Load Pega Platform data to DuckDB

Pega Platform REST API is a suite of services for managing and interacting with application components such as cases, assignments, and data integration. Everything needed to build a working Pega Platform → 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 Pega Platform 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 Pega Platform 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 Pega Platform 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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Pega Platform API at a glance

These values come from the Pega Platform API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Pega Platform API?

Authentication is typically performed using OAuth 2.0 via a Bearer token in the Authorization header or HTTP Basic Authentication. When using OAuth 2.0, the Authorization header follows the format 'Authorization: Bearer ', where the token is a Pega-issued Authorized Access Token (AAT) in JWT format.

1. Get your credentials

1. Log in to your Pega Platform instance and switch to Dev Studio. 2. Navigate to the Records explorer and locate Data Model > Data-Admin-Security-OAuth2-ClientRegistration. 3. Create a new OAuth 2.0 client registration instance. 4. In the Client Information tab, select 'Client Credentials' grant type. 5. Associate an Operator ID that has the necessary access privileges. 6. Click 'Save' to generate the Client ID and Client Secret. 7. Click 'View & download' to save your credentials to a text file (this is your only opportunity to view the secret). 8. Use the provided Access token endpoint URL, along with your Client ID and Client Secret, to authenticate and receive an access token.

2. Add them to .dlt/secrets.toml

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[sources.pega_platform_source]
api_key = "REPLACE_ME"

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 Pega Platform data can I load into DuckDB?

These are the Pega Platform endpoints dlt can load into DuckDB:

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How do I load only new Pega Platform records?

The Pega Platform 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": "cases", "endpoint": {
    "path": "api/v1/cases",
    # 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 Pega Platform pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /cases and /assignments from the Pega Platform API into DuckDB:

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

@dlt.source
def pega_platform_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<host>/prweb/api",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "cases", "endpoint": {"path": "api/v1/cases", "data_selector": "cases"}},
            {"name": "data_views_query", "endpoint": {"path": "data_views/{data_view_ID}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_pega_platform_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pega_platform_pipeline",
        destination="duckdb",
        dataset_name="pega_platform_data",
    )
    load_info = pipeline.run(pega_platform_source())
    print(load_info)


if __name__ == "__main__":
    load_pega_platform_to_duckdb()

Run it with python pega_platform_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 Pega Platform 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("pega_platform_pipeline").dataset()
df = data.data_views.df()
print(df.head())

SQL:

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SELECT * FROM pega_platform_data.data_views LIMIT 10;

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

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How do I deploy the Pega Platform 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 Pega Platform 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 Pega Platform 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