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Medallion Architecture Guide

$29

A comprehensive decision framework and implementation guide for building production-grade medallion (bronze/silver/gold) architectures in Databricks.

🏷 v1.0.0
Production-ready
✓ Instant download✓ Lifetime updates✓ MIT licensed✓ MIT license✓ Secure checkout (Stripe)

📁 File Structure 26 files

medallion-architecture-guide/
├── LICENSE
├── README.md
├── cheatsheets/
│ ├── layer_comparison.md
│ ├── migration_checklist.md
│ ├── naming_conventions_cheatsheet.md
├── code_examples/
│ ├── bronze_ingestion.py
│ ├── cross_layer_pipeline.py
│ ├── gold_aggregation.py
│ ├── naming_convention_generator.py
│ ├── schema_migration.py
│ ├── silver_transformation.py
├── diagrams/
│ ├── data_flow.md
│ ├── decision_tree.md
│ ├── medallion_overview.md
├── free-sample.zip
├── guide/
│ ├── 01_introduction.md
│ ├── 02_decision_framework.md
│ ├── 03_bronze_layer.md
│ ├── 04_silver_layer.md
│ ├── 05_gold_layer.md
│ ├── 06_naming_conventions.md
│ ├── 07_schema_evolution.md
│ ├── 08_data_quality_gates.md
│ ├── 09_anti_patterns.md
│ ├── 10_reference_architectures.md
├── index.html

📖 Documentation Preview README excerpt

Medallion Architecture Guide

> A comprehensive decision framework and implementation guide for building production-grade medallion architectures in Databricks.

Product: Medallion Architecture Guide

Version: 1.0.0

Price: $19

Publisher: DataStack Pro

License: MIT

---

Why This Guide Exists

Every team that adopts Databricks eventually faces the same question: "How should we organize our lakehouse?"

The medallion architecture (bronze → silver → gold) is the dominant pattern, but most teams implement it poorly. They either:

1. Copy a blog post's toy example and wonder why it doesn't scale

2. Over-engineer the layers with unnecessary abstractions

3. Skip layers they actually need, creating unmaintainable pipelines

4. Choose medallion when a different pattern would serve them better

This guide gives you the decision framework to know when medallion is right, and the implementation playbook to do it properly when it is.

---

What You Get

Decision Framework (Not Just "Use Medallion Always")

Before writing a single line of code, you'll work through a genuine decision tree that considers:

  • Your data volume, velocity, and variety
  • Team size and SQL vs. engineering skill mix
  • Regulatory and audit requirements
  • Whether data vault, one-big-table, streaming-first, or hybrid patterns might serve you better
    • Deep-Dive Layer Guides

    Each layer gets a dedicated chapter covering patterns that blog posts skip:

    LayerKey Topics
    **Bronze**Auto Loader vs. batch, append-only vs. overwrite, CDC capture, raw schema preservation
    **Silver**SCD Type 1/2, deduplication strategies, data quality gates, conforming dimensions
    **Gold**Business aggregates, feature stores, materialized views, serving layer patterns
    Production-Ready Code

    Not snippets—complete, runnable PySpark scripts with type hints, docstrings, and error handling:

  • Bronze ingestion with Auto Loader and batch fallback
  • Silver transformation pipeline with quality gates
  • Gold aggregation with incremental processing
  • End-to-end cross-layer pipeline
  • Naming convention SQL generator (YAML-driven)
  • Schema migration utility
    • Anti-Pattern Encyclopedia

    ... preview truncated, see full README in product download.

    📄 Code Sample .py preview

    code_examples/bronze_ingestion.py"""Bronze Layer Ingestion Pipeline. Complete bronze layer ingestion example supporting both Auto Loader (streaming) and batch JDBC ingestion patterns. Designed for Databricks Runtime 13.3+. Usage: # In a Databricks notebook or job: from bronze_ingestion import BronzeIngestionPipeline pipeline = BronzeIngestionPipeline(spark, config) pipeline.ingest_files("json", "/landing/orders/", "bronze.erp.raw_orders") pipeline.ingest_jdbc("silver_source_db", "public.customers", "bronze.erp.raw_customers") Copyright (c) 2026 DataStack Pro. MIT License. """ from __future__ import annotations from dataclasses import dataclass, field from datetime import datetime from typing import Any from pyspark.sql import DataFrame, SparkSession from pyspark.sql import functions as F from pyspark.sql.streaming import StreamingQuery from pyspark.sql.types import ( StringType, StructField, StructType, TimestampType, ) @dataclass class IngestionConfig: """Configuration for a bronze ingestion pipeline. Attributes: checkpoint_base: Base path for streaming checkpoints source_system: Name of the source system (for metadata)

    📅 Changelog

    v1.0.0 — Initial release.

    Purchases include lifetime updates. Check the product page for the latest version.

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