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oracle-ai-data-platform-workbench-databricks-migrator Claude Code Plugin

Author
Oracle
Category
Development
Topics
Migration & Modernization · Data Engineering & Analytics · Cloud, Deployment & CI/CD
First cataloged
2026-07-09 (UTC)
Explanation last updated
2026-09-23 (UTC)
Source (GitHub) last updated
2026-10-14 (UTC) (yesterday)

The explanation below is AI-generated. Please verify it against the sources.

This plugin lets Claude Code drive migrations of Databricks workloads onto Oracle AI Data Platform (AIDP) through natural-language requests. According to the README, it automates conversion of notebooks, jobs, schedules, and catalog DDL using a two-pass engine: a first pass that rewrites code dependencies, and a second pass that executes, verifies, and self-corrects each notebook cell on a live AIDP cluster. The full Python migration engine is bundled inside the plugin, so no separate repository checkout is needed. Running an actual migration still requires the user's own OCI authentication, an active AIDP cluster, and an Anthropic API key, since the engine uses Claude with tool use to rewrite and verify each cell. The plugin also includes catalog-migration rules, a sandboxed write-redirect mechanism, and review agents for post-migration checks.

About the service

Oracle AI Data Platform Workbench is described on its homepage as a cloud service for cataloging, ingesting, and analyzing organizational data, offering a platform for building data analytics pipelines, notebooks, workflows, and AI agents or models.

What you can do with oracle-ai-data-platform-workbench-databricks-migrator

  • Build a migration plan (DAG/manifest) from a Databricks workspace path
  • Check that source tables and paths exist before starting migration
  • Run a full job migration combining dependency rewrite (Pass-1) and cell-by-cell execute/verify/fix (Pass-2)
  • Resume an interrupted migration run
  • Re-run and fix a specific failing cell with a targeted rewind
  • Migrate Unity Catalog / HMS metadata to AIDP using rule-based DDL rewriting
  • Configure S3-to-OCI bucket and namespace mappings used by the rewriter
  • Set up acceptance criteria for batch or streaming pipeline convergence
  • Use guided slash commands for job migration, catalog migration, data checks, and status summaries
  • Get notebook analysis and post-migration correctness review from specialist agents

Sources

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