Load Celonis Data Job Execution API data in Python using dltHub

Celonis Data Job Execution API allows users to trigger, stop, and monitor data job executions within the Celonis platform. The REST API base URL is https://{tenant}.{realm}.celonis.cloud/integration/api/v2/ and all requests require an Authorization header using Bearer token or AppKey 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 add "dlt[hub]" and start loading Celonis Data Job Execution API data in under 10 minutes.

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What data can I load from Celonis Data Job Execution API?

Here are some of the endpoints you can load from Celonis Data Job Execution API:

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How do I authenticate with the Celonis Data Job Execution API API?

All requests require an 'Authorization' header. The value must follow the format 'Bearer ' or 'AppKey ', where or is your OAuth access token or API key respectively.

1. Get your credentials

To obtain credentials, log in to the Celonis Platform and navigate to your user profile settings or the Applications section to generate a User API Key or an Application API Key. For production environments, it is strongly recommended to use OAuth 2.0 (Client Credentials grant type), which involves creating an OAuth client in the platform to receive a client ID and client secret. Ensure the generated key or client has the 'integration.data-pools' scope and necessary permissions to view and manage the relevant data pool.

2. Add them to .dlt/secrets.toml

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[sources.celonis_data_job_execution_api_source]
api_key = "your_api_key_or_token_here"
# Alternatively for OAuth 2.0:
# client_id = "your_client_id"
# client_secret = "your_client_secret"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Celonis Data Job Execution API 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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uv run python celonis_data_job_execution_api_pipeline.py

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

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Pipeline celonis_data_job_execution_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset celonis_data_job_execution_api_data
The duckdb destination used duckdb:/celonis_data_job_execution_api.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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uv run dlthub 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 /integration/api/v2/data-pools/{poolId}/data-jobs/{jobId}/execute and /integration/api/v1/data-pools/{poolId}/data-models/{dataModelId}/load from the Celonis Data Job Execution API 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 celonis_data_job_execution_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{tenant}.{realm}.celonis.cloud/integration/api/v2/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "job_executions", "endpoint": {"path": "api/jobs/{jobId}/executions"}},
            {"name": "job_execution", "endpoint": {"path": "api/jobs/executions/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="celonis_data_job_execution_api_pipeline",
        destination="duckdb",
        dataset_name="celonis_data_job_execution_api_data",
    )
    load_info = pipeline.run(celonis_data_job_execution_api_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("celonis_data_job_execution_api_pipeline").dataset()
sessions_df = data.job_executions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM celonis_data_job_execution_api_data.job_executions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("celonis_data_job_execution_api_pipeline").dataset()
data.job_executions.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 Celonis Data Job Execution API 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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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