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Load Keypay data to DuckDB

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

SourceKeypayEmployment Hero Payroll API Reference: Specifications listDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Employment Hero Payroll (formerly KeyPay) is a REST API for managing businesses, employees, pay runs, leave, timesheets, super, tax, and reporting across multiple payroll regions. Everything needed to build a working Keypay → 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 Keypay 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 Keypay 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 Keypay 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.


Keypay API at a glance

Base URLhttps://api.yourpayroll.com.au/api/v2
Example endpointGET api/v2/business/{businessId}/employee/unstructured
AuthenticationAPI requests can be authenticated using HTTP Basic Authentication or OAuth 2.0 Bearer tokens — sent in the Authorization header, prefixed Bearer
PaginationNot paginated
API referencehttps://api.keypay.com.au/guides/OAuth2.html

These values come from the Keypay API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Keypay API?

KeyPay supports both HTTP Basic Authentication and OAuth 2.0. For Basic Auth, use the API key as the username with a blank password; for OAuth 2.0, provide the access token in the Authorization: Bearer header.

1. Get your credentials

Log into your KeyPay (Employment Hero Payroll) dashboard and click on your name in the top right corner. Select 'My Account' from the menu. Within the account settings, click 'Generate API Key' to create or refresh your key. Copy and store this key securely; note that generating a new key will invalidate any existing one.

2. Add them to .dlt/secrets.toml

[sources.keypay_source] api_key = "your_generated_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 Keypay data can I load into DuckDB?

These are the Keypay endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
businessesapi/v2/businessGETList all businesses
employeesapi/v2/business/{businessId}/employee/unstructuredGETList employees for a business
pay_runsapi/v2/business/{businessId}/payrunGETList pay runs for a business
pay_run_totalsapi/v2/business/{businessId}/payrun/{payRunId}/totalsGETList totals for a specific pay run
work_typesapi/v2/business/{businessId}/worktypeGETList work types for a business

How do I load only new Keypay records?

The Keypay 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": "employees", "endpoint": { "path": "api/v2/business/{businessId}/employee/unstructured", # 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 Keypay pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v2/business and /api/v2/business/{businessId}/employee from the Keypay API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def keypay_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.yourpayroll.com.au/api/v2", "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key}, }, "resources": [ {"name": "employees", "endpoint": {"path": "api/v2/business/{businessId}/employee/unstructured"}}, {"name": "pay_runs", "endpoint": {"path": "api/v2/business/{businessId}/payrun"}} ], } yield from rest_api_resources(config) def load_keypay_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="keypay_pipeline", destination="duckdb", dataset_name="keypay_data", ) load_info = pipeline.run(keypay_source()) print(load_info) if __name__ == "__main__": load_keypay_to_duckdb()

Run it with python keypay_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 Keypay 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("keypay_pipeline").dataset() df = data.employees.df() print(df.head())

SQL:

SELECT * FROM keypay_data.employees LIMIT 10;

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


How do I deploy the Keypay 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 Keypay 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 Keypay 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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