DATA OPS
Weekly scheduled BigQuery extract with standing masking rules to Dropbox
On a weekly schedule, runs a saved BigQuery report, applies standing PII masking rules, and drops the timestamped file into a partner Dropbox folder.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionRun saved report query in BigQueryBigQuery
- LogicApply standing PII masking rules
- ActionWrite date-stamped file to DropboxDropbox
- OutputAnnounce delivery in SlackSlack
What it does
Delivers a recurring data feed without anyone touching it. Each week it runs a saved BigQuery report, applies a fixed set of masking rules to known sensitive columns, and writes a date-stamped file to a Dropbox folder shared with an external partner. A Slack note announces each delivery.
When to use it
Use it for standing data-sharing agreements where the same masked extract goes to a partner or downstream team on a cadence. It removes the manual weekly pull and guarantees the same masking is applied every time.
How it works
- 1A weekly schedule fires the workflow.
- 2BigQuery runs the saved report query for the new period.
- 3A masking step applies the predefined PII rules to the configured columns.
- 4The masked extract is written to Dropbox with a YYYY-MM-DD filename.
- 5A Slack message posts to the data-sharing channel confirming the file and row count.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect DropboxFiles and folders.
- 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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