Delta Lake vs Parquet in Apache Spark

Introduction

Delta Lake and Parquet are ways to store data. Delta Lake adds extra features to Parquet to make data easier to manage and update.

When you want to store large amounts of data in a fast and efficient way.

When you need to update or delete data easily without rewriting everything.

When you want to keep track of changes to your data over time.

When you want to make sure your data is consistent and reliable.

When you want to run fast queries on big data.

Syntax

Apache Spark

spark.read.format("delta").load("path_to_delta_table")
spark.read.format("parquet").load("path_to_parquet_file")

Delta Lake uses the format "delta" in Spark to read and write data.

Parquet is a file format and uses "parquet" in Spark to read and write data.

Examples

This loads a Delta Lake table and shows its content.

Apache Spark

df_delta = spark.read.format("delta").load("/data/delta/events")
df_delta.show()

This loads a Parquet file and shows its content.

Apache Spark

df_parquet = spark.read.format("parquet").load("/data/parquet/events")
df_parquet.show()

This writes data to a Delta Lake table, replacing existing data.

Apache Spark

df_delta.write.format("delta").mode("overwrite").save("/data/delta/events")

This writes data to a Parquet file, replacing existing data.

Apache Spark

df_parquet.write.format("parquet").mode("overwrite").save("/data/parquet/events")

Sample Program

This program creates a small dataset and saves it as both Parquet and Delta Lake formats. It then reads and shows both. Finally, it updates one record in the Delta Lake table and shows the updated data. Parquet files cannot be updated like this easily.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DeltaVsParquetExample").getOrCreate()

# Create sample data
data = [(1, "Alice"), (2, "Bob"), (3, "Cathy")]
columns = ["id", "name"]
df = spark.createDataFrame(data, columns)

# Write data as Parquet
parquet_path = "/tmp/parquet_example"
df.write.mode("overwrite").format("parquet").save(parquet_path)

# Write data as Delta
delta_path = "/tmp/delta_example"
df.write.mode("overwrite").format("delta").save(delta_path)

# Read Parquet data
df_parquet = spark.read.format("parquet").load(parquet_path)
print("Parquet data:")
df_parquet.show()

# Read Delta data
df_delta = spark.read.format("delta").load(delta_path)
print("Delta data:")
df_delta.show()

# Update Delta data (change name where id=2)
from delta.tables import DeltaTable

delta_table = DeltaTable.forPath(spark, delta_path)
delta_table.update(condition = "id = 2", set = {"name": "Bobby"})

# Show updated Delta data
print("Updated Delta data:")
df_delta_updated = spark.read.format("delta").load(delta_path)
df_delta_updated.show()

spark.stop()

Important Notes

Delta Lake supports updates, deletes, and time travel (viewing past data versions), unlike Parquet.

Parquet files are simple and fast for reading but do not support easy data changes.

Delta Lake stores extra information to manage data changes and ensure consistency.

Summary

Parquet is a fast, columnar file format for storing data.

Delta Lake builds on Parquet by adding features for data updates and reliability.

Use Delta Lake when you need to change data or track history; use Parquet for simple, read-only data storage.

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