Google BigQuery Connector

Google BigQuery is a fully-managed, serverless Destination solution that allows you to run super-fast SQL queries on massive datasets in the cloud.

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4.8 on G2

Weld · Data Sources

Google BigQueryYour warehouseLive

tldv_raw.meetings9 columnsSynced

tldv_raw.transcripts7 columnsSynced

sales.commissions6 columnsSynced

google_sheets.worksheet_sheet33 columnsSyncing

Lands in

Agent context over MCP

How it works

Set up in minutes.
No code, no maintenance.

Google BigQuery

Destination

Host

account.google bigquery.com

Database

analytics_db

Overview

Google BigQuery, both ways.
As a source or your warehouse.

Google BigQuery is a fully-managed, serverless Destination solution that allows you to run super-fast SQL queries on massive datasets in the cloud. Weld works with Google BigQuery in both directions: replicate its tables into another warehouse, or use Google BigQuery as the warehouse your other sources load into. Either way the data lands in a clean, documented schema that analysts can query with SQL or dbt, and agents can query over the Weld MCP server.

Agent context

Google BigQuery data schema.
The map your agents read.

Every table, field and relationship Weld syncs from Google BigQuery is served to agents as context over MCP, so they pick the right tables and join on the right keys.

tables: 4
fields: 25

Loading diagram…

google_sheets.worksheet_sheet3

Open the schema explorer Open the full schema explorer

Weld MCP server

Ask your agent.
It already knows Google BigQuery.

Connect Claude, Cursor, ChatGPT or your own agent to Weld over MCP. It reads the Google BigQuery context, finds the tables it needs and queries your warehouse.

Agent contextGoogle BigQuery tables, fields and joins
QuerySQL against your warehouse
OperateCheck, rerun and refresh syncs

Set up the MCP server

Claude CodeOpus 5

ConnectedWeld MCPAgent contextGoogle BigQuerytables4fields25

How many tldv_raw.meetings rows do we have in our Google BigQuery data?

Reading the Google BigQuery context first, so the query uses the right tables and keys.

●weld·search_available_references

⎿raw.bigquery.tldv_raw.meetings

+ 3 more Google BigQuery tables

●weld·get_reference_schema

⎿tldv_raw.meetings · 9 columns

primary key id

●weld·run_query

⎿select count(*) from {{raw.bigquery.tldv_raw.meetings}}

submitted

●weld·get_query_status

⎿DONE

●weld·get_query_result

⎿rows returned

✓Answered from Google BigQuery data in your warehouse

>Ask about your Google BigQuery dataMCP

Destinations

Load Google BigQuery into any warehouse.
Set up in minutes, no code.

Resources

Everything you need.
To get started.

Documentation

Google BigQuery setup guide

Step-by-step instructions to configure and start syncing.

MCP server

Connect your agents

Set up Claude, Cursor, ChatGPT or Codex against your Weld workspace.

Weld Connect API

Build custom connectors

Use the Weld Connect API to build and publish your own connector.

FAQ

Common questions

Answers to frequently asked questions about Weld connectors.

Weld Connect API

Set it up in code.
One REST API.

Use the Weld Connect REST API to create Google BigQuery connections, configure ELT syncs, and manage pipelines entirely through code. Built for embedding, automation, and CI/CD workflows.

Explore Weld Connect API reference

POST/elt_syncs

# Create a Google BigQuery ELT sync
curl -X POST https://connect.weld.app/elt_syncs \
  -H "x-api-key: $API_KEY" \
  -d '{
    "connection_id": "conn_abc123",
    "integration_id": "bigquery",
    "destination_schema_name": "bigquery_data",
    "sync_interval": "0 * * * *"
  }'
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