MARKET RESEARCH
Trend Corpus Snapshot to BigQuery
On a daily schedule, snapshots Brave Search result volume and top titles for a large tracked vocabulary and appends every reading to a BigQuery table so analysts can chart…
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionSweep tracked vocabulary through Brave SearchBrave Search
- LogicNormalize readings into flat rows with timestamp
- OutputAppend rows to BigQuery time-series tableBigQuery
What it does
Each day it sweeps a large list of tracked terms through Brave Search, capturing result volume, top result titles, and the timestamp for every term. Rather than judging trends inline, it appends each reading as a row to a BigQuery table — building the raw time-series corpus that powers emergence-curve dashboards and downstream models.
When to use it
Use it when you want the data layer, not the alert. Analysts and data teams who prefer to compute their own slopes, seasonality, and break-out detection in SQL or a BI tool should run this to keep a clean, growing history of term volume.
How it works
- 1A daily schedule fires.
- 2Brave Search runs across the full tracked vocabulary and returns volume and top titles per term.
- 3A logic step normalizes each reading into a flat row with a run timestamp and validates the payload.
- 4All rows are appended to a BigQuery table for time-series analysis.
Set it up
What you configure once, before turning it on.
- 1Connect Brave SearchWeb, news, image, video search.
- 2Connect BigQueryDatasets, queries, schemas.
- 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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Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.