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SolarWinds Enhances SolarWinds Database Performance Analyzer (DPA)

SolarWinds announced extensive correlation and visibility into the database and the layers that support it with enhancements to SolarWinds Database Performance Analyzer (DPA), delivering database administrators – or DBAs – valuable insight into the database’s impact on other layers of an application stack and empowering them to provide their IT departments with the solution to the common concern: “Why is this app so slow?”

SolarWinds DPA, with its unique approach that includes Multi-Dimensional Performance Analysis, continuously monitors SQL Server, Oracle, Sybase and DB2 databases on physical, cloud-based and VMware servers to identify database performance issues that impact end-user response times, isolate root cause, show historical performance trends, and correlate metrics with response time and performance. In its latest version, SolarWinds DPA 9.0 adds storage resource visibility and correlation, providing database admins with unique insight into how storage I/O issues contribute to poor response time; and adds metric baselining and alerting, which enables DBAs to pinpoint the root cause of performance issues within minutes.

“Simply put, application performance is database performance, so when apps are slow, fingers often point at the database and the DBA. However, since databases are complex and full of core critical information, we need to look to the DBAs not to place blame, but to find solutions,” said Gerardo Dada, VP of Product Marketing, SolarWinds. “Now with SolarWinds DPA 9.0, DBAs have insight both into the performance of databases and how they work with other components that support an app. In this way, DBAs essentially become the performance gurus of an IT department, offering a single truth that aligns teams behind facts and empowers them to take action.”

New in SolarWinds DPA 9.0: DBAs have the power to answer, “Is the app issue in the database?”

- No, the problem is somewhere else: DBAs can eliminate the database as the source of the problem and provide context into how other components of the IT infrastructure correlate with database activities. With NEW storage I/O analysis, DBAs can see the impacts of storage on the database workload, gaining insight into how storage issues such as latency and disk performance can contribute to poor database response time and ultimately impact the end user of an application.

- Yes, the problem is in the database: Database performance is dynamic, so DBAs need the right tools to be able to compare expected performance with abnormal performance. With NEW resource metric baselining and alerting, DBAs can proactively identify resource outliers, correlating with app response time, and then drill deeper to see if the problem indeed originated in the database and, if so, how to resolve it.

SolarWinds DPA is based on a non-intrusive agentless architecture, making it safe to use in production environments with negligible performance impact. SolarWinds DPA can be used to monitor thousands of database instances running on premise, VMware or in the cloud.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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 ...

SolarWinds Enhances SolarWinds Database Performance Analyzer (DPA)

SolarWinds announced extensive correlation and visibility into the database and the layers that support it with enhancements to SolarWinds Database Performance Analyzer (DPA), delivering database administrators – or DBAs – valuable insight into the database’s impact on other layers of an application stack and empowering them to provide their IT departments with the solution to the common concern: “Why is this app so slow?”

SolarWinds DPA, with its unique approach that includes Multi-Dimensional Performance Analysis, continuously monitors SQL Server, Oracle, Sybase and DB2 databases on physical, cloud-based and VMware servers to identify database performance issues that impact end-user response times, isolate root cause, show historical performance trends, and correlate metrics with response time and performance. In its latest version, SolarWinds DPA 9.0 adds storage resource visibility and correlation, providing database admins with unique insight into how storage I/O issues contribute to poor response time; and adds metric baselining and alerting, which enables DBAs to pinpoint the root cause of performance issues within minutes.

“Simply put, application performance is database performance, so when apps are slow, fingers often point at the database and the DBA. However, since databases are complex and full of core critical information, we need to look to the DBAs not to place blame, but to find solutions,” said Gerardo Dada, VP of Product Marketing, SolarWinds. “Now with SolarWinds DPA 9.0, DBAs have insight both into the performance of databases and how they work with other components that support an app. In this way, DBAs essentially become the performance gurus of an IT department, offering a single truth that aligns teams behind facts and empowers them to take action.”

New in SolarWinds DPA 9.0: DBAs have the power to answer, “Is the app issue in the database?”

- No, the problem is somewhere else: DBAs can eliminate the database as the source of the problem and provide context into how other components of the IT infrastructure correlate with database activities. With NEW storage I/O analysis, DBAs can see the impacts of storage on the database workload, gaining insight into how storage issues such as latency and disk performance can contribute to poor database response time and ultimately impact the end user of an application.

- Yes, the problem is in the database: Database performance is dynamic, so DBAs need the right tools to be able to compare expected performance with abnormal performance. With NEW resource metric baselining and alerting, DBAs can proactively identify resource outliers, correlating with app response time, and then drill deeper to see if the problem indeed originated in the database and, if so, how to resolve it.

SolarWinds DPA is based on a non-intrusive agentless architecture, making it safe to use in production environments with negligible performance impact. SolarWinds DPA can be used to monitor thousands of database instances running on premise, VMware or in the cloud.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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 ...