When your database performance degrades, diagnosing the root cause is rarely quick or straightforward. Your existing tools might surface metrics like CPU utilization, wait events, and query duration, but then leave you to correlate the data and identify what went wrong. Worse, what first appears to be the root cause can often just be a downstream effect of multiple interrelated issues. Cutting through all that complexity to get to an actionable fix requires deep database expertise, application knowledge, and institutional context.
Database Investigator takes an agentic approach to solving your database performance issues. It draws on Datadog’s context about your database and application, and layers in experience from real-world incidents, to handle the diagnostic heavy lifting of root cause analysis. Engineers can ask questions and get answers in plain language about what broke, why it happened, and how to resolve it. For DBAs, platform teams, and application developers without deep database expertise, this means faster mean time to resolution (MTTR), fewer escalations, and the ability to find and fix performance issues.
In this blog post, we will cover how Database Investigator makes it easy for teams to:
