Workday strategic sourcing Python API Docs | dltHub

Workday Strategic Sourcing is a REST API for accessing and managing Workday Strategic Sourcing (Workday Spend) resources using a JSON:API-compatible interface. The REST API base URL is https://api.us.workdayspend.com and All requests require company and user API headers for authentication..

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 Workday strategic sourcing data in under 10 minutes.

---

What data can I load from Workday strategic sourcing?

Here are some of the endpoints you can load from Workday strategic sourcing:

---

How do I authenticate with the Workday strategic sourcing API?

Authentication uses API keys/tokens via three required HTTP headers on every request: X-Api-Key (company API key), X-User-Token (user personal API token) and X-User-Email (the user's email). Requests must be made over HTTPS and include Content-Type: application/vnd.api+json for JSON:API requests.

1. Get your credentials

1. Log in to your Workday Strategic Sourcing account. 2) Open your Profile page. 3) Navigate to Integrations -> Personal API Tokens (or "API tokens"). 4) Create/generate a new company API key and a user personal API token; copy the values (tokens are shown only once). 5) Use the user email corresponding to the token. 6) Store these securely and use them in the required headers for API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.workday_strategic_sourcing_source]
api_key = "your_company_api_key_here"
user_token = "your_user_token_here"
user_email = "your_user_email_here"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
dlt 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:

Copy
dlt 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 Workday strategic sourcing 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:

Copy
python workday_strategic_sourcing_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline workday_strategic_sourcing_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.

---

Python pipeline example

This example loads projects and events from the Workday strategic sourcing API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def workday_strategic_sourcing_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.us.workdayspend.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "services/projects/v1/projects", "data_selector": "data"}},
            {"name": "events", "endpoint": {"path": "services/events/v1/events", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="workday_strategic_sourcing_pipeline",
        destination="duckdb",
        dataset_name="workday_strategic_sourcing_data",
    )
    load_info = pipeline.run(workday_strategic_sourcing_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.

---

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):

Copy
import dlt

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

SQL (DuckDB example):

Copy
SELECT * FROM workday_strategic_sourcing_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

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

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

---

What destinations can I load Workday strategic sourcing 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.

---

Troubleshooting

Authentication failures

If you receive 401/403 errors, verify all three headers (X-Api-Key, X-User-Token, X-User-Email) are present and correct. Ensure the user token has not been revoked and the user has access to requested resources. Tokens are shown once at creation—if lost, generate a new one.

Rate limiting (429 Too Many Requests)

Rate limits are enforced per-company per-second (5 requests/second). Retry after a short backoff (e.g., sleep 1s) and implement exponential backoff for retries.

Pagination and data completeness

List endpoints default to page[size]=10 and support page[size] up to 100. Paginated responses include "links" (self, next) and a "meta" object. Use the "links.next" URL or page parameters to iterate pages; the collection of records is in the top-level "data" array.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

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

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

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime 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