Medallion architecture (bronze, silver, gold) in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read raw data into the Bronze layer DataFrame.

Apache Spark

bronzeDF = spark.read.format([1]).load("/data/raw/events")

Drag options to blanks, or click blank then click option'

A"json"

B"csv"

C"parquet"

D"text"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'csv' format when data is JSON.

Using 'parquet' format before transformation.

The Bronze layer usually ingests raw JSON data, so the format should be "json".

2fill in blank

medium

Complete the code to clean and transform Bronze data into Silver layer DataFrame.

Apache Spark

silverDF = bronzeDF.filter(bronzeDF.event_type.isNotNull()).selectExpr([1])

Drag options to blanks, or click blank then click option'

A"event_id, user_id, event_value"

B"event_id, event_type, event_timestamp"

C"user_id, event_type, event_location"

D"event_timestamp, event_value, event_location"

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting irrelevant columns for Silver layer.

Not filtering out null event_type values.

Silver layer keeps cleaned and structured columns like event_id, user_id, and event_value.

3fill in blank

hard

Fix the error in the code that writes Silver data to the Silver table in Delta format.

Apache Spark

silverDF.write.format([1]).mode("overwrite").saveAsTable("silver_events")

Drag options to blanks, or click blank then click option'

A"json"

B"csv"

C"parquet"

D"delta"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'json' or 'csv' format which do not support ACID.

Using 'parquet' without Delta features.

The Silver layer is stored in Delta format for ACID transactions and versioning.

4fill in blank

hard

Fill both blanks to create a Gold layer table with aggregated data.

Apache Spark

goldDF = silverDF.groupBy([1]).agg({'event_value': [2])

Drag options to blanks, or click blank then click option'

A"user_id"

B"sum"

C"avg"

D"event_type"

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by event_type instead of user_id.

Using avg instead of sum for aggregation.

Gold layer aggregates event_value by user_id using sum for total value per user.

5fill in blank

hard

Fill all three blanks to write the Gold DataFrame as a Delta table with partitioning.

Apache Spark

goldDF.write.format([1]).mode("overwrite").partitionBy([2]).saveAsTable([3])

Drag options to blanks, or click blank then click option'

A"delta"

B"event_type"

C"gold_aggregated_events"

D"user_id"

Attempts:

3 left

💡 Hint

Common Mistakes

Not partitioning the Gold table.

Using wrong table name or format.

Gold data is saved as Delta table partitioned by user_id for efficient queries.

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