dbt connector

Set up the dbt connector in Kaivo: authentication, configuration, the 5 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated About 5 hours ago

Kaivo is a fully managed data platform that syncs your dbt data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your data from dbt instead of moving it.

What is the dbt connector

Sync your dbt Cloud runs, projects, and environments into BigQuery with Kaivo to monitor your data pipelines alongside the rest of your data.

Category	Tech
Status	Generally available
Authentication	API key
Setup	Self-service

Getting started with the dbt connector

Sign up for Kaivo and create a workspace.
Connect your dbt account.
Choose which tables to sync.
Wait for the initial sync to finish.
Query your data in BigQuery or your favourite AI or BI tool.

Authenticating dbt

Authenticate with your Token.

Field	Description
Token	A dbt Cloud service token. Create one in dbt Cloud under Account settings → Service tokens with the Read-only permission set, applied to the projects you want to sync. The token is shown only once, when you create it.

Configuring the dbt connector

When you set up the connector, you provide:

Field	Description
Account ID	Your dbt Cloud account ID — the number in your dbt Cloud URL right after `/accounts/` (e.g. `12345`). Also shown in Account settings.
Access URL	The start of your dbt Cloud URL, up to the domain — copy it from your browser's address bar (e.g. `https://cloud.getdbt.com`). The default suits standard multi-tenant accounts; single-tenant or other-region accounts use their own (e.g. `https://abc123.us1.dbt.com`).

Tables and columns synced from dbt

Kaivo syncs 5 tables from dbt into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

How the dbt sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where dbt supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.

Frequently asked questions

How long does the initial sync take for dbt?

It depends on how much history is in your dbt account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.

Can I sync only some tables or columns?

Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.

What happens when dbt's schema changes?

New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in dbt, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.

Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in dbt instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the dbt connector in Kaivo and all of its synced data is deleted with it.

Common use cases for dbt data

Run monitoring

Track runs over time to monitor build success, failures, and run duration.

Project health

Analyse runs by project and environment to find slow or failing models.

Team activity

Use users and projects to see who is running what across your dbt account.

Reliability trends

Report on run outcomes to measure pipeline reliability over time.

Use dbt data in your AI and BI tools

Once dbt data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with dbt data.

Claude

Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →

Power BI

Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →

Data Studio

Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →

Tableau

The premium analytics standard, with native BigQuery integration. Setup guide →

Google Sheets

Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →

Excel

Connect via Power Query's BigQuery connector. Setup guide →

Metabase

Open-source BI tool with strong BigQuery support. Setup guide →

See our pricing page for dbt connector pricing and plan details.

Adform: Sync Adform to BigQuery.
Amplitude: Sync Amplitude to BigQuery.
Auth0: Sync Auth0 to BigQuery.
Convex: Sync Convex to BigQuery.
GitHub: Sync GitHub to BigQuery.
GitLab: Sync GitLab to BigQuery.