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Overview

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Tool Name

data_dev_tools

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Purpose

The data_dev_tools provide a foundation for data development workflows with planned, native Jira connectivity. The goal is to bridge day-to-day data engineering work with project tracking so teams can plan, build, and report outcomes in one place.

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Functions Available

There are no callable functions in this tool yet. The interface is reserved for upcoming Jira-linked actions such as create, update, sync, and report.

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Key Features

Jira Alignment

Planned two-way integration with Jira issues for data tasks, bugs, and stories.

Lifecycle Support

Designed to map the data dev lifecycle from backlog to deploy and monitor.

Automation Hooks

Future triggers for auto-creating tickets from test failures or pipeline alerts.

Stakeholder Visibility

Consistent status and links between technical work and business outcomes.

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Input Parameters for Each Function

No function parameters are available yet. The section below outlines the planned shape once actions are released.
When Jira actions go live, expect inputs for issue keys, project keys, summaries, descriptions, assignees, priorities, and status transitions. Mappings will follow the conventions used injira_connector_tools.

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Use Cases

  1. Pipeline task tracking Connect data pipeline changes to Jira issues for clear ownership and audit history.
  2. Data quality incident flow Convert validation failures into actionable tickets with auto-filled context.
  3. Sprint coordination Tie modeling or ELT milestones to stories and epics with status sync.
  4. Business traceability Link technical commits and runs to requirements and acceptance criteria.
Integration requires Jira credentials and appropriate permissions. Coordinate with your Jira admin to provision safe access.

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Workflow/How It Works

  1. Plan Define the work in Jira and capture the data scope in the tool’s context.
  2. Build Use data tools such asdata_connector_tools,git_action, anddocument_index_tools to implement.
  3. Sync When available, push status and artifacts back to Jira so stakeholders see progress.
  4. Report Surface outcomes, links to PRs, run logs, and docs directly in issue threads.
Until functions are available, usejira_connector_toolsfor issue CRUD and reference those tickets inside your data development tasks.

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Integration Relevance

  • Project tracking: Works with project_manager_tools to align todos and milestones with Jira items.
  • Source work: Pairs with data_connector_tools for query and metadata tasks tied to tickets.
  • Version control: Use git_action to link commits and diffs back to issues.
  • Jira API: Complements jira_connector_tools for authenticated operations.
  • Build systems: Intended to sit alongside dbt_action and databricks_action for end-to-end visibility.

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Configuration Details

  • Jira server details and authentication will be required when actions are released.
  • Field mappings will follow your Jira project configuration. Custom fields will be supported through explicit mapping keys.
  • User and role alignment between this platform and Jira must be established for accurate assignment and transitions.

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Limitations or Notes

  1. No runtime functions The tool is pre-release and not yet callable.
  2. Dependency on Jira setup Future usage depends on valid Jira API access and project configuration.
  3. Org variability Workflows may need per-project field mapping where Jira is customized.

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Output

No runtime output is available yet. When released, outputs will include ticket identifiers, transition receipts, synchronization logs, and error messages with remediation hints.