Building a Modern Data Lakehouse with the Medallion Architecture

Introduction

Contemporary data environments have evolved from monolithic systems to diversified solutions spanning cloud services, specialized analytics tools, and real-time streaming platforms. This diversity, while powerful, often leads to disjointed data ecosystems where data quality, governance, and accessibility become significant challenges.

The Medallion Architecture provides a robust framework for consolidating these disparate elements into a cohesive Lakehouse architecture. It harmonizes capabilities from raw data processing to sophisticated analytics, offering a progressive, adaptable solution for modern data challenges.

This architecture outlines a method for structuring and managing data pipelines, encompassing everything from raw data handling in cloud environments to refined data utilization in analytics platforms. It emphasizes:

• Uniform data structures across the organization
• Efficient schema management with evolution support
• Robust governance and security controls
• Seamless transition from raw data to advanced BI tools

The Six-Layer Architecture Overview

The Medallion Architecture organizes data into six distinct layers, each serving a specific purpose in the data lifecycle:

Layer Mapping Reference

Layer 1: Landing Zone

The Landing Zone serves as a versatile staging area designed for data ingestion from varied sources. Here, data is gathered in its raw form prior to any transformation.

Key Capabilities:

• Schema-on-read: Retaining original data schema without enforcement
• Multi-format support: CSV, JSON, Parquet, AVRO, and more
• Dual ingestion modes: Both batch and streaming data ingestion
• Validation gateway: Optional data validation and quarantine processes
• Transitory storage: Temporary holding before Initial layer processing

Layer 2: Initial Layer

The Initial Layer serves as the immutable repository for raw data, preserving a complete history and functioning as the single source of truth.

Characteristics:

• Immutable storage in original form
• Comprehensive and auditable history with timestamping
• Automatic tracking of schema changes
• Organized partitioning by data ingestion time
• No transformation or business logic applied
• Efficient storage formats like Parquet

This layer ensures that you can always trace back to the original data, supporting compliance requirements and enabling data lineage tracking.

Layer 3: Intermediate Layer

The Intermediate Layer is where raw data from the Initial Layer is refined into high-quality, shareable datasets.

Responsibilities:

• Data cleansing, validation, and filtering
• Establishing data models with clear domains and semantics
• Type 2 historization for temporal data tracking
• Management of master data and conformed dimensions
• Data enrichment through reference data joins
• Creation of materialized datasets and data cubes
• Development of business metadata repository

Layer 4: Integrated Layer

The Integrated Layer aggregates and refines standardized data from the Intermediate Layer into well-governed and broadly accessible data products.

Primary Functions:

• Merging various data sources from the Intermediate Layer
• Data certification and trust indicators
• Management of data access and utilization policies
• Establishment of business glossary and definitions
• Creation of common data models, metrics, and KPIs
• Master and reference data management
• Integration with third-party and external sources

Layer 5: Refined Layer

The Refined Layer focuses on preparing customized datasets for specific applications, users, and consumption scenarios.

Features:

• Tailored data preparation for business users
• Data sandboxes for analysts and knowledge workers
• Data masking for sensitive information protection
• Cross-system integration for siloed data
• Advanced data aggregation and hypercubes
• Performance optimization through caching
• Custom views and schemas for different consumers
• Metadata-driven automation and reusability

Schema Management with Delta Lake

Effective schema management is crucial for maintaining analytical datasets as source schemas evolve. The Medallion Architecture leverages Delta Lake for robust schema handling:

Schema Capabilities:

Historization Strategies

The architecture supports two primary historization approaches:

Type 2 Historization

Implemented using start and end dates within the same table. Optimal when:

• Historical data volume is moderate
• Time-series analysis is the primary query pattern
• Managing table size without splitting is feasible

Type 4 Historization

Separates current and historical data into different tables. Suitable when:

• Historical data volume is significant
• Queries primarily target current data
• Separating historical data improves performance

Infrastructure as Code with Terraform

The MedallionArchitecture project includes a complete Terraform implementation for deploying the architecture on AWS with Databricks. The modular design enables infrastructure provisioning across multiple environments.

Terraform Module Structure

# Define the AWS provider
provider "aws" {
  region = "us-east-1"
}

# Module for creating VPCs
module "vpcs" {
  source    = "./modules/vpcs"
  vpc1_cidr = "10.0.0.0/16"
  vpc2_cidr = "10.1.0.0/16"
}

# Module for creating IAM roles and policies for Databricks
module "databricks_iam" {
  source = "./modules/databricks_iam"
}

# Module for creating Databricks clusters
module "databricks_clusters" {
  source       = "./modules/databricks_clusters"
  num_clusters = 3
  subnet_ids   = module.vpcs.private_subnet_ids
  role_arn     = module.databricks_iam.databricks_role_arn
}

output "databricks_cluster_ids" {
  value = module.databricks_clusters.cluster_ids
}

Terraform Component Overview

Infrastructure Workflow

AWS and Databricks Integration

The architecture leverages AWS services integrated with Databricks for a complete data lakehouse solution:

Networking Design

The solution implements secure networking with private connectivity:

• VPC 1: Hosts private subnets for Raw and Trusted S3 zones
• VPC 2: Contains private subnet for Refined zone access
• Private Links: Ensures data privacy between services
• Databricks Integration: Secure cluster access to all data zones

Data Governance and Security

The Medallion Architecture implements comprehensive governance and security measures:

Governance Framework

• Policy Management: Access controls, retention rules, and compliance standards
• Data Lineage: Full tracking through all Medallion layers
• Data Cataloging: Unity Catalog for metadata management and discovery

Security Controls

• Encryption: Data at rest and in transit
• Authentication: Robust identity and access management
• Data Masking: Protection of sensitive information in Refined layer
• Anonymization: Privacy-preserving analytics capabilities

Metadata Management

The architecture leverages three types of metadata for comprehensive data management:

Technical Metadata

Dataset and attribute definitions including table/column information, names, descriptions, data types, and relationships. Captured by tools like Unity Catalog.

Operational Metadata

Data processing operations including job execution details, performance metrics, and processing histories. Essential for monitoring pipeline health.

Business Metadata

Business-relevant information including data ownership, usage policies, and domain-specific definitions. Critical for understanding data context.

Advanced Analytics and ML Integration

The Medallion Architecture seamlessly integrates with ML/AI workflows:

MLOps Capabilities:

• Model development and training on Refined layer data
• Efficient deployment with continuous monitoring
• Feature store integration for ML pipelines

Analytics Workflows:

• Self-service BI tool integration
• Real-time analytics via streaming capabilities
• Custom dashboard and reporting support

Scalability and Performance

The architecture is designed for enterprise scale:

Scalability Features:

• Elastic computing resources with automatic scaling
• Distributed processing with Apache Spark
• Multi-region deployment support

Performance Optimization:

• Data caching in Refined layer
• Query optimization algorithms
• Partitioning and clustering strategies

Conclusion

The Medallion Architecture provides a comprehensive, scalable, and secure framework for modern data management and analytics. By structuring data across distinct layers, organizations can:

1. Efficiently manage data lifecycles from raw ingestion to analytics
2. Ensure data quality through progressive refinement
3. Maintain governance with built-in security and compliance
4. Enable self-service analytics for business users
5. Support ML/AI initiatives with high-quality data products

The combination of Delta Lake, Databricks, and Terraform IaC creates a reproducible, version-controlled infrastructure that scales with your data needs.

For the complete implementation including Terraform modules and reference architectures, visit the MedallionArchitecture GitHub repository.

Further Reading

• Medallion Reference Architecture on AWS
• AWS and Databricks Integration Guide
• Reference Implementation Details