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Load Google Finance API data to DuckDB

Build a Google Finance API to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Google Finance API API base URL, auth, endpoints, and incremental loading.

SourceGoogle Finance APIGoogle Finance API API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Google Finance is a financial information website that no longer provides a public REST API for developers. Everything needed to build a working Google Finance API → 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 Google Finance API to DuckDB pipeline

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

Prompt
Run uvx dlthub-init@latest to build a pipeline from Google Finance API 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 Google Finance API 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.


Google Finance API API at a glance

Base URLN/A
Example endpointGET none
AuthenticationThe service has no authentication as it is no longer available — sent in the request header
PaginationNot paginated
API referencehttps://web.archive.org/web/20111201024359/code.google.com/apis/finance/docs/2.0/developers_guide_protocol.html

These values come from the Google Finance API API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Google Finance API API?

The Google Finance API was permanently shut down on October 20, 2012, and no official developer API, authentication mechanism, or endpoints exist in 2026.

No credentials required. The Google Finance API API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.


What Google Finance API data can I load into DuckDB?

These are the Google Finance API endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
N/AN/AN/AN/ANo official public REST API exists for Google Finance.
N/AN/AN/AN/AThe legacy Google Finance API was shut down in 2012.
N/AN/AN/AN/AData access is only provided via Google Sheets GOOGLEFINANCE function.
N/AN/AN/AN/AThird-party scrapers simulate API access via web scraping methods.
N/AN/AN/AN/ADevelopers typically use alternative market data providers (e.g., Alpha Vantage, Polygon.io).

How do I load only new Google Finance API records?

The Google Finance API 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.

{"name": "none", "endpoint": { "path": "none", # 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 Google Finance API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading search and historical_data (Note: These are examples based on common third-party service patterns, not Google endpoints). from the Google Finance API API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def google_finance_api_source(): config: RESTAPIConfig = { "client": { "base_url": "N/A", }, "resources": [ {"name": "none", "endpoint": {"path": "none"}} ], } yield from rest_api_resources(config) def load_google_finance_api_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="google_finance_api_pipeline", destination="duckdb", dataset_name="google_finance_api_data", ) load_info = pipeline.run(google_finance_api_source()) print(load_info) if __name__ == "__main__": load_google_finance_api_to_duckdb()

Run it with python google_finance_api_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 Google Finance API 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):

import dlt data = dlt.pipeline("google_finance_api_pipeline").dataset() df = data.none.df() print(df.head())

SQL:

SELECT * FROM google_finance_api_data.none LIMIT 10;

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


How do I deploy the Google Finance API 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 Google Finance API 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 Google Finance API data to?

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

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

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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