DATA OPS
Open a Linear ticket when a BigQuery slot spike is traced to one author
On a webhook from BigQuery slot-usage alerting, identifies the single author responsible for the spike and files a Linear ticket assigned to them with the offending job, slot…
How it runs
The automated pipeline, trigger to output.
- TriggerWebhook from slot-usage alertHTTP webhook
- ActionQuery JOBS in spike window by slot_msBigQuery
- LogicBranch on single-author dominance share
- ActionGenerate remediation suggestion from query shape
- OutputCreate assigned Linear ticket with fixLinear
What it does
When reservation slot utilization breaches its alert threshold in real time, this workflow finds the one query author whose job caused the spike and turns the incident into a tracked, owner-assigned Linear ticket instead of a transient alert that scrolls away.
When to use it
Use it when slot saturation causes visible query queuing and you need durable ownership, not just a ping. Ideal for platform teams that triage cost-and-performance regressions through their issue tracker and want each spike to have a clear owner and a closing fix.
How it works
- 1A webhook from your slot-usage monitor fires the moment utilization crosses the threshold.
- 2Query `INFORMATION_SCHEMA.JOBS_BY_PROJECT` for the running/just-finished jobs in the spike window, sorted by slot_ms.
- 3Branch: if a single author owns more than the dominance share of the spike, proceed; otherwise post a generic channel note and stop.
- 4Generate a concise remediation suggestion (partitioning, clustering, or LIMIT) from the query shape.
- 5Create a Linear ticket assigned to that author with the job ID, slot cost, query text, and the suggested fix.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect LinearIssues, projects, cycles, triage.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Connect HTTP webhookTrigger any URL on agent actions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Data Ops workflows
Snowflake column type-drift sentinel with Linear fix ticket
Snapshots the data types of every column in your tracked Snowflake schemas on a schedule, diffs against the last snapshot.
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.
BigQuery dropped/renamed column sentinel with PagerDuty incident
Detects when a column is dropped or renamed in your governed BigQuery datasets and, because that breaks downstream queries hard, pages the on-call via PagerDuty and posts…
PR-time Snowflake schema contract check on dbt model changes
When a pull request changes a dbt model, it compares the model's declared output columns against the live Snowflake table it will replace and blocks the merge with a GitHub check…
Agent-triaged warehouse drift with impact analysis and runbook update
On a webhook from your warehouse audit log, an agent investigates the changed column, traces which downstream models and dashboards depend on it.
Cross-warehouse replication schema mismatch reconciler
Compares the column shape of mirrored tables between BigQuery and Snowflake and, when a replicated table has drifted out of sync between the two, opens an Asana task for the data…
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.