Build Your First AI-Powered Databricks Pipeline

Build a complete Databricks Medallion architecture using nothing but natural-language prompts. In this hands-on workshop, we show how Databricks' AI DevKit connects Claude Code to your Databricks workspace through MCP (Model Context Protocol), so you can create catalogs, ingest data, and build Bronze, Silver, and Gold layers without copy-pasting a single line of code. We walk through a real retail/e-commerce use case end to end, and along the way we cover the practices that keep an AI-driven pipeline safe and cost-aware. What you'll learn: What AI DevKit and MCP are, and how they connect Claude Code to the Databricks REST API Setting up the Databricks CLI, profiles, host URL, and access tokens Installing AI DevKit and choosing skill profiles (Data Engineer, AI/ML, or all skills) Building a Medallion architecture: volumes to Bronze to Silver to Gold Ingesting CSV files with COPY INTO and enabling schema evolution Cleaning real data issues (multi-line addresses, special characters) with simple prompts Creating a CLAUDE.md file to enforce rules: approvals before running, no accidental DROP/DELETE, serverless compute only, catalog boundaries Why human validation and good context still matter, and how prompt quality drives both speed and cost Key takeaway: AI doesn't replace the data engineer. It removes the manual work so you can focus on giving clear context, following best practices, and validating results. A setup guide and all notebooks are available in the shared GitHub repo. #Databricks #AIDevKit #ClaudeCode #DataEngineering #MCP