Load Real Time Data Ingestion Platform data to DuckDB

Real Time Data Ingestion Platform (RTDIP) is a scalable solution for the ingestion, processing, and sharing of high-volume, historical and real-time process data from diverse sources. Everything needed to build a working Real Time Data Ingestion 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 Real Time Data Ingestion 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 Real Time Data Ingestion 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 Real Time Data Ingestion 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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Real Time Data Ingestion Platform API at a glance

These values come from the Real Time Data Ingestion Platform API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Real Time Data Ingestion Platform API?

RTDIP uses Azure Active Directory authentication, requiring an access token passed as an 'Authorization' header. The header must follow the format 'Authorization: Bearer '.

1. Get your credentials

The Real Time Data Ingestion Platform (RTDIP) REST API primarily uses Azure Active Directory (Azure AD) for authentication. To obtain credentials: 1. Register your application in the Azure portal to receive a Client ID and Client Secret. 2. Use the Microsoft identity platform to obtain an OAuth 2.0 access token. 3. This token must be passed in the HTTP Authorization header as a Bearer token (e.g., 'Authorization: Bearer <access_token>'). You can obtain this token programmatically using libraries like azure-identity or by making a POST request to the Microsoft login endpoint (https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token) with your Client ID, Client Secret, and the specific scope for your RTDIP instance.

2. Add them to .dlt/secrets.toml

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[sources.real_time_data_ingestion_platform_source]
access_token = "your_bearer_token_here" # If using a custom authentication configuration in dlt:
# [sources.rtdip_source]
# client_id = "your_client_id"
# client_secret = "your_client_secret"

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 Real Time Data Ingestion Platform data can I load into DuckDB?

These are the Real Time Data Ingestion Platform endpoints dlt can load into DuckDB:

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How do I load only new Real Time Data Ingestion Platform records?

Real Time Data Ingestion Platform exposes updated_at on events, 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": "events", "endpoint": {
    "path": "events",
    "data_selector": "data",
    "incremental": {"cursor_path": "updated_at", "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 Real Time Data Ingestion Platform pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/openapi.json and /docs from the Real Time Data Ingestion Platform API into DuckDB:

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

@dlt.source
def real_time_data_ingestion_platform_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{domain name}/api/",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "events", "endpoint": {"path": "events", "data_selector": "data"}},
            {"name": "sensors", "endpoint": {"path": "sensors", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_real_time_data_ingestion_platform_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="real_time_data_ingestion_platform_pipeline",
        destination="duckdb",
        dataset_name="real_time_data_ingestion_platform_data",
    )
    load_info = pipeline.run(real_time_data_ingestion_platform_source())
    print(load_info)


if __name__ == "__main__":
    load_real_time_data_ingestion_platform_to_duckdb()

Run it with python real_time_data_ingestion_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 Real Time Data Ingestion 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("real_time_data_ingestion_platform_pipeline").dataset()
df = data.events.df()
print(df.head())

SQL:

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SELECT * FROM real_time_data_ingestion_platform_data.events LIMIT 10;

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

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How do I deploy the Real Time Data Ingestion 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 Real Time Data Ingestion 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 Real Time Data Ingestion 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