Medallion architecture (bronze, silver, gold) in Apache Spark - Time & Space Complexity

Time Complexity: Medallion architecture (bronze, silver, gold)

O(n)

Understanding Time Complexity

We want to understand how the time to process data grows as the data size increases in the medallion architecture.

How does the processing time change when moving data through bronze, silver, and gold layers?

Scenario Under Consideration

Analyze the time complexity of the following Apache Spark code snippet.

// Read raw data into bronze layer
val bronzeDF = spark.read.format("json").load("/data/bronze")

// Clean and filter data for silver layer
val silverDF = bronzeDF.filter("value IS NOT NULL")
  .dropDuplicates()

// Aggregate for gold layer
val goldDF = silverDF.groupBy("category").count()

This code reads raw data, cleans it, and then aggregates it in three steps representing bronze, silver, and gold layers.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Data scanning and transformations on the dataset at each layer.
• How many times: Each operation processes all rows once per layer (bronze, silver, gold).

How Execution Grows With Input

As the input data size grows, each layer processes all rows, so the total work grows roughly in proportion to the data size.

Input Size (n)	Approx. Operations
10	About 30 (3 layers x 10 rows)
100	About 300 (3 layers x 100 rows)
1000	About 3000 (3 layers x 1000 rows)

Pattern observation: The total operations grow linearly with input size, multiplied by the number of layers.

Final Time Complexity

Time Complexity: O(n)

This means the processing time grows roughly in direct proportion to the amount of data.

Common Mistake

[X] Wrong: "Because there are three layers, the time complexity is cubic or grows much faster than the data size."

[OK] Correct: Each layer processes the data once, so the total work adds up linearly, not multiplies exponentially.

Interview Connect

Understanding how data flows through layers and how processing time scales helps you explain real-world data pipelines clearly and confidently.

Self-Check

"What if the silver layer included a join with another large dataset? How would the time complexity change?"

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