Load Foursquare data to DuckDB

Foursquare provides a suite of APIs for places, location, and developer services that allow for integrating location data into applications. Everything needed to build a working Foursquare → 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 Foursquare 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 Foursquare 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 Foursquare 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.

---

Foursquare API at a glance

These values come from the Foursquare API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Foursquare API?

Authenticating against the Foursquare REST API is done by passing a service key or access token as a Bearer token in the Authorization header. Specifically, the header should be 'Authorization: Bearer '.

1. Get your credentials

1. Sign up for a Foursquare Developer Account at the official Developer portal (https://foursquare.com/developer/). 2. Once logged in, navigate to the Developer Console. 3. Create or select a Project. 4. Within your project's Settings page, locate the 'Service API Keys' section. 5. Click 'Generate Service API Key', provide a name, and copy the key immediately as it will not be visible again.

2. Add them to .dlt/secrets.toml

Copy
[sources.foursquare_source]
api_key = "your_service_api_key_here"

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 Foursquare data can I load into DuckDB?

These are the Foursquare endpoints dlt can load into DuckDB:

---

How do I load only new Foursquare records?

Foursquare exposes cursor on places/search, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

Copy
{"name": "places_search", "endpoint": {
    "path": "places/search",
    "data_selector": "results",
    "incremental": {"cursor_path": "cursor", "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 Foursquare pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /places/search and /users/self/checkins from the Foursquare API into DuckDB:

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

@dlt.source
def foursquare_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://places-api.foursquare.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "places_search", "endpoint": {"path": "places/search", "data_selector": "results"}},
            {"name": "venue_search", "endpoint": {"path": "venues/search", "data_selector": "response.venues"}}
        ],
    }
    yield from rest_api_resources(config)


def load_foursquare_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="foursquare_pipeline",
        destination="duckdb",
        dataset_name="foursquare_data",
    )
    load_info = pipeline.run(foursquare_source())
    print(load_info)


if __name__ == "__main__":
    load_foursquare_to_duckdb()

Run it with python foursquare_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 Foursquare 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("foursquare_pipeline").dataset()
df = data.places_search.df()
print(df.head())

SQL:

Copy
SELECT * FROM foursquare_data.places_search LIMIT 10;

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

---

How do I deploy the Foursquare 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 Foursquare 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 Foursquare 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