DATA OPS
Datadog cost export to BigQuery warehouse
Exports Datadog's daily attributed usage and cost into a partitioned BigQuery table so finance can build long-term trend dashboards and per-team chargeback reports.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionPull Datadog usage and cost by product/tagDatadog
- LogicFlatten and normalize into warehouse rows
- ActionUpsert partition into BigQuery cost tableBigQuery
- OutputPost load summary to SlackSlack
What it does
This workflow makes Datadog spend queryable. Each day it extracts attributed usage and estimated cost, normalizes it into a flat row schema (date, team, product, units, cost), and appends it to a date-partitioned BigQuery table. Finance and FinOps then build trend dashboards and chargeback reports on top of warehouse data instead of scraping the Datadog UI.
When to use it
Use it when you need durable, historical cost data — month-over-month trends, budget-vs-actual, and per-team chargeback — that lives beyond Datadog's own retention and joins cleanly to other cloud spend in your warehouse.
How it works
- 1A daily schedule triggers after the prior day closes.
- 2The Datadog action pulls usage and cost broken down by product and tag.
- 3A logic step flattens and normalizes the response into typed warehouse rows, attaching the ingestion date partition key.
- 4A BigQuery action upserts the rows into the partitioned cost table, replacing the day's partition to stay idempotent on re-runs.
- 5The output step posts a load summary (rows written, total cost, partition) to Slack for the data team.
Set it up
What you configure once, before turning it on.
- 1Connect DatadogMetrics, traces, log search.
- 2Connect BigQueryDatasets, queries, schemas.
- 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.
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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.
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