DATA OPS
BigQuery schema diff PR gate that posts a GitHub status check
On every pull request that touches BigQuery DDL files, diffs the proposed table definitions against the live dataset and posts a pass/fail GitHub check that blocks merge…
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub PR opened touching DDL filesGitHub
- LogicParse proposed column definitions from diff
- ActionFetch live BigQuery table schemaBigQuery
- LogicClassify proposed vs live as safe or breaking
- OutputPost GitHub status check and PR commentGitHub
What it does
Turns warehouse schema changes into a reviewable, gated step. When a PR modifies BigQuery DDL or dbt model schemas, it compares what the PR proposes against the actual live table definitions and reports a GitHub commit status check — green for safe migrations, red with a comment for breaking ones.
When to use it
Use it when analytics engineers ship schema changes through pull requests and you want a guardrail that fails the PR before a destructive `ALTER` reaches production. It moves drift detection left, into code review, instead of catching it after deploy.
How it works
- 1A GitHub pull_request webhook triggers when DDL paths change.
- 2Parse the proposed column definitions from the changed files.
- 3Query BigQuery for the current live schema of each affected table.
- 4Diff proposed vs live and classify any breaking changes.
- 5If breaking, post a failing GitHub check plus a PR comment listing the dropped or retyped columns; if safe, post a passing check.
- 6The status check gates the merge.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, 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.