Medallion Architecture

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1. What is Medallion Architecture?

Medallion Architecture is a layered approach to organizing data in a data lake or data lakehouse. It divides data into three distinct layers, each with a specific purpose:

1. Bronze Layer: Raw, unprocessed data.
2. Silver Layer: Cleaned, filtered, and enriched data.
3. Gold Layer: Highly refined, aggregated, and business-ready data.

The architecture is named after the “medallion” because the layers represent increasing levels of data quality and refinement, similar to the tiers of a medal (bronze, silver, gold).

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2. Key Concepts

1. Bronze Layer:

   • Purpose: Stores raw, unprocessed data as it is ingested from source systems.
   • Characteristics:
     • Data is stored in its original format (e.g., JSON, CSV, logs).
     • No transformations or cleaning are applied.
     • Acts as a “landing zone” for all incoming data.
   • Use Case: Backup of raw data for auditing, reprocessing, or debugging.

2. Silver Layer:

   • Purpose: Stores cleaned, filtered, and enriched data.
   • Characteristics:
     • Data is transformed, deduplicated, and standardized.
     • Business rules and validations are applied.
     • Data is structured and ready for analysis.
   • Use Case: Intermediate storage for data that is ready for further processing.

3. Gold Layer:

   • Purpose: Stores highly refined, aggregated, and business-ready data.
   • Characteristics:
     • Data is optimized for querying and reporting.
     • Aggregations, summaries, and business-specific transformations are applied.
     • Data is stored in a format suitable for end-users (e.g., star schema, denormalized tables).
   • Use Case: Final storage for data used in reporting, dashboards, and analytics.

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3. Benefits of Medallion Architecture

1. Data Quality: Ensures data is cleaned, validated, and enriched as it moves through the layers.
2. Scalability: Handles large volumes of data efficiently by processing it in stages.
3. Flexibility: Allows raw data to be stored for future reprocessing or auditing.
4. Efficiency: Optimizes data for specific use cases (e.g., raw storage, analytics, reporting).
5. Traceability: Provides a clear lineage of data as it moves through the layers.

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4. How Medallion Architecture Works

1. Data Ingestion:
   • Data is ingested from various sources (e.g., databases, APIs, IoT devices) into the Bronze Layer.
   • Example: Raw customer orders, website logs, or sensor data.
2. Data Cleaning and Enrichment:
   • Data from the Bronze Layer is cleaned, deduplicated, and enriched in the Silver Layer.
   • Example: Removing invalid records, standardizing formats, and joining data from multiple sources.
3. Data Aggregation and Refinement:
   • Data from the Silver Layer is aggregated, summarized, and transformed into business-ready formats in the Gold Layer.
   • Example: Creating daily sales reports, customer segmentation, or financial summaries.

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5. Tools and Technologies for Medallion Architecture

1. Data Lake Platforms:

   • Amazon S3: A scalable object storage service for the Bronze Layer.
   • Azure Data Lake Storage: A cloud-based data lake solution.
   • Google Cloud Storage: A scalable storage service for raw data.

2. Data Processing Frameworks:

   • Apache Spark: A distributed processing engine for cleaning and transforming data in the Silver and Gold Layers.
   • Delta Lake: An open-source storage layer that brings ACID transactions to data lakes.

3. Data Orchestration Tools:

   • Apache Airflow: A platform to orchestrate data pipelines across the layers.
   • Databricks: A unified analytics platform for building Medallion Architecture.

4. Data Warehousing:

   • Snowflake: A cloud data platform for storing and querying Gold Layer data.
   • Google BigQuery: A serverless data warehouse for analytics.

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6. Challenges in Medallion Architecture

1. Data Governance: Ensuring data quality, security, and compliance across all layers.
2. Complexity: Managing multiple layers and transformations can be complex.
3. Cost: Storing and processing large volumes of data can be expensive.
4. Performance: Optimizing query performance across layers, especially for large datasets.

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7. Real-World Examples

1. E-Commerce:

   • Bronze Layer: Raw order data from the website.
   • Silver Layer: Cleaned and enriched order data with customer details.
   • Gold Layer: Aggregated sales reports and customer segmentation.

2. IoT:

   • Bronze Layer: Raw sensor data from devices.
   • Silver Layer: Filtered and standardized sensor data.
   • Gold Layer: Aggregated metrics and anomaly detection reports.

3. Finance:

   • Bronze Layer: Raw transaction data from banking systems.
   • Silver Layer: Cleaned and validated transaction data.
   • Gold Layer: Financial summaries and fraud detection reports.

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8. Best Practices for Medallion Architecture

1. Design for Scalability: Use distributed storage and processing frameworks to handle large volumes of data.
2. Ensure Data Quality: Implement data validation and cleaning in the Silver Layer.
3. Optimize for Performance: Use indexing, partitioning, and caching to improve query performance in the Gold Layer.
4. Monitor and Log: Continuously monitor data pipelines and log transformations for debugging.
5. Implement Data Governance: Enforce security, compliance, and access controls across all layers.
6. Document Data Lineage: Track the flow of data through the layers for auditing and traceability.

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9. Key Takeaways

1. Medallion Architecture: A layered approach to organizing data in a data lake or data lakehouse.
2. Bronze Layer: Raw, unprocessed data.
3. Silver Layer: Cleaned, filtered, and enriched data.
4. Gold Layer: Highly refined, aggregated, and business-ready data.
5. Benefits: Data quality, scalability, flexibility, efficiency, traceability.
6. Tools: Amazon S3, Apache Spark, Delta Lake, Snowflake, Databricks.
7. Challenges: Data governance, complexity, cost, performance, data lineage.
8. Best Practices: Design for scalability, ensure data quality, optimize for performance, monitor and log, implement data governance, document data lineage.