Medallion architecture (bronze, silver, gold) in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Bronze layer in Medallion architecture?

The Bronze layer is the raw data layer where data is ingested as-is from source systems. It contains original, unprocessed data for traceability and auditing.

beginner

What happens in the Silver layer of Medallion architecture?

The Silver layer contains cleaned and enriched data. It removes errors, duplicates, and applies transformations to make data more reliable and usable.

beginner

Describe the Gold layer in Medallion architecture.

The Gold layer holds highly curated, aggregated, and business-level data. It is optimized for analytics, reporting, and machine learning use cases.

intermediate

Why use Medallion architecture in data engineering?

It organizes data into layers for better quality control, easier debugging, and clear separation of raw, cleaned, and business-ready data.

intermediate

How does Medallion architecture improve data reliability?

By progressively cleaning and enriching data through Bronze, Silver, and Gold layers, it ensures data is accurate and trustworthy at each stage.

Which Medallion layer contains raw, unprocessed data?

A Bronze

B Silver

C Gold

D Platinum

The Bronze layer stores raw data exactly as it arrives from the source.

What is the main purpose of the Silver layer?

A Store raw data

B Clean and enrich data

C Aggregate data for reports

D Archive old data

The Silver layer cleans and enriches data to improve quality.

Which layer is best suited for business analytics and machine learning?

A Gold

B Bronze

C Silver

D Raw

The Gold layer contains curated, aggregated data optimized for analytics.

What is a key benefit of using Medallion architecture?

A Faster data ingestion only

B No need for data cleaning

C Single data storage layer

D Clear data quality stages

Medallion architecture clearly separates data quality stages for better management.

In which layer would you expect to find duplicate data removed?

A Bronze

B None

C Silver

D Gold

Duplicates are removed during the Silver layer cleaning process.

Explain the purpose and characteristics of each Medallion architecture layer: Bronze, Silver, and Gold.

Think about how data quality improves from raw to business-ready.

Describe how Medallion architecture helps improve data quality and usability in a data pipeline.

Consider the flow of data from source to analytics.

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