DATA OPS
Daily warehouse freshness scorecard with Axiom trend logging
Each morning computes a freshness scorecard across all warehouse domains, logs the metrics to Axiom for trend tracking.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily morning schedule
- ActionPull load times + SLA targets by domainBigQuery
- LogicCompute pass rates + rank worst offenders
- ActionIngest freshness metrics for trendsAxiom
- OutputPost freshness scorecard digestSlack
What it does
Produces a once-a-day health report for your warehouse: per-domain freshness pass rates, the tables most often late, and week-over-week movement. It ships the raw metrics to Axiom so you can chart degradation over time, then posts a readable digest to your data channel.
When to use it
Use it for a morning standup artifact and for tracking whether freshness is trending better or worse across teams, rather than reacting to one-off breaches. Pairs well with the real-time SLA monitor for tactical alerts.
How it works
- 1A daily schedule fires before standup.
- 2A BigQuery query pulls last-load times and SLA targets for every table grouped by domain.
- 3A logic step computes pass/fail per table, domain pass rates, and a ranked worst-offenders list.
- 4An Axiom step ingests each table's freshness metric as a timestamped event for long-run trend dashboards.
- 5A Slack digest posts the scorecard with worst tables and biggest improvements versus yesterday.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect AxiomLog streams, queries, dashboards.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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.