Orchestra

ADF to Databricks Lakeflow Jobs translator via Declarative Automation Bundles.

Orchestra is a Claude Code plugin that converts Azure Data Factory (ADF) pipeline definitions into Databricks Lakeflow Jobs packaged as Declarative Automation Bundles (DABs). It deterministically translates known activity types and falls back to agentic LLM-assisted translation for complex or rare types.

Architecture

                         Orchestra Pipeline
                         ==================

  ADF JSON (UC Volumes)
        |
        v
  +------------------+
  |  1. INGEST       |    Parse ADF ARM/JSON exports
  |  adf_loader.py   | -> Typed AST -> inventory.json
  +------------------+
        |
        v
  +------------------+
  |  2. TRANSLATE     |    Registry dispatch + topological sort
  |  engine.py       | -> Pipeline IR (deterministic + agentic gaps)
  +------------------+
        |
        v
  +------------------+
  |  3. PREPARE       |    IR -> DAB YAML + notebooks + setup scripts
  |  dab_writer.py   | -> Deployable DABs project
  +------------------+
        |
        v
  databricks bundle validate / deploy

Quick Start

1. Install the plugin in Claude Code:

   claude plugin install ghanse/orchestra

2. Run the end-to-end migration:

   /orchestra:migrate
   

   Or run individual phases:

   /orchestra:ingest      # Parse ADF JSON, produce inventory
   /orchestra:translate   # Deterministic + agentic translation
   /orchestra:prepare     # Generate DABs project
   

Supported ADF Activity Types

Deterministic (16 types)

ADF Activity	Databricks Task	Category
Copy	Notebook task	Data movement
DatabricksNotebook	Notebook task	Compute
DatabricksSparkJar	Spark JAR task	Compute
DatabricksSparkPython	Spark Python task	Compute
ForEach	for_each_task	Control flow
IfCondition	if_else_task	Control flow
Switch	if_else_task chain	Control flow
SetVariable	run_job_task	Control flow
AppendVariable	run_job_task	Control flow
Filter	Notebook task	Control flow
Wait	Notebook task (sleep)	Control flow
Lookup	Notebook task	Data access
WebActivity	Notebook task	External
Delete	Notebook task	Data management
ExecutePipeline	run_job_task	Orchestration
DatabricksJob	run_job_task	Compute

Agentic Fallback (12 types)

ADF Activity	Strategy
ExecuteDataFlow	LLM-assisted via adf-to-databricks-plugin
SqlServerStoredProcedure	LLM-assisted via adf-to-databricks-plugin
AzureFunction	LLM-assisted via adf-to-databricks-plugin
WebHook	LLM-assisted via adf-to-databricks-plugin
Custom	LLM-assisted via adf-to-databricks-plugin
ExecuteSSISPackage	LLM-assisted via adf-to-databricks-plugin
AzureMLExecutePipeline	LLM-assisted via adf-to-databricks-plugin
GetMetadata	LLM-assisted via adf-to-databricks-plugin
Validation	LLM-assisted via adf-to-databricks-plugin
Fail	LLM-assisted via adf-to-databricks-plugin
Script	LLM-assisted via adf-to-databricks-plugin
Until	LLM-assisted via adf-to-databricks-plugin

How It Works

Phase 1: Ingest

Reads ADF JSON definitions from Unity Catalog volumes, normalizes ARM template format, parses into typed AST nodes, and classifies each activity as deterministic, agentic, or unsupported. Produces inventory.json.

Phase 2: Translate

Applies deterministic translators via registry dispatch, resolves dependencies through topological sort, and threads immutable TranslationContext through control-flow visitors. Agentic gaps are flagged for LLM-assisted translation. Produces Pipeline IR.

Phase 3: Prepare

Converts Pipeline IR into a deployable DABs project: databricks.yml, per-job YAML resource files, generated Python notebooks, and setup scripts for UC volumes, secrets, and connections.

Output Format

dab_output/
  databricks.yml              # Bundle configuration
  resources/
    jobs/
      <pipeline_name>.yml     # One job per ADF pipeline
  src/
    notebooks/
      <pipeline_name>/
        <activity_name>.py    # Generated notebooks per activity
    setup/
      create_volumes.py       # UC volume setup
      create_secrets.py       # Secret scope setup
      create_connections.py   # Connection setup

Development

make dev          # Install dependencies
make test         # Run unit tests
make integration  # Run integration tests
make fmt          # Format + lint (ruff + mypy)
make clean        # Remove build artifacts

Prerequisites

• Python 3.12+
• uv package manager

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Follow the adding a new translator guide
4. Run make fmt && make test before committing
5. Open a pull request