrective action. This is particularly important because database performance issues can have multiple contributing factors. Overall, I consider the output useful for accelerating investigation, but I still rely on data validation before making production changes.
I recommend that organizations evaluate Foglight for Databases by first identifying their main monitoring and troubleshooting requirements and then validating those requirements with a representative setup of their database environment.
I give Foglight for Databases an overall rating of 8 out of 10 because it is very helpful for any database monitoring such as CPU and memory, which is essential for my daily routine work.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)