Working as a DBA has always meant doing more with less. But the gap between estate growth and headcount is becoming harder to ignore. Database platforms keep multiplying. Instance counts keep climbing. The DBA team rarely grows at the same rate. Often, the assumption is that scaling coverage means adding people. It doesn’t have to.

In a recent webinar, I sat down with Ash Recksiedler. Together, we walked through the habits, workflows, and tools that let a single DBA keep pace with a sprawling, multi-platform data estate. Here’s a rundown of what we covered. It includes a live look at how SolarWinds® Database Performance Analyzer (DPA) software helped us trace a business complaint back to its root cause in minutes.

Why Database Estates Are Outgrowing DBA Teams

The 2025 State of Database Report surveyed more than a thousand DBAs worldwide, and the picture it paints isn’t comfortable. DBAs report spending roughly 27 of 40 working hours a week on reactive firefighting. That means responding to alerts, chasing incidents, and putting out fires that a more proactive process could have prevented.

That pressure has consequences. Around 38% of DBAs say they’re considering leaving the role, and it’s not only the DBAs nearing retirement. Burnout and constant on-call pressure are pushing people out at every career stage. Meanwhile, the average DBA now manages three to five different database platforms in enterprise and large midsize organizations. This pattern shows up across roughly 65% of the global Fortune 1000.

Add to that the reality that nearly every application problem traces back to the database layer eventually. It might be a poorly written query or a missing index. Either way, it’s clear why “slow is the new down” has become a common refrain from business leaders. The DBAs who get ahead of this aren’t the ones with bigger teams. They’re the ones who’ve built proactive, repeatable processes instead of reactive habits, which is how they scale database coverage without adding headcount.

Treat Your Database Like Code

One of the clearest differences between leaders and laggards in our research is simple: does the team treat its database as a first-class citizen in the deployment process? Database objects, from tables and views to stored procedures and user-defined functions, are built out of SQL code. That code deserves version control just as much as application code does.

Storing database code in a system like Azure DevOps or GitHub gives a team a historical record of what changed and when. That record becomes essential the moment something needs to be rolled back. Without it, a stored procedure in production may be the only record of what changed from release to release, especially if documentation habits are inconsistent. Managing database code this way helps eliminate manual errors. It also standardizes rollout and keeps the database in sync with the application it supports.

Shift SQL Quality Checks Left

Teams that consistently perform well share another habit. They catch poor-performing SQL before it reaches production, not after. In many organizations, a developer’s only requirement is that code meets a functional specification. Performance isn’t part of the spec, so performance problems ship. End users become the unofficial beta testers who report it.

The fix is to push performance evidence earlier in the cycle. Source control and DevOps platforms support quality gates. These are custom conditions. A statement gets rejected if it creates too many key lookups, generates excessive locks, or trips a threshold on a PerfMon counter or DMV. A statement that doesn’t meet the bar gets kicked back to the developer instead of rolling into general availability. SolarWinds® SQL Sentry® software can even mark deployment events directly on the performance timeline. That way, a team can see immediately whether wait stats or other metrics shifted after a release. Catching an anti-pattern in development is dramatically cheaper than fixing the same issue as technical debt in production.

Cut Tool Sprawl With Standard Operating Procedures

Scaling any environment, not only databases, depends on automation and standard operating procedures. A good rule of thumb: if you’ve typed the same query three times, turn it into a script. Scripts are also safer. A tired DBA clicking through a GUI at 3 a.m. is far more likely to restore the wrong database than a script that was already tested.

Community resources make this easier than it used to be. Ola Hallengren’s maintenance scripts and the DBA tools scripts referenced in the webinar offer hundreds of ready-made options. They cover replication, security permissions, and backup and recovery, and they can meaningfully speed up repetitive work.

Tool sprawl deserves attention too. Picture a five-person DBA team juggling Oracle OEM, SQL Server Management Studio, and a handful of other platform-specific tools. That spreads the team’s cognitive load thin. Nobody becomes proficient in all of them, and inconsistent assumptions across tools make it harder for team members to help each other. Reducing the number of tools, and consolidating onto a shared dashboard where possible, keeps a team aligned around the same data and the same assumptions.

Three Capabilities That Scale DBA Coverage

Some of these habits can be built in-house. A few capabilities are much harder to replicate without purpose-built tooling. We call these three the “superpowers” for scaling database coverage.

Multiplatform observability. Monitoring across physical servers, virtual machines, and cloud-managed instances adds complexity at every layer. Bringing that complexity into a single pane of glass is what makes multi-platform scale manageable in the first place, rather than juggling separate tools per platform.

AI-driven anomaly detection. Every relational database generates wait statistics. These are the record of what’s slowing transactions down, whether that’s a missing memory grant, lock contention, or something else entirely. The challenge isn’t collecting that data. It’s knowing what counts as normal for a given day, time, and workload, since normal is highly circumstantial. A Tuesday afternoon slowdown might be routine. The same slowdown on a different day might be the first sign of a real problem. Machine learning-based anomaly detection goes further than static thresholds or standard deviations, and it helps separate the two automatically.

Evidence-based root cause analysis. Keeping history to build forecasts and baselines helps replace guesswork with concrete evidence. Correlating that history with wait statistics also helps end the old “blame game” between teams. That evidence is what shortens mean time to resolution and reduces the number of incidents that escalate to P1 severity.

From Alert to Root Cause: A Real Troubleshooting Walkthrough

Ash walked through a live example using DPA. It shows how these capabilities work together. The scenario: the orders department couldn’t process orders starting around 10 a.m. on September 29.

Starting from DPA’s main dashboard, Ash got a cross-platform view of the entire estate, including Amazon, Azure, DB2, SQL Server, Postgres, and SAP HANA instances. The top SQL chart showed a 30-day snapshot of query activity. The real signal came from the anomaly detection chart underneath it. September 29 wasn’t the busiest day in the window, but it was flagged as anomalous. DPA had already learned the instance’s normal wait-time patterns, and this day didn’t fit them.

Drilling into the 10 a.m. hour narrowed the investigation to the specific queries driving wait time. From there, multidimensional analysis let Ash pivot the data by wait category, by program, and by the user tied to the orders complaint. That pivot showed the affected session was blocked 100% of the time.

Rather than stopping at “this query is slow,” DPA’s blockers view identified the connection causing the block, down to the program, machine, and SQL text behind it. The root cause was a connection from a user who shouldn’t have had that level of access. The next step: alert on future activity from that user or query, and fix the access issue before it causes another outage.

That’s what evidence-based troubleshooting makes possible. A generic complaint (“orders are slow”) became a specific query, time window, and blocking connection, all without manually correlating logs across tools.

What High-Performance DBAs Do Differently

The DBAs who manage large, multi-platform estates without burning out share a few habits. They:

  • Treat database code with the same version control discipline as application code
  • Push SQL performance checks into the development cycle instead of catching problems in production
  • Automate repetitive tasks and limit the number of tools the team depends on
  • Monitor wait statistics across every platform in one place, rather than per-tool
  • Let anomaly detection flag what’s genuinely abnormal instead of chasing every alert
  • Build evidence first, then act, rather than guessing where to look

None of this requires a bigger team. It requires a shift from reactive firefighting to proactive, evidence-based operations that scale database coverage without adding headcount. It’s a shift most teams can make with the tools they already have.

Frequently Asked Questions

How many database platforms does a typical DBA manage? Enterprise and large midsize organizations generally report managing three to five different database platforms. This pattern spans roughly 65% of the global Fortune 1000.

What is AI-driven anomaly detection in a database monitoring context? It’s a technique that learns a database’s normal wait-time patterns, including daily and weekly cycles, and flags activity that falls outside that expected range. This helps DBAs tell a busy-but-normal day apart from a day with a genuine, actionable problem.

Why does multiplatform observability matter if most of my environment runs on one platform today? Estates rarely stay single-platform for long. Bringing physical, virtual, and cloud-managed instances into a single pane of glass early makes it easier to scale coverage later, without re-architecting your monitoring approach.

What’s the fastest way to start shifting from reactive to proactive database management? Start with what’s measurable. Put your database code in version control. Add a quality gate for new SQL before it reaches production. Then reduce the number of monitoring tools your team depends on day to day.

Which SolarWinds tool fits my environment? DPA is designed to help teams monitoring a heterogeneous mix of relational and non-relational platforms. SQL Sentry is built for teams that need to go deep on the Microsoft data platform. SolarWinds® Database Observability software is a SaaS option. It’s designed to help cover cloud-native and NoSQL platforms like MongoDB and Redis alongside relational databases.

Resources

Ready to Go Deeper on AI for DBAs?

If you want hands-on guidance on applying AI to your own database environment, watch the on-demand AI Masterclass for DBAs to learn practical techniques you can put to work right away.

Tags:
database monitoring