databricks-exec-code-mcp

Package and deploy Databricks Asset Bundles with proper parameterization, multi-environment support, and serverless compute. Handles project structure, databricks.yml generation, validation, and deployment. Use when packaging tested code for production, deploying pipelines, or managing multi-environment deployments.

Production data engineering pipelines following medallion architecture (Bronze/Silver/Gold layers) with data ingestion, transformation, quality checks, Delta Lake optimization, and orchestration. Use when building ETL pipelines, medallion architecture, data lakes, or data transformation workflows.

End-to-end machine learning pipelines on Databricks including data exploration, feature engineering, model training with hyperparameter optimization, MLflow experiment tracking, model registration to Unity Catalog, and deployment as DABs. Use when building ML workflows, training models, or deploying ML pipelines.

Execute code on Databricks clusters using MCP Command Execution API. Supports stateless quick validation and stateful iterative development. Use when testing Python/SQL code on clusters, debugging pipelines, or validating transformations.

Manage Unity Catalog resources including catalogs, schemas, and tables. Handles discovery, creation, updates, and deletions with proper naming conventions and governance. Use when exploring catalogs, creating schemas, managing tables, or setting up data governance.