Load gspread data to DuckDB

gspread is a Python library that provides a simple interface for interacting with the Google Sheets REST API v4. Everything needed to build a working gspread → DuckDB 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.

---

Build your gspread to DuckDB pipeline

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

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from gspread to DuckDB and run it on dltHub

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 gspread 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.

---

gspread API at a glance

These values come from the gspread API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the gspread API?

gspread uses OAuth2 to access Google Sheets, typically managed via service account credentials or OAuth Client IDs, which handles the necessary Bearer token authentication under the hood when communicating with the Google Sheets API. Header injection is handled automatically by the library and its underlying HTTP client.

1. Get your credentials

1. Go to the Google Cloud Console (console.cloud.google.com) and create or select a project. 2. Navigate to 'APIs & Services' > 'Library' and enable the 'Google Sheets API'. 3. Navigate to 'APIs & Services' > 'Credentials'. 4. To use a Service Account (recommended for pipelines): Click 'Create credentials' > 'Service account'. Follow the prompts, then click on the created account, go to the 'Keys' tab, select 'Add key' > 'Create new key' (JSON format). Download the resulting file. If using User OAuth (for interactive sessions), select 'OAuth client ID' instead, choose 'Desktop app', and download the JSON credentials file. 5. Share your target spreadsheet with the service account email address provided in your JSON key file.

2. Add them to .dlt/secrets.toml

Copy
[sources.gspread_source]
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.

---

What gspread data can I load into DuckDB?

These are the gspread endpoints dlt can load into DuckDB:

---

How do I load only new gspread records?

The gspread 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.

Copy
{"name": "values", "endpoint": {
    "path": "spreadsheets/{spreadsheetId}/values/{range}",
    # 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.

---

What does the generated gspread pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading spreadsheets.values.get and spreadsheets.values.update from the gspread API into DuckDB:

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

@dlt.source
def gspread_source(credentials_filename=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sheets.googleapis.com",
            "auth": {"type": "bearer", "token": credentials_filename},
        },
        "resources": [
            {"name": "values", "endpoint": {"path": "spreadsheets/{spreadsheetId}/values/{range}", "data_selector": "values"}},
            {"name": "spreadsheets", "endpoint": {"path": "spreadsheets/{spreadsheetId}", "data_selector": "sheets"}}
        ],
    }
    yield from rest_api_resources(config)


def load_gspread_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="gspread_pipeline",
        destination="duckdb",
        dataset_name="gspread_data",
    )
    load_info = pipeline.run(gspread_source())
    print(load_info)


if __name__ == "__main__":
    load_gspread_to_duckdb()

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

---

How do I query gspread data in DuckDB?

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):

Copy
import dlt

data = dlt.pipeline("gspread_pipeline").dataset()
df = data.values.df()
print(df.head())

SQL:

Copy
SELECT * FROM gspread_data.values LIMIT 10;

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

---

How do I deploy the gspread to DuckDB 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 gspread loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load gspread 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.

---

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