Speech-To-Text-With-Databricks: Skill for Claude Code

Lakeflow Spark Declarative Pipelines (SDP)

IMPORTANT: If this is a new pipeline (one does not already exist), see Quick Start. Be sure to use whatever language user has specified only (Python or SQL). Be sure to use Databricks Asset Bundles for new projects.

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Critical Rules (always follow)

• MUST confirm language as Python or SQL. Stick with that language unless told otherwise.
• MUST if not modifying an existing pipeline, use Quick Start below.
• MUST create serverless pipelines by default. ** Only use classic clusters if user explicitly requires R language, Spark RDD APIs, or JAR libraries.

Required Steps

Copy this checklist and verify each item:

- [ ] Language selected: Python or SQL
- [ ] Compute type decided: serverless or classic compute
- [ ] Decide on multiple catalogs or schemas vs. all in one default schema
- [ ] Consider what should be parameterized at the pipeline level to make deployment easy.
- [ ] Consider [Multi-Schema Patterns](#multi-schema-patterns) below, ask if unclear on best choices.
- [ ] Consider [Modern Defaults](#modern-defaults) below, ask if unclear on best choices.


## Quick Start: Initialize New Pipeline Project

**RECOMMENDED**: Use `databricks pipelines init` to create production-ready Asset Bundle projects with multi-environment support.

### When to Use Bundle Initialization

Use bundle initialization for **New pipeline projects** for a professional structure from the start

Use manual workflow for:
- Quick prototyping without multi-environment needs
- Existing manual projects you want to continue
- Learning/experimentation

### Step 1: Initialize Project

I will automatically run this command when you request a new pipeline:

```bash
databricks pipelines init

Interactive Prompts:

• Project name: e.g., customer_orders_pipeline
• Initial catalog: Unity Catalog name (e.g., main, prod_catalog)
• Personal schema per user?: yes for dev (each user gets their own schema), no for prod
• Language: SQL or Python (auto-detected from your request - see language detection below)

Generated Structure:

my_pipeline/
├── databricks.yml              # Multi-environment config (dev/prod)
├── resources/
│   └── *_etl.pipeline.yml      # Pipeline resource definition
└── src/
    └── *_etl/
        ├── explorations/       # Exploratory code in .ipynb
        └── transformations/    # Your .sql or .py files here

Step 2: Customize Transformations

Replace the example code created by the init process with custom transformation files in src/transformations/ based on provided requirements, using best practice guidance from this skill.

For Python pipelines using cloudFiles: Ask the user where to store Auto Loader schema metadata. Recommend:

/Volumes/{catalog}/{schema}/{pipeline_name}_metadata/schemas

Step 3: Deploy and Run