BigQuery Connector Overview
Overview
The BigQuery connector syncs your product data straight from your data warehouse into TrailSpark — no webhook code required. You point it at SQL you already trust, and it keeps three kinds of data up to date on a schedule:
- Product events — the things users do in your product (signed in, created a project, hit a usage limit, and so on)
- Users — who your users are and what you know about them
- Accounts — the companies using your product, their plan, and their usage
Everything that comes in through this connector flows through the exact same pipeline as our other integrations — the same lead matching, scoring, and role detection you already use for data from Segment, your CRM, or any other source.
You'll find it under Settings > Integrations > BigQuery.
When to choose the BigQuery connector
The BigQuery connector is the right fit when:
- Your product data already lives in a data warehouse, and you'd rather write SQL against tables you trust than stand up a webhook or tracker
- You don't have Segment or another product-analytics tracker sending events to TrailSpark directly
- You want a single, scheduled sync instead of a live event stream — a sync runs hourly or daily rather than in real time
The BigQuery connector runs alongside a CRM connection and any other signal sources you already use. It doesn't replace them — it's simply another way for data to reach TrailSpark.
The three feed types at a glance
Every sync you create is one SQL query pointed at one of three feed types:
| Feed type | One row is... | Feeds |
|---|---|---|
| Product events | A single thing a user did (an event) | Lead scoring, buying-group role attribution |
| Users | A person using your product | Lead profiles and attributes |
| Accounts | A company/organization using your product | Account profiles, plan and usage details |
Each feed type has its own required and optional columns — see Designing Sync Queries for the full column-by-column breakdown and worked SQL examples.
How it fits with everything else
Once a row comes in through a Product events, Users, or Accounts sync, TrailSpark treats it the same way it treats data from any other source: it's matched to a lead and account, scored, and (for product events) used to attribute buying-group roles like Champion or Economic Buyer. There's nothing BigQuery-specific about how that data is used downstream — the connector's only job is getting your warehouse data in reliably, on a schedule, without duplicates.
Next Steps
- Connecting BigQuery — set up a connection to your warehouse
- Designing Sync Queries — write SQL for each feed type, with worked examples
- Schedules and Run History — schedule syncs and read run results