DATA OPS
Agent-triaged schema drift with downstream impact analysis
When nightly drift is detected, an agent traces which dbt models and dashboards depend on the changed columns, writes a plain-English impact summary.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule triggers run
- ActionDiff warehouse schema vs baselineSnowflake
- ActionRead dbt + dashboard definitionsGitHub
- LogicAgent traces lineage and scores blast radius
- OutputFile prioritized Linear impact ticketLinear
What it does
This workflow goes beyond detecting drift — it reasons about consequences. After a scheduled diff finds changed columns in the warehouse, an agent reads the dbt project and BI metadata in your repo to find every model and report that references the affected columns. It then writes a human-readable impact summary ("3 models and the Revenue dashboard break") and files a Linear ticket whose priority reflects how many critical assets are downstream.
When to use it
Use it when a raw drop in a column name tells you nothing about whether it matters. Teams with sprawling dbt DAGs use this to skip the manual lineage tracing and get a ticket that already explains what will break and how urgent it is.
How it works
- 1A nightly schedule triggers the run.
- 2The warehouse schema is diffed against the stored baseline.
- 3On any change, the agent reads the dbt and dashboard definitions from GitHub.
- 4The agent traces lineage to find every downstream consumer of changed columns.
- 5It composes an impact summary and a priority based on blast radius.
- 6It files a scoped, prioritized Linear ticket with the analysis attached.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Data Ops workflows
BigQuery Per-Team Budget Breach Alert to PagerDuty
Tracks month-to-date BigQuery scheduled-query spend per team and, when a team crosses its monthly budget, pages the team's on-call in PagerDuty and snapshots the spend breakdown…
dbt orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
Weekly BigQuery Cost Trend Sheet and Exec Digest
Compiles week-over-week BigQuery scheduled-query cost by owner and dataset into a Google Sheet with trend columns.
Backfill Missing Owner Labels on BigQuery Scheduled Queries
Finds scheduled queries with no owner label, infers the likely owner from creator metadata and target-table lineage, proposes a label.
Daily BigQuery Scheduled-Query Cost Attribution to Owners
Each morning, totals the prior day's on-demand bytes-billed per scheduled query, maps each query to its owner from a label, and posts a per-owner cost leaderboard to Slack.
dbt source freshness watcher with severity-routed alerts
Checks Snowflake loaded-at timestamps against each dbt source's freshness SLA, then routes warnings to Slack and hard breaches to a PagerDuty incident so stale data never…
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.