Medallion Architecture Best Practices in Databricks: A Technical Deep Dive

Getting Started

Medallion Architecture has become a proven paradigm for building robust, scalable data pipelines in Databricks. By organizing data into three distinct layers—Bronze, Silver, and Gold—this approach ensures that raw data is preserved, cleansed data is reliable, and business-ready insights are optimized for consumption. In this blog, we dive into the nuances of designing your Medallion Architecture, exploring catalog separation, data integrity, incremental processing, and performance optimizations with practical code examples.

Overview of Medallion Architecture Layers

Before discussing best practices, it’s important to understand the core responsibilities of each layer:

Bronze Layer:
Captures raw, unprocessed data directly from the source. Its primary goal is to preserve the original data, including metadata like ingestion timestamps, which is essential for auditing and reprocessing.
Silver Layer:
Transforms and cleanses data from the Bronze layer. This involves deduplication, filtering, and schema standardization, ensuring data quality and consistency for downstream processes.
Gold Layer:
Aggregates, enriches, and optimizes data for business analytics and reporting. It delivers actionable insights by preparing data in a form that is directly consumable by BI tools and data scientists.

The following table summarizes the key elements and best practices for each layer:

Layer	Purpose	Key Best Practices	Example Reference
Bronze	Ingest raw data with full fidelity.	Preserve original data; include metadata (e.g., ingestion timestamps); store as-is for audit/reprocessing.	Raw data ingestion code snippet.
Silver	Cleanse and transform data.	Deduplicate records; filter out invalid entries; standardize schema; perform necessary data quality checks.	Data transformation code snippet.
Gold	Aggregate and enrich data for business use.	Compute aggregates; derive business metrics; optimize data layout for faster queries; ensure data accuracy.	Aggregation and optimization code snippet.

Catalog Separation and Access Control

A critical design decision in Databricks is how to organize your layers in terms of catalog structure. Options include using separate catalogs for each layer or a single catalog with multiple schemas. Consider these factors:

Security & Governance:
Isolating layers by catalog allows for granular access control. For example, raw data in the Bronze layer can be restricted to a smaller group of data engineers, while the Gold layer is accessible to business analysts.
Metadata Management:
Separate catalogs can reduce metadata clutter and improve query planning. In larger environments, this separation streamlines management and troubleshooting.
Operational Clarity:
Clear separation simplifies debugging and ensures that the data lineage is transparent from raw ingestion through to business insights.

Example – Creating Separate Catalogs (SQL):

-- Creating catalogs for each medallion layer to enforce governance and improve metadata management.
CREATE CATALOG IF NOT EXISTS bronze_catalog;
CREATE CATALOG IF NOT EXISTS silver_catalog;
CREATE CATALOG IF NOT EXISTS gold_catalog;

-- Organizing tables within each catalog using descriptive schemas.
CREATE SCHEMA IF NOT EXISTS bronze_catalog.raw_data;
CREATE SCHEMA IF NOT EXISTS silver_catalog.processed_data;
CREATE SCHEMA IF NOT EXISTS gold_catalog.analytics;

Best Practices for the Bronze Layer

Preserve Data Integrity

The Bronze layer is your source of truth. It must capture every nuance of the raw data, including potential inconsistencies. This archived data is critical for audits and future reprocessing.

Example – Ingesting Raw JSON Data:

# Bronze Layer: Ingest raw JSON data and append an ingestion timestamp.
from pyspark.sql.functions import current_timestamp

# Read raw data from an external source
df_raw = spark.read.json("/mnt/data/raw_data.json")

# Append an ingestion timestamp to preserve context
df_bronze = df_raw.withColumn("ingest_time", current_timestamp())

# Write raw data to the Bronze catalog/table
df_bronze.write.format("delta") \
  .mode("overwrite") \
  .saveAsTable("bronze_catalog.raw_data.raw_table")  # Saving raw data with ingestion metadata

Insight: Preserving raw data enables you to reprocess historical records if upstream sources change or errors are detected.

Best Practices for the Silver Layer

Cleanse and Standardize Data

Transformations in the Silver layer are essential for ensuring data quality. This is where you deduplicate, filter, and standardize data to produce a reliable dataset.

Example – Transforming Data from Bronze to Silver:

# Silver Layer: Clean and transform data from the Bronze layer.
df_bronze = spark.table("bronze_catalog.raw_data.raw_table")

# Filter active records, remove duplicates, and rename columns for clarity
df_silver = (df_bronze.filter("status = 'active'")
                      .dropDuplicates(["id"])
                      .withColumnRenamed("old_column", "new_column"))  #  Standardizing column names

# Save the cleansed data to the Silver catalog/table
df_silver.write.format("delta") \
  .mode("overwrite") \
  .saveAsTable("silver_catalog.processed_data.silver_table")  #  Writing cleaned data to Silver layer

Insight: Data quality starts with thoughtful transformations. Engage with domain experts to ensure that the cleansing logic aligns with business requirements.

Best Practices for the Gold Layer

Aggregate and Optimize Data

The Gold layer is where you turn clean data into actionable insights. Aggregation, enrichment, and performance optimizations ensure that the final dataset meets the demands of business analytics.

Example – Aggregating Sales Data:

# Gold Layer: Aggregate and prepare business-ready metrics.
df_silver = spark.table("silver_catalog.processed_data.silver_table")

# Aggregate data to compute total sales per category
df_gold = (df_silver.groupBy("category")
                      .agg({"sales": "sum"})
                      .withColumnRenamed("sum(sales)", "total_sales"))  #  Aggregating sales data

# Write the aggregated data to the Gold catalog/table
df_gold.write.format("delta") \
  .mode("overwrite") \
  .saveAsTable("gold_catalog.analytics.gold_table")  #  Saving aggregated metrics in Gold layer

Insight: Collaboration with business users is crucial in this phase to validate that the metrics truly reflect business performance and drive decision-making.

Optimizations and Incremental Processing

Optimize Data Layout

As your datasets grow, query performance becomes critical. Leverage Delta Lake optimizations like Z-Ordering to physically cluster related data, reducing query times.

Example – Optimizing a Silver Table:

-- SQL: Optimize the Silver table for faster queries using Z-Ordering on frequently queried columns.
OPTIMIZE silver_catalog.processed_data.silver_table ZORDER BY (customer_id);

Z-Ordering clusters data for improved query performance on common filters.*

Embrace Incremental Processing

For large datasets, reprocessing the entire dataset is inefficient. Instead, implement incremental processing to update only the new or modified records, ensuring cost efficiency and reduced latency.

Example – Incremental Merge for the Silver Layer:

from delta.tables import DeltaTable

# Read incremental updates from the raw data source
bronze_updates = spark.read.format("delta").load("/mnt/data/new_raw_data")

# Load the existing Silver table
silver_table = DeltaTable.forName(spark, "silver_catalog.processed_data.silver_table")

# Merge new and updated records into the Silver table
(silver_table.alias("silver")
  .merge(
      bronze_updates.alias("bronze"),
      "silver.id = bronze.id")
  .whenMatchedUpdateAll()
  .whenNotMatchedInsertAll()
  .execute())  #  Incremental merge for efficient data updates

Insight: Incremental processing ensures that your pipeline remains responsive and cost-effective, even as data volumes scale.

Tecyfy Takeaway

Implementing Medallion Architecture in Databricks is about more than just dividing data into Bronze, Silver, and Gold layers. It involves making informed design decisions—whether through catalog separation, rigorous data cleansing, or performance optimizations—that drive data quality and operational efficiency.

Preserve Raw Data: The Bronze layer is your immutable source of truth.
Transform Thoughtfully: The Silver layer ensures data quality and consistency.
Deliver Insights: The Gold layer aggregates and optimizes data for business analytics.
Optimize and Scale: Leverage Delta Lake optimizations and incremental processing for efficient, high-performance pipelines.

By following these best practices, you can build a Medallion Architecture that meets the technical and business demands of modern data engineering in Databricks.