> ## Documentation Index > Fetch the complete documentation index at: https://docs.textql.com/llms.txt > Use this file to discover all available pages before exploring further. # Data Quality & Validation Deep-Dive > A workshop of short, hands-on modules for data engineers and analytics engineers who need to trust — and prove — answer accuracy. The thesis throughout: accuracy is checked, not...

A workshop of short, hands-on modules for data engineers and analytics engineers who need to trust — and prove — answer accuracy. The thesis throughout: accuracy is checked, not asserted. Golden datasets pinned to numbers the org already trusts, eval cases that exercise edge conditions, drift detection on a schedule, reconciliation workflows for when systems disagree, and a validation agent that runs the moment data lands.

Total time: about 2 hours.

Builds on Build Your Ontology, End to End and Ontology Operations — this workshop assumes governed definitions exist and doesn't re-teach the build; it teaches the proof.

## Who this is for

Data/analytics engineers accountable for "is this number right?"
Ontology owners who need tests, not vibes, behind their definitions
Teams burned by a silent upstream change that shipped wrong numbers for a week
Anyone preparing for an audit, a board cycle, or a regulator

## What you'll be able to do

After module	You can...
0	Map every way an answer goes wrong to the defense that catches it
1	Build golden queries pinned to numbers the org already trusts
2	Generate and curate eval cases that exercise definitions and edge cases
3	Run drift detection on a schedule, distinguishing data drift from definition drift
4	Reconcile two disagreeing systems to the exact fork, and record the resolution
5	Deploy a data-quality agent triggered by ETL completion
6	Write the quality runbook: owners, cadences, and what a red alert triggers

## Workshop modules

Module	Time
0 · The Trust Stack	10 min
1 · Golden Datasets	20 min
2 · Validation Sets & Eval Cases	20 min
3 · Drift Detection on a Schedule	20 min
4 · Reconciliation Workflows	20 min
5 · The Data-Quality Feed Agent	15 min
6 · The Quality Runbook	15 min

## How to use this workshop

Prerequisites: a connected warehouse and an existing ontology with governed metrics (the build workshops above if not).
Work in order — golden datasets (Module 1) are the foundation every later module schedules, extends, or operationalizes.
Swap \[bracketed] placeholders for your metrics, tables, and trusted sources.

T-minus-1-day pre-flight: run the workshop's key prompts end-to-end in the session workspace — confirm logins, connectors, and the features this workshop touches are enabled for every attendee; have a fallback demo workspace ready in case a customer connector fails live. Pacing: when a long prompt is running (2–4 min), fire it first, then discuss the concept while Ana works — never watch a spinner in silence. If behind schedule, cut sections marked optional, never the checkpoints. Group sessions: attendees drive, you narrate; collect every miss or wrong answer in a shared doc — that list is the ontology backlog. With 10+ attendees on one warehouse, stagger the heavy prompts.