Delta Lake vs Parquet in Apache Spark - Visual Side-by-Side Comparison

Concept Flow - Delta Lake vs Parquet

Start: Raw Data

↓

Save as Parquet

↓

Read Parquet

↓

Limitations: No ACID, No Time Travel

↓

Save as Delta Lake

↓

Read Delta Lake

↓

Benefits: ACID, Time Travel, Schema Evolution

Shows the flow from saving raw data as Parquet and Delta Lake, highlighting Delta Lake's added features.

Execution Sample

Apache Spark

df.write.format('parquet').save('/path/parquet')
df.write.format('delta').save('/path/delta')
df_parquet = spark.read.format('parquet').load('/path/parquet')
df_delta = spark.read.format('delta').load('/path/delta')

Writes data as Parquet and Delta Lake formats, then reads them back for comparison.

Execution Table

Step	Action	Format	Feature	Result
1	Write data	Parquet	No ACID, No Time Travel	Data saved as Parquet files
2	Write data	Delta Lake	Supports ACID, Time Travel	Data saved as Delta Lake format
3	Read data	Parquet	Simple read	Data loaded, no versioning
4	Read data	Delta Lake	Versioned read	Data loaded with version control
5	Update data	Parquet	No ACID	Manual overwrite needed, risk of corruption
6	Update data	Delta Lake	ACID transactions	Safe update with transaction support
7	Schema change	Parquet	No schema evolution	Manual schema management required
8	Schema change	Delta Lake	Schema evolution supported	Schema updated automatically
9	Time travel query	Parquet	Not supported	Cannot query past versions
10	Time travel query	Delta Lake	Supported	Can query historical data versions
11	Exit	-	-	Comparison complete

💡 All key differences between Parquet and Delta Lake demonstrated

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
Data Format	Raw Data	Parquet files created	Delta Lake files created	Parquet data loaded	Delta Lake data loaded	Comparison done
ACID Support	No	No	Yes	No	Yes	Yes
Time Travel	No	No	Yes	No	Yes	Yes
Schema Evolution	No	No	Yes	No	Yes	Yes

Key Moments - 3 Insights

Why can't we do safe updates with Parquet like with Delta Lake?

Parquet files lack ACID transaction support, so updates require manual overwrite risking data corruption, as shown in steps 5 and 6.

What does 'time travel' mean in Delta Lake?

Time travel means querying past versions of data safely, which Parquet does not support, as seen in steps 9 and 10.

How does schema evolution differ between Parquet and Delta Lake?

Delta Lake supports automatic schema changes, while Parquet requires manual handling, demonstrated in steps 7 and 8.

Visual Quiz - 3 Questions

Look at the execution table, at which step does Delta Lake show support for ACID transactions?

A Step 5

B Step 6

C Step 2

D Step 9

💡 Hint

Check the 'Feature' column for ACID support during update actions in steps 5 and 6.

According to the variable tracker, which feature is supported by Delta Lake but not by Parquet?

A Simple read

B Manual overwrite

C Time Travel

D No schema evolution

💡 Hint

Look at the 'Time Travel' row in the variable tracker after Step 4.

If we remove schema evolution support from Delta Lake, which step in the execution table would change?

A Step 8

B Step 9

C Step 7

D Step 10

💡 Hint

Schema evolution is discussed in steps 7 and 8; focus on the 'Feature' column.

Concept Snapshot

Delta Lake vs Parquet:
- Parquet: columnar storage, no ACID, no time travel
- Delta Lake: built on Parquet, adds ACID transactions
- Supports time travel (query past data versions)
- Supports schema evolution automatically
- Safer updates and deletes with transaction support

Full Transcript

This visual execution compares Delta Lake and Parquet formats. We start with raw data saved as Parquet and Delta Lake. Parquet files lack ACID transactions and time travel, so updates are risky and schema changes manual. Delta Lake adds ACID support, allowing safe updates and deletes, supports querying past data versions (time travel), and handles schema evolution automatically. The execution table traces writing, reading, updating, and schema changes step-by-step. The variable tracker shows how features differ after each step. Key moments clarify common confusions about ACID, time travel, and schema evolution. The quiz tests understanding of these differences using the execution visuals.

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