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Medallion Architecture Accelerator

$79

Production implementation of Bronze/Silver/Gold architecture on Databricks with metadata-driven pipeline generator and quality gates.

📁 34 files🏷 v2.0.0 (updated 2026-03-12)

Production-ready

✓ Instant download✓ Lifetime updates✓ MIT licensed✓ MIT license✓ Secure checkout (Stripe)

📋 What's Inside 34 files

✓ README.md—
✓ manifest.json—
✓ framework/bronze/base_ingestor.py—
✓ framework/bronze/auto_loader_ingestor.py—
✓ framework/bronze/jdbc_ingestor.py—
✓ framework/silver/base_transformer.py—
✓ framework/silver/scd_type2.py—
✓ framework/gold/aggregation_builder.py—
✓ framework/gold/fact_table_builder.py—
✓ framework/naming_convention_generator.py—
✓ framework/schema_migration.py—
✓ config/pipeline_generator.py—
✓ config/example_sources.yaml—
✓ quality/quality_gates.py—
✓ optimization/delta_optimization.py—

📁 File Structure 34 files

medallion-architecture-accelerator/
├── README.md
├── manifest.json
├── framework/
│ ├── bronze/
│ │ ├── base_ingestor.py
│ │ ├── auto_loader_ingestor.py
│ │ ├── jdbc_ingestor.py
│ ├── silver/
│ │ ├── base_transformer.py
│ │ ├── scd_type2.py
│ ├── gold/
│ │ ├── aggregation_builder.py
│ │ ├── fact_table_builder.py
│ ├── naming_convention_generator.py
│ ├── schema_migration.py
├── config/
│ ├── pipeline_generator.py
│ ├── example_sources.yaml
├── quality/
│ ├── quality_gates.py
├── optimization/
│ ├── delta_optimization.py
├── testing/
│ ├── layer_tests.py
├── docs/
│ ├── architecture_guide.md
│ ├── performance_benchmarks.md
│ ├── 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
│ ├── cheatsheets/
│ │ ├── layer_comparison.md
│ │ ├── migration_checklist.md
│ │ ├── naming_conventions_cheatsheet.md
│ ├── diagrams/
│ │ ├── data_flow.md
│ │ ├── decision_tree.md
│ │ ├── medallion_overview.md

📖 Documentation Preview README excerpt

Medallion Architecture Accelerator

By [Datanest Digital](https://datanest.dev) | Version 2.0.0 | $79

Production-ready framework for implementing the Medallion Architecture (Bronze / Silver / Gold) on Databricks with Delta Lake. Eliminate weeks of boilerplate and enforce best practices from day one.

v2.0 adds a comprehensive 10-chapter implementation guide, quick-reference cheatsheets, architecture diagrams, plus naming convention and schema migration utilities.

---

What You Get

Production Framework (`framework/`)

#### Bronze Layer (Raw Ingestion)

**Base Ingestor** -- Schema evolution, exactly-once semantics, source lineage tracking

**Auto Loader Ingestor** -- Cloudfiles-based streaming ingestion with rescue columns

**JDBC Ingestor** -- Incremental extraction from relational databases with watermark tracking

#### Silver Layer (Conformed / Cleansed)

**Base Transformer** -- Deduplication, type casting, null handling, data conformance

**SCD Type 2** -- Slowly-changing dimension management with merge-based upserts

#### Gold Layer (Business / Aggregated)

**Aggregation Builder** -- Business KPIs, time-series rollups, dimensional model support

**Fact Table Builder** -- Star-schema fact tables with conformed dimension joins

#### Utility Tools (New in v2.0)

**Naming Convention Generator** -- Enforce consistent table/column/path naming across your lakehouse

**Schema Migration** -- Track and apply schema changes across medallion layers with full audit trail

Pipeline Generation (`config/`)

**Metadata-Driven Generator** -- Define sources in YAML, generate all three layers automatically

**Example Configs** -- Five pre-built source configurations covering common patterns

Quality & Optimization

**Quality Gates** (`quality/`) -- Null checks, schema validation, business rules, inter-layer assertions

**Delta Optimization** (`optimization/`) -- OPTIMIZE, ZORDER, VACUUM, liquid clustering per layer tier

**Layer Tests** (`testing/`) -- Unit and integration testing framework for every layer

10-Chapter Implementation Guide (New in v2.0) (`docs/guide/`)

A complete, opinionated guide to designing, building, and operating a medallion lakehouse:

Chapter	Topic	What You'll Learn
01	Introduction	Why medallion, when to use it, core principles
02	Decision Framework	How to decide if medallion fits your use case
03	Bronze Layer	Ingestion patterns, schema evolution, exactly-once
04	Silver Layer	Cleansing, conformance, deduplication, SCD
05	Gold Layer	Aggregations, dimensional modeling, materialized views
06	Naming Conventions	Databases, tables, columns, paths -- consistent naming
07	Schema Evolution	Safe schema changes, migration strategies, compatibility
08	Data Quality Gates	Null checks, business rules, inter-layer assertions
09	Anti-Patterns	Common mistakes and how to avoid them
10	Reference Architectures	Three complete architectures (startup → enterprise)

Quick-Reference Cheatsheets (New in v2.0) (`docs/cheatsheets/`)

**Layer Comparison** -- Side-by-side comparison of Bronze vs Silver vs Gold responsibilities

**Migration Checklist** -- Step-by-step checklist for migrating existing pipelines to medallion

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

📄 Code Sample .py preview

framework/bronze/base_ingestor.py# Databricks notebook source # MAGIC %md # MAGIC # Bronze Layer - Base Ingestor # MAGIC **Medallion Architecture Accelerator** by [Datanest Digital](https://datanest.dev) # MAGIC # MAGIC Base class for bronze layer ingestion with: # MAGIC - Schema evolution support # MAGIC - Exactly-once semantics via checkpoint tracking # MAGIC - Source lineage and metadata tracking # MAGIC - Rescue column for malformed records # MAGIC - Configurable write modes (append / merge) # COMMAND ---------- from __future__ import annotations import json import hashlib from abc import ABC, abstractmethod from dataclasses import dataclass, field from datetime import datetime, timezone from typing import Any, Dict, List, Optional, Sequence from pyspark.sql import DataFrame, SparkSession from pyspark.sql import functions as F from pyspark.sql.types import ( StringType, StructField, StructType, TimestampType, ) # COMMAND ---------- # MAGIC %md # MAGIC ## Configuration # COMMAND ----------

⚙ Requirements & Compatibility

Requirement	Details
Databricks Runtime	≥ 13.3 LTS
License	MIT (see license-terms page)

📅 Changelog

v2.0.0 — Initial release. Last updated 2026-03-12.

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

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