Load Jira Service Desk data to DuckDB

Jira Service Management REST API enables programmatic management of service desk projects, requests, and portal configurations. Everything needed to build a working Jira Service Desk → 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 Jira Service Desk 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 Jira Service Desk 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 Jira Service Desk 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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Jira Service Desk API at a glance

These values come from the Jira Service Desk API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Jira Service Desk API?

For basic authentication, requests require an 'Authorization' header with a value of 'Basic <base64_encoded_credentials>'. The credentials must be a string formatted as 'email@example.com:api_token', where the token is an Atlassian account API token.

1. Get your credentials

1. Log in to your Atlassian account at https://id.atlassian.com/manage-profile/security/api-tokens. 2. Click 'Create API token'. 3. Enter a label for the token (e.g., 'dlt-pipeline') and click 'Create'. 4. Copy the token to a secure location, as it will not be displayed again. If you are using a service account, navigate to Atlassian Administration > Directory > Service accounts, select the account, and create the credentials there.

2. Add them to .dlt/secrets.toml

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[sources.jira_service_desk_source]
subdomain = "your-domain"
email = "your-email@example.com"
api_token = "your-api-token-here"

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 Jira Service Desk data can I load into DuckDB?

These are the Jira Service Desk endpoints dlt can load into DuckDB:

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How do I load only new Jira Service Desk records?

Jira Service Desk exposes start on rest/servicedeskapi/organization, 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": "organizations", "endpoint": {
    "path": "rest/servicedeskapi/organization",
    "data_selector": "values",
    "incremental": {"cursor_path": "start", "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 Jira Service Desk pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading issues and search from the Jira Service Desk API into DuckDB:

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

@dlt.source
def jira_service_desk_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://your-domain.atlassian.net/rest/servicedeskapi/",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_token},
        },
        "resources": [
            {"name": "organizations", "endpoint": {"path": "rest/servicedeskapi/organization", "data_selector": "values"}},
            {"name": "request", "endpoint": {"path": "rest/servicedeskapi/request", "data_selector": "values"}}
        ],
    }
    yield from rest_api_resources(config)


def load_jira_service_desk_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="jira_service_desk_pipeline",
        destination="duckdb",
        dataset_name="jira_service_desk_data",
    )
    load_info = pipeline.run(jira_service_desk_source())
    print(load_info)


if __name__ == "__main__":
    load_jira_service_desk_to_duckdb()

Run it with python jira_service_desk_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 Jira Service Desk 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("jira_service_desk_pipeline").dataset()
df = data.request.df()
print(df.head())

SQL:

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SELECT * FROM jira_service_desk_data.request LIMIT 10;

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

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How do I deploy the Jira Service Desk 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 Jira Service Desk 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 Jira Service Desk 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