Load Name HR Management data to DuckDB

Namely is an HRIS platform that provides a REST API for managing employee profiles, groups, and organizational HR data. Everything needed to build a working Name HR Management → 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 Name HR Management 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 Name HR Management 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 Name HR Management 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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Name HR Management API at a glance

These values come from the Name HR Management API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Name HR Management API?

Requests are authenticated by including an Authorization header with a Bearer token. Personal access tokens are generated via the Namely profile settings.

1. Get your credentials

Log in to your Namely dashboard and navigate to the API section within your user profile settings. Click to create a new Personal Access Token. Provide a name for the token, ensure you have the required administrative permissions, and click Create. Copy the generated token immediately, as it will not be displayed again.

2. Add them to .dlt/secrets.toml

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[sources.name_hr_management_source]
api_key = "your_personal_access_token_here"
company_slug = "your_company_slug"

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 Name HR Management data can I load into DuckDB?

These are the Name HR Management endpoints dlt can load into DuckDB:

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How do I load only new Name HR Management records?

Name HR Management exposes page on api/v1/profiles, 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.

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{"name": "namely_profiles", "endpoint": {
    "path": "api/v1/profiles",
    "data_selector": "result",
    "incremental": {"cursor_path": "page", "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 Name HR Management pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v1/profiles and /api/v1/profiles.json from the Name HR Management API into DuckDB:

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

@dlt.source
def name_hr_management_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{company}.namely.com/api/v1",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "namely_profiles", "endpoint": {"path": "api/v1/profiles", "data_selector": "result"}},
            {"name": "servicenow_employees", "endpoint": {"path": "api/sn_hr_core/V1/hr_rest_api/get_usa_employee_profile", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def load_name_hr_management_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="name_hr_management_pipeline",
        destination="duckdb",
        dataset_name="name_hr_management_data",
    )
    load_info = pipeline.run(name_hr_management_source())
    print(load_info)


if __name__ == "__main__":
    load_name_hr_management_to_duckdb()

Run it with python name_hr_management_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 Name HR Management 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("name_hr_management_pipeline").dataset()
df = data.namely_profiles.df()
print(df.head())

SQL:

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SELECT * FROM name_hr_management_data.namely_profiles LIMIT 10;

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

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How do I deploy the Name HR Management 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 Name HR Management 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 Name HR Management 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