DATA OPS
Profile uploaded CSV for anomalies before gating the Snowflake load
When a CSV is dropped to S3, it computes profiling stats (null rates, distinct counts.
How it runs
The automated pipeline, trigger to output.
- TriggerNew CSV uploaded to S3AWS S3
- ActionCompute profiling metrics and fetch baselines
- LogicEvaluate metrics against thresholds; pass or hold
- ActionAuto-load within-tolerance files into SnowflakeSnowflake
- OutputSend Slack approval request for anomalous filesSlack
What it does
Profiles each incoming CSV before it reaches the warehouse. It measures null rates, distinct-value counts, and total row volume, compares them to recent historical baselines for that feed, and only auto-loads into Snowflake when every metric is within tolerance. Suspicious files are held and routed to a human for an explicit go/no-go in Slack.
When to use it
Use it when a clean schema isn't enough — the file parses fine but a partner sends a half-empty export or a 10x volume spike. This catches statistical anomalies that schema validation misses, before they corrupt downstream dashboards.
How it works
- 1An S3 object-created event triggers the run.
- 2The pipeline computes profiling metrics for the file and pulls the feed's recent baselines.
- 3A logic step evaluates each metric against its threshold to decide pass or hold.
- 4Within-tolerance files load straight into Snowflake.
- 5Anomalous files trigger a Slack approval message with the metric deltas; an approve reply releases the load, a reject archives the file.
Set it up
What you configure once, before turning it on.
- 1Connect AWS S3Buckets, objects, signed URLs.
- 2Connect SnowflakeWarehouses, queries, shares.
- 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
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.
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 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 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…
dbt orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
Raw Sensor Telemetry Archive to BigQuery
Captures every incoming building sensor reading via webhook, normalizes the payload into a consistent schema.
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.