Load BigQuery data to BigQuery

Google BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse that supports data ingestion and SQL analysis. Everything needed to build a working BigQuery → BigQuery pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your BigQuery to BigQuery pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the BigQuery API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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BigQuery API at a glance

These values come from the BigQuery API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the BigQuery API?

Authentication typically uses OAuth 2.0 with a Bearer token in the Authorization header. Requests require the 'Authorization: Bearer ' header.

1. Get your credentials

BigQuery does not support standard API keys for authentication; you must use a Service Account with Application Default Credentials (ADC). 1. Go to the Google Cloud Console, navigate to IAM & Admin > Service Accounts. 2. Create a service account and assign it the 'BigQuery Data Editor' and 'BigQuery Job User' roles. 3. Click the service account, go to the Keys tab, and click Add Key > Create new key (choose JSON). 4. Download the JSON file. 5. Set the path to this file in your environment using: export GOOGLE_APPLICATION_CREDENTIALS='/path/to/your/service_account.json'.

2. Add them to .dlt/secrets.toml

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[sources.bigquery_source]
credentials = { "project_id" = "your_project_id", "private_key" = "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n", "client_email" = "your_service_account_email@project.iam.gserviceaccount.com" }

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What BigQuery data can I load into BigQuery?

These are the BigQuery endpoints dlt can load into BigQuery:

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How do I load only new BigQuery records?

The BigQuery API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "datasets", "endpoint": {
    "path": "bigquery/v2/projects/{projectId}/datasets",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated BigQuery pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading datasets and jobs from the BigQuery API into BigQuery:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def bigquery_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://bigquery.googleapis.com",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "bigquery/v2/projects/{projectId}/datasets", "data_selector": "datasets"}},
            {"name": "tables", "endpoint": {"path": "bigquery/v2/projects/{projectId}/datasets/{datasetId}/tables", "data_selector": "tables"}}
        ],
    }
    yield from rest_api_resources(config)


def load_bigquery_to_bigquery() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bigquery_pipeline",
        destination="bigquery",
        dataset_name="bigquery_data",
    )
    load_info = pipeline.run(bigquery_source())
    print(load_info)


if __name__ == "__main__":
    load_bigquery_to_bigquery()

Run it with uv run python bigquery_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query BigQuery data in BigQuery?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("bigquery_pipeline").dataset()
df = data.tables.df()
print(df.head())

SQL:

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SELECT * FROM bigquery_data.tables LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the BigQuery to BigQuery pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw BigQuery loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load BigQuery data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack