DATA OPS
Snowflake table freshness SLA monitor with Slack alerting
Checks the last-loaded timestamp of every SLA-tracked Snowflake table on a schedule and posts a Slack alert listing only the tables that have gone stale past their freshness…
How it runs
The automated pipeline, trigger to output.
- TriggerEvery 15 minutes (schedule)
- ActionQuery MAX(loaded_at) per tracked tableSnowflake
- LogicCompute staleness vs each table's SLA
- LogicStop if no tables breached
- OutputPost breached tables to #data-oncallSlack
What it does
Queries Snowflake for the most recent load time of each table you track, compares it against that table's freshness SLA, and alerts your data team in Slack the moment a table falls behind. No more discovering stale dashboards from a confused stakeholder.
When to use it
Run it when you maintain a set of business-critical tables (revenue, sessions, orders) with explicit freshness expectations and want a single, deduplicated alert instead of per-table noise. Ideal for hourly or every-15-minute cadences.
How it works
- 1A schedule fires the run (e.g. every 15 minutes).
- 2A Snowflake query reads `MAX(loaded_at)` per tracked table from your metadata or `INFORMATION_SCHEMA`.
- 3A logic step computes age = now - loaded_at for each table and keeps only those exceeding their configured SLA in minutes.
- 4A second logic step short-circuits the run if the breach list is empty, so healthy cycles stay silent.
- 5A Slack message posts the breached tables, their staleness, and owning team to the data on-call channel.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect SlackChannels, DMs, threads, mentions.
- 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.
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