The Medallion Architecture is a powerful data design pattern for logically organizing data in a Databricks lakehouse. It’s a „multi-hop” architecture that progressively improves the structure and quality of data as it flows through three layers: Bronze, Silver, and Gold.
In this technical guide, I will provide practical examples and best practices to help you implement Medallion Architecture in Databricks for scalable and well-governed data pipelines. By following this architecture, you can achieve significant benefits, including improved data quality, scalability, and robust data governance.
Understanding Medallion Architecture
The Medallion Architecture provides a structured approach to building data pipelines, ensuring that data is refined and validated at each stage. This multi-hop design pattern consists of three key layers:
- Bronze Layer: This is the initial landing zone for raw data from various source systems. The data is ingested in its original, unaltered format.
- Silver Layer: Data from the Bronze layer is cleaned, conformed, and enriched in the Silver layer. It’s transformed into a more structured and queryable format.
- Gold Layer: The Gold layer contains highly refined, aggregated data that is optimized for business intelligence (BI), reporting, and analytics.
Compared to traditional data architectures, the Medallion Architecture offers a more flexible and scalable approach to data management. It unifies data analytics, governance, and machine learning by providing a single source of truth for all data consumers.
The Bronze Layer: Raw Data Ingestion
The primary purpose of the Bronze layer is to capture raw data from source systems with minimal transformation. This layer serves as a historical archive, allowing you to replay data pipelines if needed.
Best Practices:
- Use Delta Lake: Store data in Delta Lake format to ensure immutability and enable features like time travel.
- Add Metadata Columns: Include metadata such as ingestion timestamps, source system identifiers, and batch IDs to track data lineage.
- Implement Auto Loader: Leverage Databricks Auto Loader for efficient and incremental ingestion of data from cloud storage.
Implementation Steps:
- Strategic Partitioning: Partition data based on ingestion date or source system to optimize query performance.
- Schema-on-Read: Use a schema-on-read approach to handle evolving schemas and data formats.
- Data Retention Policies: Implement data retention policies to manage storage costs and comply with data regulations.
Example: Ingesting JSON data using Spark readStream
(spark.readStream
.format(„cloudFiles”)
.option(„cloudFiles.format”, „json”)
.option(„cloudFiles.schemaLocation”, „/path/to/schema”)
.load(„/path/to/raw/data”)
.writeStream
.format(„delta”)
.option(„checkpointLocation”, „/path/to/checkpoint”)
.start(„/path/to/bronze/table”))
The Silver Layer: Data Validation and Cleansing
In the Silver layer, the raw data from the Bronze layer is transformed into a validated and queryable format. This is where data quality rules are enforced, and the data is cleansed and enriched.
Best Practices:
- Enforce Data Quality: Use Delta Live Tables (DLT) with expectations to define and enforce data quality rules.
- Handle Errors: Create error tables to capture and manage records that fail data quality checks.
- Use Delta Live Tables (DLT): DLT simplifies the development of reliable and maintainable streaming data pipelines.
Implementation Steps:
- Data Cleansing: Handle missing values, correct data types, and standardize formats.
- Deduplication: Remove duplicate records to ensure data integrity.
- Schema Enforcement: Enforce a well-defined schema to ensure data consistency.
- Apply Business Logic: Apply business rules to transform and enrich the data.
Example: DLT table definition with quality checks for customer events
CREATE STREAMING LIVE TABLE customer_events_silver
(
CONSTRAINT valid_customer_id EXPECT (customer_id IS NOT NULL) ON VIOLATION DROP ROW,
CONSTRAINT valid_event_timestamp EXPECT (event_timestamp > ’2022-01-01′) ON VIOLATION FAIL UPDATE
)
AS SELECT
customer_id,
event_type,
event_timestamp,
…
FROM stream(LIVE.customer_events_bronze)
The Gold Layer: Business-Ready Analytics
The Gold layer contains aggregated data that is optimized for business intelligence, reporting, and analytics. This data is organized in a way that is easy for business users to understand and consume.
Best Practices:
- Optimize for Query Patterns: Design Gold tables based on the specific query patterns of your BI and analytics tools.
- Implement Access Controls: Use Unity Catalog to implement fine-grained access controls and ensure data security.
- Use Materialized Views: Create materialized views for frequently accessed aggregations to improve query performance.
Implementation Steps:
- Domain-Specific Organization: Organize Gold tables by business domain (e.g., sales, marketing, finance).
- Semantic Enrichment: Add business-friendly column names and descriptions to make the data more understandable.
- Schedule Refreshes: Schedule regular refreshes of Gold tables to ensure the data is up-to-date.
Example: DLT for daily metrics aggregation
CREATE LIVE TABLE daily_metrics_gold
AS SELECT
region,
COUNT(DISTINCT customer_id) AS unique_customers,
…
FROM LIVE.customer_events_silver
GROUP BY region
Best Practices for Implementing Medallion Architecture in Databricks
- Separation of Concerns: Each layer should have a distinct purpose and responsibility.
- Workload Isolation: Isolate workloads to prevent interference between different stages of the data pipeline.
- Monitoring Data Quality: Implement data quality monitoring to ensure the reliability of your data.
- Performance Optimizations:
- Auto-Optimize: Enable auto-optimization to automatically compact small files and optimize table layout.
- Z-Order Indexing: Use Z-ordering to colocate related information in the same set of files.
- Balancing Batch and Streaming: Choose the right approach for your use case, and leverage the capabilities of Delta Lake for both batch and streaming workloads.
- Tools Integration:
- Unity Catalog: Leverage Unity Catalog for centralized governance, audit logging, and lineage tracking.
- Delta Live Tables: Use DLT to simplify the development of ETL pipelines.
- Databricks Workflows: Orchestrate your data pipelines with Databricks Workflows.
Comparison of Layers
| Layer | Purpose | Data State | Best Practices | Tools/Example |
|---|---|---|---|---|
| Bronze | Raw ingestion | Immutable, append-only | Add metadata, incremental loading | Auto Loader, Delta Lake |
| Silver | Validation and transformation | Cleansed, queryable | Quality expectations, error handling | DLT, Spark SQL |
| Gold | Aggregated for business use | Optimized, semantic | Access controls, materialized views | Unity Catalog, Aggregations |
How Medallion Architecture Supports Data Governance
The structured progression of data through the Medallion Architecture makes it easier to maintain and ensure compliance. Key features that support data governance include:
- Fine-grained Access Controls: With Unity Catalog, you can implement fine-grained access controls at the table, row, and column level.
- Data Lineage: Unity Catalog automatically captures data lineage, providing a clear view of how data flows through the different layers.
- Column-level Security: Protect sensitive data with column-level security policies.
- Views for Abstraction: Use views to abstract the underlying data and provide a secure access layer for different user groups.
At Dateonic, our consulting experience in the logistics and energy sectors has shown that a well-implemented Medallion Architecture significantly improves data governance and reduces compliance risks.
Real-World Examples and Project Insights
Case Study Overview
Based on our experience building AI-ready platforms, a common use case is processing user events from a web application. The Medallion Architecture provides a robust framework for handling this type of data.
Bronze-to-Silver Transformation Example
In the Bronze layer, user events are ingested as raw JSON strings. A view can be created to parse the JSON and extract the relevant fields.
CREATE VIEW user_events_bronze_vw AS
SELECT
value:user_id::string AS user_id,
value:event_type::string AS event_type,
value:event_timestamp::timestamp AS event_timestamp
FROM bronze_table
Silver-to-Gold Aggregation
In the Gold layer, a view can be created to aggregate the data and generate a report of top users by purchase count.
CREATE VIEW top_users_gold_vw AS
SELECT
user_id,
COUNT(*) AS purchase_count
FROM silver_table
WHERE event_type = ’purchase’
GROUP BY user_id
ORDER BY purchase_count DESC
Lessons from Dateonic Projects
Our projects have demonstrated that the Medallion Architecture enhances data governance in scalable data platforms and helps reduce compliance risks. The separation of layers and the use of tools like Unity Catalog and DLT enable organizations to build robust and maintainable data pipelines.
Conclusion
Implementing the Medallion Architecture in Databricks provides a structured and scalable approach to building data pipelines. By following the best practices outlined in this guide, you can improve data quality, enhance data governance, and unlock the full potential of your data.
For expert guidance on your Databricks implementation, contact certified consultants at Dateonic to build governance-strong data pipelines.