# BigQuery Connector Overview

> Source: https://docs.trailspark.ai/docs/bigquery-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

> [!NOTE]
> 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](/docs/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](/docs/connecting-bigquery) — set up a connection to your warehouse
- [Designing Sync Queries](/docs/designing-sync-queries) — write SQL for each feed type, with worked examples
- [Schedules and Run History](/docs/schedules-and-run-history) — schedule syncs and read run results