Medallion Architecture On AWS

Building Modern Data Lakes on AWS S3 with the Medallion Architecture

Introduction

Data constitute the foundation of contemporary enterprises. However as the volume,
velocity, and variety of data grow, organizations face a critical
challenge: how to store, manage, and analyze data efficiently at
scale.

data lakes which is a centralized repository is used to stored structured,
semi-structured, and unstructured data in their raw form. When combined with AWS S3
and the Medallion architecture, this approach provides scalable, reliable, and layered approach
for transforming raw data into insights ready for analysis.

In this post, we'll explore:

• Why S3 is the go-to storage for modern data lakes

• The Medallion architecture and its layers (Bronze, Silver, Gold)

• Practical design patterns, best practices, and real-world use cases

• How AWS services integrate seamlessly with S3-based data lakes

1. Why AWS S3 for Data Lakes?

Amazon S3 (Simple Storage Service) is object storage that offers:

• Unlimited storage -- scale from gigabytes to petabytes of data without distruption

• High durability -- 11 nines of durability (99.999999999%)

• Flexible storage classes and formats -- Standard, Infrequent Access, Glacier etc classes and can stores data
  in CSV ,JSON ,Parquet ,ORC etc formats.

• Secure and compliant -- encryption at rest and transit(SSE-S3/SSE-KMS), IAM
  policies, and fine-grained access control

• Integration with analytics and AI/ML services -- Intergrates seamlessly with AWS services like Glue, Athena,
  Redshift Spectrum, EMR, SageMaker, Kinesis and MSK

Why S3 is perfect for a data lake:

• Any format: can be stored: CSV, JSON, Parquet, Avro, ORC, images, audio,
  logs

• Decouples compute and storage -- separates computation from storage so that different analytics engines can
  access the same raw data without having to move it.

• Supports schema-on-read -- The schema is defined during querying, not during writing

2. Overview of the Medallion Architecture

A layered approach to organising your data lake is the Medallion Architecture. To enhance data quality, governance, and performance ,
it arranges data into several refined layers.

The layers are:

2.1 Bronze Layer -- Raw Data

• Purpose: Ingest all raw data exactly as it was obtained from sources.

• Data Characteristics:

  • Semi-structured or unstructured
  • May contain duplicates, errors, or missing values
  • Timestamped to monitor ingestion

• Use Cases:

  • Audit trail
  • Unprocessed logs and events
  • Origin of downstream transformations

• Example in S3:

• s3://your-datalake/bronze/customers/

• s3://your-datalake/bronze/orders/

2.2 Silver Layer -- Data that has been conformed and cleaned

• Purpose: Transform raw data into The goal is to create clean, standardised, and enriched databases from raw data.

• Data Characteristics:

  • Deduplicated
  • Corrected data types
  • Enhanced using lookups or joins
  • Consistent timestamps and formats

• Use Cases:

  • Intermediate analytics
  • Providing data to BI dashboards

• Example in S3:

• s3://your-datalake/silver/customers/

• s3://your-datalake/silver/orders/

Typical transformations:

• Filter out bad or null records

• Standardise currencies and timestamps.

• Join with reference tables (like product categories)

• Verify using business rules

2.3 Gold Layer -- Business-Level / Analytics Data

• Purpose: Create analytics-ready, aggregated, or curated data

• Data Characteristics:

  • Fully cleansed, trustworthy, and aggregated
  • Optimized for reporting or machine learning
  • For quicker enquiries, data is frequently stored in columnar formats (Parquet, ORC).

• Use Cases:

  • BI dashboards and reports
  • ML training datasets
  • KPI calculation and trend analysis

• Example in S3:

• s3://your-datalake/gold/sales_summary/

• s3://your-datalake/gold/customer_lifetime_value/

Typical transformations:

• Aggregation on daily, weekly, or monthly

• Join many silver tables to build fact tables

• Compute metrics like revenue, churn, or retention

3. AWS Services that Complement S3 Data Lakes

Building a Medallion architecture on S3 works best when combined with
AWS analytics services:

---

Service Role in Data Lake

---

AWS Glue ETL/ELT jobs to transform Bronze → Silver → Gold

Amazon Athena Query S3 data directly using SQL without moving
it

Amazon Redshift Query S3 data as external tables in Redshift
Spectrum

Amazon EMR Distributed Spark/Hadoop processing for
large-scale transformations

AWS Lake Centralized access control, data catalog, and
Formation governance

Amazon QuickSight BI dashboards on curated Gold data

---

4. Practical S3 Design Patterns

4.1 Partitioning

• Organize large datasets for query performance

• Common partitions: year=2025/month=11/day=30/

• Example:

• s3://my-datalake/silver/orders/year=2025/month=11/day=30/orders.parquet

4.2 File Formats

• Bronze: raw JSON, CSV, log files

• Silver: Parquet or ORC (columnar)

• Gold: Parquet with compression (Snappy)

4.3 Naming Conventions

• s3://<bucket>/<layer>/<entity>/year=YYYY/month=MM/day=DD/

• Helps Athena, Glue Crawlers, and partition pruning

5. Example Data Flow (End-to-End)

1. Bronze Layer: S3 ingests raw data from sources (e.g., Kafka,
   APIs, IoT)

2. Glue ETL: Cleanses, deduplicates, and standardizes → Silver
   Layer

3. Silver Layer: Curated, conformed tables available for analytics

4. Gold Layer: Aggregations, business KPIs, and ML datasets

5. Analytics: Athena queries, Redshift reports, QuickSight
   dashboards

Diagram Concept:

6. Best Practices for Medallion Data Lakes on S3

1. Use separate buckets or prefixes per layer (Bronze/Silver/Gold)

2. Partition and compress data for performance and cost savings

3. Enforce data validation rules in Silver ETL

4. Track metadata with Glue Catalog or Lake Formation

5. Secure access using IAM policies, S3 bucket policies, and KMS
   encryption

6. Use consistent naming conventions across layers and datasets

7. Version your data if necessary (append date/time to files for
   auditability)

7. Real-World Example

E-Commerce Data Lake:

• Bronze: raw JSON order events from web or mobile apps

• Silver: deduplicated, validated orders joined with product catalog

• Gold: aggregated revenue by category, daily sales metrics, customer
  LTV

• Analytics: Quicksight dashboards for executives, Athena queries for marketing

Conclusion

combining AWS S3 and the Medallion architecture provides a
scalable, structured, and reliable foundation for modern data
analytics.

• S3 gives unlimited storage and flexibility

• Medallion layers ensure data quality, governance, and analytics
  readiness

• Integration with Glue, Athena, Redshift, and QuickSight enables
  end-to-end insights

By implementing this architecture, organizations can build
enterprise-grade data lakes, reduce time-to-insight, and empower
data-driven decision-making.