BigQuery

Google BigQuery is a fully managed, serverless data warehouse that lets you run fast SQL queries on large datasets. It's ideal for analytics, reporting, and data pipelines — and it scales automatically as your data grows.

Using BigQuery as a Coupler.io destination lets you load structured data from any supported source directly into your data warehouse, without writing any ETL code.

Why use BigQuery as a destination?

• Centralize your data — pull from marketing tools, CRMs, spreadsheets, and more into a single place for SQL-based analysis

• Automate pipelines — schedule data loads on any cadence without manual exports or scripts

• Works with any Coupler.io source — any source Coupler.io supports can be routed to BigQuery

• Flexible schema control — let BigQuery auto-detect column types, or define your own schema manually for full control

• BI-ready — data lands in BigQuery ready for Looker Studio, Power BI, or any SQL-compatible analytics tool

Prerequisites

Before you start, make sure you have:

• A Google Cloud Platform account with a BigQuery project and dataset already created

• A Service Account with the correct IAM roles (see Permission errors if you're unsure)

• A JSON key file downloaded from your GCP Service Account — this is how Coupler.io authenticates with BigQuery

Quick start

Have your GCP JSON key file ready before you begin. You'll upload it during the connection step. If you haven't created one yet, follow Coupler.io's guide on generating a Google Cloud JSON key file first.

How to connect

1

Add a source to your data flow. In Coupler.io, create a new data flow and configure at least one source. This can be any supported integration — Airtable, Facebook Ads, Clockify, and so on. You must have a source connected before you can save and run the data flow.

2

Select BigQuery as your destination. In the Destination step of your data flow, choose BigQuery from the list of available destinations.

3

Upload your JSON key file. Click Select file and upload the JSON key file you downloaded from your GCP Service Account. This file authenticates Coupler.io with your BigQuery project. Once uploaded, click Save to establish the connection.

4

Set your dataset and table name. Enter the Dataset name exactly as it appears in BigQuery (for example, my_analytics_dataset). Then enter a Table name — you can use an existing table or type a new name, and Coupler.io will create it automatically on the first run.

5

Configure the schema (optional but recommended). By default, BigQuery auto-detects column types from your data. If you need precise type control — or if your source sometimes returns empty datasets — disable the Autodetect table schema toggle and enter your schema as JSON. See the schema definition guide for the format.

6

Choose a write mode. Select Replace to overwrite the table with fresh data on every run, or Append to add new rows below existing data. Replace is best for snapshots; Append is best for building historical logs.

7

Run the data flow. Click Save and Run. Coupler.io will load your data into BigQuery. Once the run completes successfully, open BigQuery to verify your table and data.

Supported features

Feature	Supported
Replace mode	Yes
Append mode	Yes
Automatic scheduling	Yes
Type enforcement (manual schema)	Yes
OAuth sign-in	No (JSON key file required)
Templates	No