Delta Lake introduction in Apache Spark - Mini Project: Build & Apply

Delta Lake Introduction with Apache Spark

📖 Scenario: You work as a data analyst at a retail company. You receive daily sales data as CSV files. You want to store this data efficiently and safely so you can update it easily and query it fast. Your team decides to use Delta Lake on Apache Spark to manage this data.

🎯 Goal: Build a simple Delta Lake table from a small dataset, configure a write mode, and read the data back to see the results.

📋 What You'll Learn

Create a Spark DataFrame with sample sales data

Write the DataFrame to a Delta Lake table

Set the write mode to 'overwrite' to replace existing data

Read the Delta Lake table back into a DataFrame

Show the contents of the DataFrame

💡 Why This Matters

🌍 Real World

Delta Lake helps companies store and manage large amounts of data reliably. It supports updates, deletes, and fast queries, which are important for daily business reports and analytics.

💼 Career

Data engineers and data analysts use Delta Lake to build robust data pipelines and ensure data quality in big data environments.

1

Create a Spark DataFrame with sales data

Create a Spark DataFrame called sales_df with these exact rows: ("2024-06-01", "StoreA", 100), ("2024-06-01", "StoreB", 150), and ("2024-06-02", "StoreA", 200). Use columns named date, store, and sales.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DeltaLakeIntro").getOrCreate()
# Create the sales_df DataFrame with the specified data and columns
# Your code here

Hint

Use spark.createDataFrame(data, schema=columns) where data is a list of tuples and columns is a list of column names.

2

Set the Delta Lake write mode

Create a variable called write_mode and set it to the string "overwrite" to replace existing data when writing.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DeltaLakeIntro").getOrCreate()
data = [("2024-06-01", "StoreA", 100), ("2024-06-01", "StoreB", 150), ("2024-06-02", "StoreA", 200)]
columns = ["date", "store", "sales"]
sales_df = spark.createDataFrame(data, schema=columns)
# Create a variable write_mode and set it to "overwrite"
# Your code here

Hint

Just assign the string "overwrite" to the variable write_mode.

3

Write the DataFrame to a Delta Lake table

Use sales_df.write.format("delta") with mode(write_mode) to write the data to a Delta Lake table at path "/tmp/delta/sales".

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DeltaLakeIntro").getOrCreate()
data = [("2024-06-01", "StoreA", 100), ("2024-06-01", "StoreB", 150), ("2024-06-02", "StoreA", 200)]
columns = ["date", "store", "sales"]
sales_df = spark.createDataFrame(data, schema=columns)
write_mode = "overwrite"
# Write sales_df to Delta Lake at "/tmp/delta/sales" using write_mode
# Your code here

Hint

Use sales_df.write.format("delta").mode(write_mode).save(path) to write the DataFrame.

4

Read and display the Delta Lake table

Read the Delta Lake table from "/tmp/delta/sales" into a DataFrame called delta_df using spark.read.format("delta"). Then print the contents of delta_df using show().

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DeltaLakeIntro").getOrCreate()
data = [("2024-06-01", "StoreA", 100), ("2024-06-01", "StoreB", 150), ("2024-06-02", "StoreA", 200)]
columns = ["date", "store", "sales"]
sales_df = spark.createDataFrame(data, schema=columns)
write_mode = "overwrite"
sales_df.write.format("delta").mode(write_mode).save("/tmp/delta/sales")
# Read the Delta Lake table into delta_df and show its contents
# Your code here

Hint

Use spark.read.format("delta").load(path) to read the table, then show() to display.

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