Medallion architecture (bronze, silver, gold) in Apache Spark

Introduction

The Medallion architecture helps organize data in layers to make it easier to clean, improve, and analyze step by step.

When you get raw data from many sources and want to clean it before analysis.

When you want to improve data quality gradually and keep track of changes.

When you need to prepare data for reports or machine learning models.

When you want to separate data processing into clear stages for better management.

Syntax

Apache Spark

bronzeDF = spark.read.format('json').load('/path/bronze')
silverDF = bronzeDF.filter('quality = true')
goldDF = silverDF.groupBy('category').agg({'sales': 'sum'})

Bronze layer stores raw or minimally processed data.

Silver layer contains cleaned and filtered data.

Gold layer holds aggregated or business-level data ready for use.

Examples

Read raw JSON data, remove duplicates in silver, then sum revenue by product in gold.

Apache Spark

bronzeDF = spark.read.json('/data/bronze')
silverDF = bronzeDF.dropDuplicates()
goldDF = silverDF.groupBy('product').sum('revenue')

Load raw CSV, filter active users in silver, select needed columns in gold.

Apache Spark

bronzeDF = spark.read.format('csv').load('/data/bronze')
silverDF = bronzeDF.filter("status = 'active'")
goldDF = silverDF.select('user_id', 'purchase_amount')

Sample Program

This example shows how to start with raw data (bronze), filter good quality rows (silver), and then sum sales by product (gold).

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('MedallionExample').getOrCreate()

# Bronze layer: load raw data
bronzeDF = spark.createDataFrame([
    (1, 'apple', 10, True),
    (2, 'banana', 5, False),
    (3, 'apple', 15, True),
    (4, 'banana', 7, True)
], ['id', 'product', 'sales', 'quality'])

# Silver layer: filter only good quality data
silverDF = bronzeDF.filter('quality = true')

# Gold layer: aggregate sales by product
from pyspark.sql.functions import sum as sum_
goldDF = silverDF.groupBy('product').agg(sum_('sales').alias('total_sales'))

goldDF.show()

Important Notes

Each layer builds on the previous one to improve data quality and usefulness.

Bronze data is often large and raw; silver is cleaner; gold is ready for business use.

Using this architecture helps teams work together and track data changes easily.

Summary

The Medallion architecture organizes data into bronze, silver, and gold layers.

Bronze is raw data, silver is cleaned data, and gold is aggregated or business-ready data.

This approach makes data easier to manage and analyze step by step.

Practice

1. What is the primary purpose of the bronze layer in the Medallion architecture?

easy

A. Store metadata about data quality

B. Store cleaned and enriched data

C. Store raw, unprocessed data as it arrives

D. Store aggregated business-level data

Solution

1. Step 1: Understand the role of the bronze layer

   The bronze layer is the first stage where raw data is ingested without transformation.

2. Step 2: Differentiate from silver and gold layers

   Silver contains cleaned data, and gold contains aggregated data, so they are not raw.

3. Final Answer:

   Store raw, unprocessed data as it arrives -> Option C

4. Quick Check:

   Bronze = Raw data [OK]

Hint: Bronze = raw data layer, first step [OK]

Common Mistakes:

• Confusing bronze with silver or gold layers
• Thinking bronze stores aggregated data
• Assuming bronze is cleaned data

2. Which of the following is the correct order of layers in the Medallion architecture from raw to business-ready data?

easy

A. Bronze -> Silver -> Gold

B. Silver -> Bronze -> Gold

C. Gold -> Silver -> Bronze

D. Silver -> Gold -> Bronze

Solution

1. Step 1: Recall the Medallion architecture flow

   Data flows from raw to cleaned to aggregated: bronze, then silver, then gold.

2. Step 2: Match the order to options

   Bronze -> Silver -> Gold correctly lists bronze first, then silver, then gold.

3. Final Answer:

   Bronze -> Silver -> Gold -> Option A

4. Quick Check:

   Order = Bronze, Silver, Gold [OK]

Hint: Remember: Bronze first, then Silver, then Gold [OK]

Common Mistakes:

• Reversing the order of layers
• Mixing silver and bronze positions
• Assuming gold comes before silver

3. Given the following PySpark code snippet, which layer does the resulting DataFrame most likely represent?

bronzeDF = spark.read.format('json').load('/data/raw/events')
silverDF = bronzeDF.filter('event_type IS NOT NULL').dropDuplicates()
goldDF = silverDF.groupBy('event_type').count()

medium

A. goldDF is gold layer

B. silverDF is bronze layer

C. bronzeDF is gold layer

D. bronzeDF is silver layer

Solution

1. Step 1: Analyze each DataFrame's transformation

   bronzeDF loads raw JSON data (raw data = bronze). silverDF filters and removes duplicates (cleaned data = silver). goldDF groups and counts (aggregated data = gold).

2. Step 2: Identify the layer of goldDF

   goldDF performs aggregation, which matches the gold layer's purpose.

3. Final Answer:

   goldDF is gold layer -> Option A

4. Quick Check:

   Aggregation = Gold layer [OK]

Hint: Aggregation means gold layer [OK]

Common Mistakes:

• Confusing raw load as silver
• Thinking filtering is aggregation
• Mixing up silver and gold transformations

4. You have a silver layer DataFrame but notice it still contains duplicate records. Which change will best fix this issue in your Spark code?

medium

A. Add .dropDuplicates() after loading raw data in bronze layer

B. Add .dropDuplicates() after cleaning transformations in silver layer

C. Add .groupBy() in bronze layer to aggregate data

D. Add .filter() in gold layer to remove duplicates

Solution

1. Step 1: Understand where duplicates should be removed

   Duplicates should be removed during cleaning, which happens in the silver layer.

2. Step 2: Evaluate options for removing duplicates

   Adding .dropDuplicates() in silver layer after cleaning transformations is the correct fix.

3. Final Answer:

   Add .dropDuplicates() after cleaning transformations in silver layer -> Option B

4. Quick Check:

   Remove duplicates in silver layer [OK]

Hint: Clean data (silver) removes duplicates [OK]

Common Mistakes:

• Removing duplicates too early in bronze layer
• Using groupBy instead of dropDuplicates
• Filtering duplicates only in gold layer

5. You want to build a dashboard showing total sales by region updated daily. Which Medallion architecture layer should you query for the most accurate and business-ready data?

hard

A. Query the bronze layer for raw sales data

B. Query the bronze layer after filtering duplicates

C. Query the silver layer for cleaned sales data

D. Query the gold layer for aggregated sales data

Solution

1. Step 1: Identify the dashboard data needs

   The dashboard requires aggregated, business-ready data (total sales by region).

2. Step 2: Match data needs to Medallion layers

   Gold layer contains aggregated and business-level data, ideal for dashboards.

3. Final Answer:

   Query the gold layer for aggregated sales data -> Option D

4. Quick Check:

   Dashboard needs gold layer data [OK]

Hint: Dashboards use gold layer data [OK]

Common Mistakes:

• Using raw or cleaned data instead of aggregated
• Querying bronze layer directly for reports
• Filtering bronze layer instead of using gold