BigQuery Source Guide

Google BigQuery is a cloud data warehouse where teams store CRM exports, POS transactions, loyalty records, and other first-party datasets. As a SignalSight source, BigQuery lets you pull structured rows on a schedule and route them to the destinations that match your business goal — without building custom ETL pipelines for every ad platform.

• Offline sales & conversions — Send in-store purchases, CRM conversions, or other offline events to Meta, TikTok, Google, and similar platforms for attribution, ROAS optimization, and campaign learning.
• Audience sync — Push hashed customer identifiers to an audience destination such as Meta Custom Audiences so ad platforms can continuously learn from your warehouse data and refine targeting automatically.

The source setup — Google authentication, project/dataset/table selection, update schedule, and field mapping — is the same regardless of which outcome you need. You decide the purpose when you create the tracker and pick a destination.

Get Started

BigQuery sources are created inside the SignalSight tracker wizard or from the Sources step before you connect a destination. The flow is: authenticate with Google, select where your data lives, define how often SignalSight should sync, map columns to SignalSight fields, then attach the source to a tracker with the right destination type.

Create a BigQuery Source

Open Signal → Trackers → Create Tracker, or add a source from the Sources step. Click Add New Source and configure the connection.

Step 1 — Source Type and Usage

1. 1In Select Source Type, choose Google BigQuery.
2. 2Set Usage to match your primary data type. For offline store sales, POS exports, and CRM conversion rows, select Offline.
3. 3Enter a Name to identify this source in your account (for example, SignalSight BQ).
4. 4Click Connect with Google BigQuery to start Google OAuth.

Step 2 — Google Authentication

Sign in with the Google account that has access to your BigQuery project. Grant SignalSight permission to view and manage data in BigQuery. This authorization lets SignalSight read the project, dataset, and table you select in the next steps.

Step 3 — Project and Dataset

1. 1Enter your Project ID — the Google Cloud project that contains your BigQuery dataset.
2. 2Enter the Dataset name where your table is stored.
3. 3Click Next to continue to table and sync settings.

Step 4 — Table and Sync Settings

Select the BigQuery table SignalSight should read and define how often new rows are ingested. These settings control the automated sync that keeps your offline events or audience lists up to date.

1. 1Table — choose the table that holds your customer or transaction data.
2. 2Data Update Period — how often SignalSight checks for new data (for example, Daily).
3. 3Data Update Method — Incremental Update processes only new or changed rows since the last sync; use a full refresh method when your table is fully replaced each run.
4. 4Schedule Start Date & Time — when the first scheduled sync should run.
5. 5Click Save to store these settings and proceed to field mapping.

Step 5 — Field Mapping

Map each BigQuery column to the corresponding SignalSight property. Correct mapping ensures events and audience records reach ad platforms with the right identifiers and attributes.

1. 1In the Value field, enter the BigQuery column name (for example, hashed_email or hashed_phone).
2. 2In Property, select the matching SignalSight field — Email, Phone Number, transaction amount, event name, or other supported properties.
3. 3Click Add New Row for each additional column you need to forward.
4. 4Click Save when mapping is complete.

Create a Tracker

With the BigQuery source saved, create a tracker to connect it to a destination. The destination type you choose determines whether data flows as offline conversions or as audience updates.

Step 1 — Select Source

In the tracker wizard, open Select Source(s) and check your Google BigQuery source. Click Continue to move to destination selection.

Step 2 — Choose a Destination by Use Case

This is where you define what SignalSight does with your BigQuery data. Pick the destination that matches your goal:

Use Case — Offline Sales & Conversions

When your BigQuery table contains in-store purchases, POS receipts, CRM won deals, or other offline conversion events, connect the source to an offline events destination — such as Meta Offline Conversions, TikTok Offline Events, or Google offline imports configured on your tracker.

• Map transaction fields (amount, currency, store ID, event time) during the mapping step so platforms can attribute revenue correctly.
• Scheduled incremental syncs keep campaign learning current as new sales land in BigQuery.
• See Offline Events API and TikTok Offline Events API for payload and event requirements.

Use Case — Audience Sync & Learning

When your goal is to feed customer lists into ad platforms for retargeting, lookalike seeding, or ongoing audience learning, choose an audience destination instead of an offline events destination. SignalSight reads hashed identifiers from BigQuery on your schedule and adds or updates records in the target audience automatically — no manual CSV uploads.

1. 1In Select Destination Type, choose an audience integration such as Meta Custom Audiences.
2. 2Authenticate with the ad platform and select the Ad Account and Custom Audience (or create a new audience).
3. 3Set the Data Strategy — for example, Add to append new users to an existing audience on each sync.
4. 4Save the destination and complete the tracker. SignalSight handles recurring syncs from BigQuery to keep the audience fresh for platform learning.

Step 3 — Name and Complete

Give your tracker a descriptive name, review the source and destination summary, and click Complete. You can return to the tracker at any time to adjust mapping, change sync schedules, or switch destinations for a new use case by creating a separate tracker against the same BigQuery source.

Manage Your Source and Tracker

• Edit Mapping — update column-to-property mappings when your BigQuery schema changes.
• Sync schedule — adjust update period, method, or start time from the source settings.
• History — review delivery logs on the tracker detail page to confirm rows are syncing as expected.
• Multiple trackers — reuse one BigQuery source across separate trackers if you need both offline conversion reporting and audience sync from the same table.