Oracle HCM Python API Docs | dltHub

Oracle Fusion Cloud HCM REST API enables integration with Oracle HCM services to view and manage human resources data. The REST API base URL is https://<servername>.fa.<datacenter>.oraclecloud.com/hcmRestApi/resources/latest and requests require either Basic Authentication or an Authorization header with a Bearer token.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Oracle HCM data in under 10 minutes.

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What data can I load from Oracle HCM?

Here are some of the endpoints you can load from Oracle HCM:

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How do I authenticate with the Oracle HCM API?

Oracle HCM REST APIs support Basic authentication (user name and password) and JWT (Bearer token) authentication. Basic authentication requires an Authorization header with a Base64-encoded string, while JWT requires an Authorization header formatted as 'Bearer '.

1. Get your credentials

To obtain credentials for the Oracle HCM REST API, you must have an Oracle Cloud service user account with the appropriate security privileges. 1. Log in to your Oracle Fusion Cloud HCM instance. 2. Navigate to Security Console to manage or assign job roles. 3. Ensure the user is assigned roles with required aggregate privileges (e.g., Human Capital Management Integration Specialist or role with REST service access). 4. For OAuth, create a client application in the identity domain to obtain your Client ID and Client Secret, or use Basic Authentication with your standard Oracle Cloud username and password. Refer to My Oracle Support Note 2060899.1 for enabling API access on your instance.

2. Add them to .dlt/secrets.toml

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[sources.oracle_hcm_source]
oracle_hcm_user = "your_username"
oracle_hcm_password = "your_password"
oracle_hcm_base_url = "https://servername.fa.us2.oraclecloud.com"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI harness:

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dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlthub ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Oracle HCM API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python oracle_hcm_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline oracle_hcm_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset oracle_hcm_data
The duckdb destination used duckdb:/oracle_hcm.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline oracle_hcm_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads /hcmRestApi/resources/11.13.18.05/workers and /hcmRestApi/resources/11.13.18.05/emps from the Oracle HCM API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def oracle_hcm_source(username_password_or_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<servername>.fa.<datacenter>.oraclecloud.com/hcmRestApi/resources/latest",
            "auth": {"type": "bearer", "token": username_password_or_token},
        },
        "resources": [
            {"name": "workers", "endpoint": {"path": "hcmRestApi/resources/11.13.18.05/workers", "data_selector": "items"}},
            {"name": "jobs", "endpoint": {"path": "hcmRestApi/resources/11.13.18.05/jobs", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="oracle_hcm_pipeline",
        destination="duckdb",
        dataset_name="oracle_hcm_data",
    )
    load_info = pipeline.run(oracle_hcm_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("oracle_hcm_pipeline").dataset()
sessions_df = data.workers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM oracle_hcm_data.workers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("oracle_hcm_pipeline").dataset()
data.workers.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Oracle HCM data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack