Load Samsung Health data to DuckDB

Samsung Health provides a suite of APIs and SDKs allowing developers to integrate and access health data from Samsung Health, including a portal-based REST API for research platforms and SDKs for Android and wearable device data synchronization. Everything needed to build a working Samsung Health → 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.

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Build your Samsung Health 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 Samsung Health 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 Samsung Health 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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Samsung Health API at a glance

These values come from the Samsung Health API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Samsung Health API?

Authentication uses OAuth 2.0 with the authorization code flow, requiring a bearer token passed in the Authorization header. Applications must exchange an authorization code for access and refresh tokens at a designated Samsung token endpoint.

1. Get your credentials

Samsung Health is not a self-serve platform; you must apply for access to their developer programs via the Samsung Developers portal. The application process requires submitting a formal partnership request, which includes details about your organization, intended use cases, data requirements, and security protocols. Upon approval, which typically takes 5-15 business days, Samsung will provision the required client_id and client_secret credentials for your project. Do not store these credentials in client-side code; keep them secured in your backend environment.

2. Add them to .dlt/secrets.toml

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[sources.samsung_health_source]
client_id = "your_samsung_client_id"
client_secret = "your_samsung_client_secret"

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 Samsung Health data can I load into DuckDB?

These are the Samsung Health endpoints dlt can load into DuckDB:

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How do I load only new Samsung Health records?

The Samsung Health 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": "projects", "endpoint": {
    "path": "projects",
    # 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 Samsung Health pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading account-service/signin and data-query-service/sql from the Samsung Health API into DuckDB:

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

@dlt.source
def samsung_health_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:3030/api",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects"}},
            {"name": "project_tasks", "endpoint": {"path": "projects/{projectId}/tasks"}}
        ],
    }
    yield from rest_api_resources(config)


def load_samsung_health_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="samsung_health_pipeline",
        destination="duckdb",
        dataset_name="samsung_health_data",
    )
    load_info = pipeline.run(samsung_health_source())
    print(load_info)


if __name__ == "__main__":
    load_samsung_health_to_duckdb()

Run it with python samsung_health_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 Samsung Health 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):

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import dlt

data = dlt.pipeline("samsung_health_pipeline").dataset()
df = data.projects.df()
print(df.head())

SQL:

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SELECT * FROM samsung_health_data.projects LIMIT 10;

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

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How do I deploy the Samsung Health 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 Samsung Health 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 Samsung Health 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