Faster triage, better validation, and more useful operational diagnostics
Data operations teams generally deal with two types of failure.
The first is obvious: a job fails, an SLA is at risk, and someone has to determine what changed and where the problem is.
The second is harder to detect: a job completes successfully but produces incorrect or incomplete data.
This session looks at how AI-assisted operations can help with both.
CloverDX records detailed execution information including row counts, component timings, execution history, and recent project changes. We’ll show how the Assistant can use that information to narrow down a failed run, identify relevant changes, highlight the useful part of the logs, and propose a fix for review.
We’ll also cover techniques for detecting jobs that technically succeeded but produced bad data, including validation rules, reconciliation checks, expected row counts, and workflows that stop questionable data for review.
The session includes live examples of both scenarios, including triage of a failed overnight run.
What to expect from this session?
You’ll see how the information already collected by CloverDX can reduce the time engineers spend investigating production issues.
We’ll also show how to add checks for cases where “the job succeeded” is not enough, and how better diagnostics and validation can improve confidence in the data delivered to downstream users.
The important point is that useful AI triage depends on the quality of the operational data available to it. CloverDX can provide structured execution context rather than asking a generic chatbot to interpret an isolated log file.
Petr Drabek has spent over 14 years with CloverDX, championing customer success and helping customers maximize value from their data integration initiatives.
Pavel Najvar has worked with CloverDX for more than 16 years, engaging in various roles combining R&D, product development, solutions and marketing.
