Application monitoring tells you when a performance problem leads back to the database. It won't tell you which of hundreds of queries deserves attention, or what to change once you've found it.
AI could close that gap, but only if you can trust the answer. An LLM can suggest an optimization, and plausible isn't enough to justify changing a production database. The challenge is proving a fix works before you apply it.
We'll cover what a trace tells you, where database-level visibility picks up, and how Datadog Bits Database Optimizations continuously detects and validates fixes before surfacing them, so the database stops being the bottleneck.
What you'll take away:
- Where application monitoring ends and database visibility begins, and why a timed-out query is where most teams run out of recourse.
- Why optimization stalls: Prioritizing the queries worth fixing takes expertise most teams don't have on hand, so the work waits for an incident.
- What separates a validated optimization from a plausible one, tested against a realistic version of your own workload.
- What changes when the queries worth fixing surface continuously rather than after an endpoint times out.
Why you should attend:
- The trace stops at the database. You can see that the database is at fault and still have no idea which query or why. We cover how Datadog Database Monitoring fills that gap.
- Database expertise isn’t a given. Application developers write the queries; a small platform or database team owns the consequences. We cover how findings in context are surfaced so they can act on them.
- Optimization gets deprioritized because nothing is technically broken. Slow isn't down, so it loses to the roadmap until it becomes an incident. We cover what continuous detection changes about that trade-off.
- Nobody wants to change a production database on a hunch. We cover how validating a fix against a realistic version of your workload turns a suggestion into something a reviewer can approve.
- Change volume outpaces review capacity. Developers ship faster than any team can review by hand, and AI-assisted development has increased that volume rather than reduced it. We cover how detection keeps up.
Your Speakers
Alex Weisberger Senior Software Engineer, Datadog
Alex works on the Database Monitoring team, where he helped build Bits Database Optimizations.
Sophie Bymark Product Marketing Manager, Datadog
Sophie is a Product Marketing Manager at Datadog focusing on Database Monitoring.
