Delta Lake vs Parquet in Apache Spark - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a Parquet file into a Spark DataFrame.

Apache Spark

df = spark.read.[1]("/data/sample.parquet")

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

Acsv

Bparquet

Cjson

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using csv or json method to read Parquet files causes errors.

Forgetting to specify the file path as a string.

To read a Parquet file, use spark.read.parquet().

2fill in blank

medium

Complete the code to write a DataFrame as a Delta Lake table.

Apache Spark

df.write.format("[1]").mode("overwrite").save("/data/delta_table")

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

Adelta

Bjson

Cparquet

Dcsv

Attempts:

3 left

💡 Hint

Common Mistakes

Using parquet format to write Delta Lake tables disables Delta features.

Forgetting to specify the format causes default Parquet output.

Delta Lake tables are saved using the delta format.

3fill in blank

hard

Fix the error in the code to read a Delta Lake table.

Apache Spark

df = spark.read.[1]("/data/delta_table")

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

Adelta

Bjson

Cparquet

Dcsv

Attempts:

3 left

💡 Hint

Common Mistakes

Using parquet format to read Delta Lake tables misses transaction logs.

Using json or csv format causes runtime errors.

Reading a Delta Lake table requires the delta format.

4fill in blank

hard

Fill both blanks to create a Delta Lake table and enable schema evolution.

Apache Spark

df.write.format("[1]")
  .mode("append")
  .option("[2]", "true")
  .save("/data/delta_table")

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

Adelta

BmergeSchema

CoverwriteSchema

Dparquet

Attempts:

3 left

💡 Hint

Common Mistakes

Using parquet format disables Delta Lake features.

Using overwriteSchema option instead of mergeSchema causes errors.

Use delta format and mergeSchema option to allow schema evolution when appending.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that filters Parquet files by size and converts file names to uppercase.

Apache Spark

file_info = {file[1]: file_size [2] 1000 for file, file_size in files.items() if file_size [3] 500}

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

A.upper()

B>

C<

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .lower() instead of .upper() changes case incorrectly.

Mixing up comparison operators > and < in conditions.

The comprehension converts file names to uppercase using .upper(), sets the value to file_size > 1000 (boolean), and filters to include only files where file_size < 500.

Practice

1. Which statement best describes the main difference between Delta Lake and Parquet?

easy

A. Delta Lake supports data updates and history tracking, while Parquet is read-only.

B. Parquet supports data updates, but Delta Lake does not.

C. Both Delta Lake and Parquet are read-only file formats.

D. Delta Lake is slower than Parquet for reading data.

Solution

1. Step 1: Understand Parquet's capabilities

   Parquet is a columnar file format optimized for fast read operations but does not support data updates or versioning.

2. Step 2: Understand Delta Lake's enhancements

   Delta Lake builds on Parquet by adding support for data updates, deletes, and tracking changes over time (history).

3. Final Answer:

   Delta Lake supports data updates and history tracking, while Parquet is read-only. -> Option A

4. Quick Check:

   Delta Lake adds update features to Parquet [OK]

Hint: Delta Lake = updates/history; Parquet = read-only [OK]

Common Mistakes:

• Thinking Parquet supports data updates
• Assuming Delta Lake is slower for reads
• Confusing both as identical formats

2. Which of the following is the correct way to write a DataFrame as a Delta Lake table in Apache Spark?

easy

A. df.write.format("parquet").saveAsTable("delta_table")

B. df.write.format("delta").save("path/to/table")

C. df.saveAsTable("delta", "path/to/table")

D. df.write.save("delta", "path/to/table")

Solution

1. Step 1: Recall the syntax for writing Delta tables

   To write a DataFrame as Delta, use df.write.format("delta").save(path).

2. Step 2: Check each option's correctness

   df.write.format("delta").save("path/to/table") matches the correct syntax. df.write.format("parquet").saveAsTable("delta_table") uses Parquet format incorrectly. The remaining options use invalid method signatures.

3. Final Answer:

   df.write.format("delta").save("path/to/table") -> Option B

4. Quick Check:

   Use .write.format("delta") to save Delta tables [OK]

Hint: Use .write.format("delta") to save Delta tables [OK]

Common Mistakes:

• Using saveAsTable with format mismatch
• Confusing Parquet and Delta formats
• Incorrect method chaining

3. Given the following code snippet, what will be the output count after the update?

from delta.tables import DeltaTable

# Load Delta table
deltaTable = DeltaTable.forPath(spark, "/data/delta/events")

# Update rows where eventType is 'click'
deltaTable.update(condition = "eventType = 'click'", set = {"eventType": "click_updated"})

# Count rows with updated eventType
count = spark.read.format("delta").load("/data/delta/events").filter("eventType = 'click_updated'").count()
print(count)

medium

A. Zero, because update does not change data

B. Total number of rows in the table

C. Number of rows where eventType was 'click' before update

D. Number of rows where eventType is 'click_updated' before update

Solution

1. Step 1: Understand the update operation on Delta table

   The update changes all rows with eventType 'click' to 'click_updated'.

2. Step 2: Count rows with eventType 'click_updated'

   After update, the count of 'click_updated' rows equals the original count of 'click' rows.

3. Final Answer:

   Number of rows where eventType was 'click' before update -> Option C

4. Quick Check:

   Update changes 'click' to 'click_updated' rows count [OK]

Hint: Updated rows count = original matching rows count [OK]

Common Mistakes:

• Assuming update does not change data
• Counting total rows instead of filtered
• Confusing pre- and post-update values

4. Identify the error in the following code snippet that tries to update a Parquet file:

parquetPath = "/data/parquet/events"
df = spark.read.format("parquet").load(parquetPath)
df.update(condition = "eventType = 'click'", set = {"eventType": "click_updated"})

medium

A. Parquet format does not support update operation

B. The condition syntax is incorrect

C. The set parameter should be a list, not a dictionary

D. The path variable is not defined

Solution

1. Step 1: Check if Parquet supports update

   Parquet is a static file format and does not support update operations directly.

2. Step 2: Identify the cause of error

   Calling update on a DataFrame loaded from Parquet will cause an error because this method is only available on Delta tables.

3. Final Answer:

   Parquet format does not support update operation -> Option A

4. Quick Check:

   Update works only on Delta, not Parquet [OK]

Hint: Update only works on Delta tables, not Parquet [OK]

Common Mistakes:

• Trying to update Parquet files directly
• Misusing update method on DataFrame
• Ignoring format limitations

5. You have a dataset stored in Parquet format that needs frequent updates and audit history. Which approach is best to handle this requirement efficiently?

hard

A. Store data in JSON files and manually track changes

B. Keep using Parquet and overwrite the entire dataset on each update

C. Use CSV format instead for easier updates

D. Convert the Parquet dataset to Delta Lake format and use Delta's update and versioning features

Solution

1. Step 1: Analyze update and audit needs

   Frequent updates and audit history require a format that supports transactions and versioning.

2. Step 2: Evaluate format options

   Delta Lake supports updates and keeps history, unlike Parquet, CSV, or JSON which lack these features natively.

3. Step 3: Choose the best approach

   Converting Parquet to Delta Lake enables efficient updates and audit tracking without full overwrites.

4. Final Answer:

   Convert the Parquet dataset to Delta Lake format and use Delta's update and versioning features -> Option D

5. Quick Check:

   Delta Lake = updates + history for Parquet data [OK]

Hint: Use Delta Lake for updates and history, not Parquet [OK]

Common Mistakes:

• Overwriting entire Parquet files repeatedly
• Choosing CSV or JSON for update-heavy data
• Ignoring Delta Lake benefits