Load Tidal Software data in Python using dltHub

Tidal Software (Tidal Automation) REST API provides endpoints for managing and extending Tidal Automation objects and features through a Client Manager interface. The REST API base URL is http://<ClientManager server hostname>:<port>/api/ and Token-based authentication using a Session ID and Session Token obtained via an initial authentication request..

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 Tidal Software data in under 10 minutes.

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What data can I load from Tidal Software?

Here are some of the endpoints you can load from Tidal Software:

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How do I authenticate with the Tidal Software API?

Authentication is performed by POSTing credentials to the /auth/authenticate endpoint to obtain a Session ID and Session Token, which are then passed in the Authorization header.

1. Get your credentials

To obtain credentials for Tidal Automation (enterprise workload automation), navigate to the Administration menu in the Tidal UI and select API Tokens. Use the 'Add' button to create a new token, which can then be downloaded as a text file (Tidaltoken.txt). Alternatively, for program-based authentication, use the /auth/key and /auth/authenticate endpoints as described in the official TA REST API reference guide. Note: Ensure the BindUser and BindPassword are configured in the clientmanager.props file if using token-based authentication.

2. Add them to .dlt/secrets.toml

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[sources.tidal_software_source]
api_token = "your_token_from_tidaltoken_txt_here"
# or if using credential-based auth:
# username = "your_username"
# password = "your_password"

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 Tidal Software 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 tidal_software_pipeline.py

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

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Pipeline tidal_software_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tidal_software_data
The duckdb destination used duckdb:/tidal_software.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 /auth/key and /auth/authenticate from the Tidal Software 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 tidal_software_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<ClientManager server hostname>:<port>/api/",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "jobs", "endpoint": {"path": "api/jobs"}},
            {"name": "job_runs", "endpoint": {"path": "api/jobruns"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tidal_software_pipeline",
        destination="duckdb",
        dataset_name="tidal_software_data",
    )
    load_info = pipeline.run(tidal_software_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("tidal_software_pipeline").dataset()
sessions_df = data.jobs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM tidal_software_data.jobs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tidal_software_pipeline").dataset()
data.jobs.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 Tidal Software 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