Flower BPM Python API Docs | dltHub

Flower REST API enables programmatic interaction with Flower, which is currently in beta and experimental. The REST API base URL is The base URL for the Flower BPM REST API is typically the Jira base URL, as Flower uses it for its internal REST API calls. and The Flower REST API supports Basic authentication using Jira API tokens or Personal Access Tokens (PAT) as Bearer tokens for Jira Data Center instances..

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 Flower BPM data in under 10 minutes.

---

What data can I load from Flower BPM?

Here are some of the endpoints you can load from Flower BPM:

---

How do I authenticate with the Flower BPM API?

Authentication for the Flower REST API can be done via Jira Basic Auth using an Atlassian API token, or by providing a Personal Access Token (PAT) as a Bearer token in the API requests for Jira Data Center instances.

1. Get your credentials

To obtain API credentials for Basic Auth, generate an API token from your Atlassian account. For Jira Data Center instances, you must provide a Personal Access Token (PAT), which is typically generated within your Jira Data Center environment.

2. Add them to .dlt/secrets.toml

Copy
[sources.flower_bpm_source]
api_key = "your_api_token_or_pat_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 Flower BPM 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 flower_bpm_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline flower_bpm_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 No specific endpoints are listed in the provided documentation. from the Flower BPM 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 flower_bpm_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL for the Flower BPM REST API is typically the Jira base URL, as Flower uses it for its internal REST API calls.",
            "auth": {
                "type": "http_basic or bearer",
                "password (for Basic Auth) or token (for Bearer)": api_key,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="flower_bpm_pipeline",
        destination="duckdb",
        dataset_name="flower_bpm_data",
    )
    load_info = pipeline.run(flower_bpm_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("flower_bpm_pipeline").dataset()
sessions_df = data.No specific endpoints are listed in the provided documentation..df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM flower_bpm_data.No specific endpoints are listed in the provided documentation. LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("flower_bpm_pipeline").dataset()
data.No specific endpoints are listed in the provided documentation..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 Flower BPM 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 Issues

The Flower REST API uses the same authentication restrictions as the standard Jira REST API. This means that common authentication issues encountered with Jira, such as incorrect API tokens or disabled Basic Authentication, may also apply. For Jira Data Center instances where Basic Authentication is disabled, ensure you are providing a Personal Access Token (PAT) as a Bearer token in your API requests.

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