databricks-reference-architecture

name	databricks-reference-architecture
description	Implement Databricks reference architecture with best-practice project layout. Use when designing new Databricks projects, reviewing architecture, or establishing standards for Databricks applications. Trigger with phrases like "databricks architecture", "databricks best practices", "databricks project structure", "how to organize databricks", "databricks layout".
allowed-tools	Read, Grep
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>
compatible-with	claude-code, codex, openclaw
tags	["saas","databricks","databricks-reference"]

Databricks Reference Architecture

Overview

Production-ready lakehouse architecture with Unity Catalog, Delta Lake, and the medallion pattern. Covers workspace organization, three-level namespace governance, compute strategy, CI/CD with Asset Bundles, and project structure for team collaboration.

Prerequisites

• Databricks workspace with Unity Catalog enabled
• Understanding of medallion architecture (bronze/silver/gold)
• Databricks CLI configured
• Terraform or Asset Bundles for infrastructure

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    UNITY CATALOG                                  │
│                                                                   │
│  ┌────────────┐  ┌────────────┐  ┌────────────┐  ┌───────────┐  │
│  │  Bronze    │  │  Silver    │  │   Gold     │  │ ML Models │  │
│  │  Catalog   │─▶│  Catalog   │─▶│  Catalog   │  │ (MLflow)  │  │
│  │  (raw)     │  │  (clean)   │  │  (curated) │  │           │  │
│  └────────────┘  └────────────┘  └────────────┘  └───────────┘  │
│       ▲                                    │                      │
│  ┌────────────┐                   ┌────────────────┐             │
│  │ Auto Loader│                   │ Model Serving  │             │
│  │ Ingestion  │                   │ Endpoints      │             │
│  └────────────┘                   └────────────────┘             │
├─────────────────────────────────────────────────────────────────┤
│  Compute: Job Clusters │ SQL Warehouses │ Instance Pools        │
├─────────────────────────────────────────────────────────────────┤
│  Security: Row Filters │ Column Masks │ Secret Scopes │ SCIM   │
├─────────────────────────────────────────────────────────────────┤
│  CI/CD: Asset Bundles │ GitHub Actions │ dev/staging/prod       │
└─────────────────────────────────────────────────────────────────┘

Project Structure

databricks-platform/
├── src/
│   ├── ingestion/
│   │   ├── bronze_raw_events.py       # Auto Loader streaming
│   │   ├── bronze_api_data.py         # REST API batch ingestion
│   │   └── bronze_file_uploads.py     # Manual file uploads
│   ├── transformation/
│   │   ├── silver_clean_events.py     # Cleansing + dedup
│   │   ├── silver_schema_enforce.py   # Schema validation
│   │   └── silver_scd2.py            # Slowly changing dimensions
│   ├── aggregation/
│   │   ├── gold_daily_metrics.py      # Business KPIs
│   │   ├── gold_user_features.py      # ML feature engineering
│   │   └── gold_reporting.py          # BI-ready views
│   └── ml/
│       ├── training/
│       │   └── train_churn_model.py
│       └── inference/
│           └── batch_scoring.py
├── tests/
│   ├── conftest.py                    # Spark fixtures
│   ├── unit/                          # Local Spark tests
│   └── integration/                   # Databricks Connect tests
├── resources/
│   ├── etl_jobs.yml                   # ETL job definitions
│   ├── ml_jobs.yml                    # ML pipeline definitions
│   └── maintenance.yml                # OPTIMIZE/VACUUM schedules
├── databricks.yml                     # Asset Bundle root config
├── pyproject.toml
└── requirements.txt