project-planning

Multi-phase project planning on Databricks requires structured phases from requirements gathering to manifest generation. This skill orchestrates that workflow, guiding you through artifact definition and interactive planning to produce machine-readable contracts for downstream implementation.

The Agent Domain Framework organizes all data artifacts by domain to ensure consistent AI agent integration and discoverability. It structures artifacts like TVFs, Metric Views, Genie Spaces, and Agents to focus on business value and prevent bloat.

Post-Gold layer development is structured using interactive planning, templates, and worked examples. This skill guides the creation of artifacts for observability, analytics, and agent-based frameworks, ensuring efficient rationalization and multi-artifact project execution.

Generating machine-readable contracts for Databricks data products is supported through manifest generation. By defining business questions and rationalizing artifacts, the skill produces contracts that enable downstream implementation of your data platform solution.

Yes, this project planning approach supports agent-based frameworks and Genie Spaces. It ensures efficient creation of these artifacts using the Agent Layer Architecture and Agent Domain Framework, organizing them by domain for consistent AI agent integration.

To prevent artifact bloat when planning a Databricks data solution, use artifact rationalization. This skill ensures the efficient creation of TVFs, Metric Views, Genie Spaces, and Agents, focusing strictly on business value and avoiding unnecessary components.