BigQuery

The BigQuery pipe streams events out of Active Reach into a BigQuery dataset, so your data team can model engagement, attribution, and revenue alongside everything else in your warehouse.

This is an outbound (Active Reach → BigQuery) pipe.

What gets exported

Engagement and commerce events as they fire — message sends and opens, link clicks, conversions, orders, opt-ins — each with its contact identity, timestamp, and properties. You choose which event types to include.

Connect

Create a service account

In Google Cloud, create a service account with BigQuery Data Editor on the target dataset, and download its JSON key.

Add the pipe

Go to Settings → Data Pipes → New pipe → BigQuery. Paste the service-account JSON and enter the project, dataset, and table to write to (Active Reach creates the table if it doesn’t exist).

Choose events

Select the event types to export and confirm the schema mapping.

Test and turn on

The wizard writes a sample row so you can confirm the dataset, schema, and permissions before going live.

Schema

Each event becomes a row with a stable set of columns (event name, contact id, timestamp, channel) plus a JSON column for event-specific properties — so new properties don’t require schema migrations. Rows stream in near-real-time.

Cost & volume

BigQuery is the right pipe for high-volume, analytical workloads. Streaming inserts incur BigQuery costs on your Google Cloud bill — scope the exported event types to what your analysts actually use.

For lighter exports or quick pivots without a warehouse, use the Google Sheets pipe instead.