Delta Lake introduction in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a Delta Lake table using Spark.

Apache Spark

df = spark.read.format("[1]").load("/delta/events")

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

Adelta

Bparquet

Cjson

Dcsv

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'parquet' or 'csv' instead of 'delta' will not read Delta Lake tables correctly.

Delta Lake tables are read using the format "delta" in Spark.

2fill in blank

medium

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

Apache Spark

df.write.format("delta").mode("[1]").save("/delta/events")

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

Aoverwrite

Berror

Cignore

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'append' will add data instead of replacing it.

Using 'ignore' or 'error' will not overwrite existing data.

To replace existing data, use mode "overwrite" when writing to Delta Lake.

3fill in blank

hard

Fix the error in the code to enable time travel on a Delta Lake table.

Apache Spark

df = spark.read.format("delta").option("[1]", "5").load("/delta/events")

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

AtimestampAsOf

BversionAsOf

CtimeTravel

Dhistory

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'timestampAsOf' requires a timestamp, not a version number.

Options like 'timeTravel' or 'history' are not valid for this purpose.

To read a previous version of a Delta Lake table, use the option "versionAsOf" with the version number.

4fill in blank

hard

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

Apache Spark

df.write.format("[1]").mode("overwrite").option("[2]", "true").save("/delta/events")

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

Adelta

Bparquet

CdeltaSchemaEnforcement

DmergeSchema

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'parquet' format will not create a Delta Lake table.

Option 'deltaSchemaEnforcement' is not a valid Delta Lake option.

Use format "delta" to write Delta Lake tables and option "mergeSchema" set to "true" to enable schema enforcement and evolution.

5fill in blank

hard

Fill all three blanks to create a Delta Lake table, enable schema merge, and read a specific version.

Apache Spark

df.write.format("[1]").mode("append").option("[2]", "true").save("/delta/events")
df2 = spark.read.format("[3]").option("versionAsOf", 3).load("/delta/events")

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

Adelta

BmergeSchema

Dparquet

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'parquet' format will not work for Delta Lake features.

For reading a specific version, the format must be 'delta'.

Write with format "delta" and option "mergeSchema" to allow schema changes. Read with format "delta" and option "versionAsOf" to time travel to version 3.

Practice

1. What is the main purpose of Delta Lake in Apache Spark?

easy

A. To provide reliable data storage with ACID transactions

B. To speed up Spark job execution by caching data

C. To visualize data using charts and graphs

D. To replace Spark SQL with a new query language

Solution

1. Step 1: Understand Delta Lake's role

   Delta Lake adds reliability by supporting ACID transactions on big data.

2. Step 2: Compare options

   Speeding up jobs, visualization, or replacing Spark SQL are not Delta Lake's main goals.

3. Final Answer:

   To provide reliable data storage with ACID transactions -> Option A

4. Quick Check:

   Delta Lake = ACID transactions [OK]

Hint: Delta Lake ensures data correctness with ACID [OK]

Common Mistakes:

• Confusing Delta Lake with caching or visualization tools
• Thinking Delta Lake replaces Spark SQL
• Assuming Delta Lake only speeds up queries

2. Which of the following is the correct way to create a Delta table in Spark using PySpark?

easy

A. df.write.format('delta').save('/path/to/delta-table')

B. df.saveAsTable('delta', '/path/to/delta-table')

C. df.write.save('/path/to/delta-table', format='delta')

D. df.createDeltaTable('/path/to/delta-table')

Solution

1. Step 1: Recall Delta table creation syntax

   In PySpark, to save a DataFrame as a Delta table, use df.write.format('delta').save(path).

2. Step 2: Check each option

   df.write.format('delta').save('/path/to/delta-table') matches the correct syntax; others are invalid or incorrect method calls.

3. Final Answer:

   df.write.format('delta').save('/path/to/delta-table') -> Option A

4. Quick Check:

   Use write.format('delta').save() [OK]

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

Common Mistakes:

• Using saveAsTable with wrong parameters
• Passing format as a keyword argument to save()
• Assuming createDeltaTable is a valid method

3. Given the following PySpark code, what will be the output count after the delete operation?

from delta.tables import DeltaTable

spark.sql("""CREATE TABLE IF NOT EXISTS events (id INT, event STRING) USING DELTA""")
spark.sql("INSERT INTO events VALUES (1, 'start'), (2, 'stop'), (3, 'pause')")
deltaTable = DeltaTable.forName(spark, 'events')
deltaTable.delete("id = 2")
count = spark.sql("SELECT COUNT(*) FROM events").collect()[0][0]
print(count)

medium

A. 3

B. 2

C. 1

D. 0

Solution

1. Step 1: Understand initial data

   The table 'events' has 3 rows with ids 1, 2, and 3.

2. Step 2: Analyze delete operation

   Deleting rows where id = 2 removes one row, leaving 2 rows.

3. Final Answer:

   2 -> Option B

4. Quick Check:

   3 rows - 1 deleted = 2 rows [OK]

Hint: Delete removes matching rows; count reflects remaining [OK]

Common Mistakes:

• Assuming delete removes all rows
• Forgetting to filter by condition in delete
• Confusing insert and delete effects

4. Identify the error in the following PySpark code snippet that tries to update a Delta table:

from delta.tables import DeltaTable

deltaTable = DeltaTable.forPath(spark, '/delta/events')
deltaTable.update(condition="id == 1", set={"event": 'resume'})

medium

A. The update method requires a SQL expression string, not a dictionary

B. The condition should be passed as a SQL expression, not a string

C. The set parameter must use column objects, not strings

D. The update method requires the condition parameter to be a string expression, and set to a dictionary with column names as keys and expressions as values

Solution

1. Step 1: Review update method signature

   DeltaTable.update expects condition as a string expression and set as a dictionary mapping column names to SQL expressions.

2. Step 2: Check given code

   Condition is correct string expression. set uses dict with column name key, but value 'resume' must be SQL expression like "'resume'".

3. Step 3: Identify missing quotes in code snippet

   The value in set lacks quotes for string literal, violating the requirement for expressions as values described in The update method requires the condition parameter to be a string expression, and set to a dictionary with column names as keys and expressions as values.

4. Final Answer:

   The update method requires the condition parameter to be a string expression, and set to a dictionary with column names as keys and expressions as values -> Option D

5. Quick Check:

   Update needs condition string and set dict [OK]

Hint: Update needs condition string and set dict with column keys [OK]

Common Mistakes:

• Passing condition as non-string
• Using incorrect types in set dictionary
• Not quoting string literals in set expressions

5. You have a Delta table with user transactions. You want to keep only the latest transaction per user and remove older duplicates. Which Delta Lake feature helps you do this efficiently?

hard

A. Delta Lake's VACUUM to clean old files

B. Delta Lake's Time Travel to query past versions

C. Delta Lake's MERGE INTO to upsert and deduplicate data

D. Delta Lake's OPTIMIZE to compact small files

Solution

1. Step 1: Understand the goal

   We want to keep only the latest transaction per user, removing duplicates.

2. Step 2: Identify Delta Lake features

   MERGE INTO allows conditional updates and inserts, perfect for upserting and deduplication.

3. Step 3: Exclude other options

   Time Travel queries past data, VACUUM cleans files, OPTIMIZE compacts files, none directly remove duplicates.

4. Final Answer:

   Delta Lake's MERGE INTO to upsert and deduplicate data -> Option C

5. Quick Check:

   Use MERGE INTO for upsert and deduplication [OK]

Hint: Use MERGE INTO to update and remove duplicates efficiently [OK]

Common Mistakes:

• Confusing Time Travel with deduplication
• Thinking VACUUM removes duplicates
• Assuming OPTIMIZE deletes old data