Data quality assertions in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Data Quality Assertion with Null Check

What will be the output of the following Apache Spark code snippet that asserts no null values in the 'age' column?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName('Test').getOrCreate()
data = [(1, 'Alice', 25), (2, 'Bob', None), (3, 'Charlie', 30)]
df = spark.createDataFrame(data, ['id', 'name', 'age'])

# Assert no nulls in 'age'
df.select('age').filter(col('age').isNull()).count() == 0

A Throws an exception

B True

C False

D None

💡 Hint

Check if any rows have null in the 'age' column.

The dataframe contains one row where 'age' is None (null). The filter selects that row, so count() returns 1, making the expression False.

❓ data_output

intermediate

Number of Rows Failing a Data Quality Assertion

Given the following Spark DataFrame, how many rows fail the assertion that 'salary' must be greater than 3000?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName('Test').getOrCreate()
data = [(1, 'John', 4000), (2, 'Jane', 2500), (3, 'Doe', 3500), (4, 'Smith', 3000)]
df = spark.createDataFrame(data, ['id', 'name', 'salary'])

failing_rows = df.filter(col('salary') <= 3000).count()

A 2

B 3

C 1

D 0

💡 Hint

Count rows where salary is less than or equal to 3000.

Rows with salary 2500 and 3000 fail the assertion, so count is 2.

🔧 Debug

advanced

Identify the Error in Data Quality Assertion Code

What error will the following Spark code raise when checking if all 'email' values contain '@'?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName('Test').getOrCreate()
data = [(1, '[e-mail]'), (2, 'bobexample.com')]
df = spark.createDataFrame(data, ['id', 'email'])

# Check if all emails contain '@'
assertion = df.filter(~col('email').contains('@')).count() == 0
print(assertion)

A SyntaxError

B True

C False

D AttributeError

💡 Hint

Check if the 'contains' method is valid on a Column object.

The 'contains' method does not exist on Column objects in PySpark. The correct method is 'like' or 'rlike'. This causes AttributeError.

❓ visualization

advanced

Visualizing Data Quality Assertion Failures

Which plot best shows the count of rows failing a data quality assertion that 'score' must be between 0 and 100?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import col
import matplotlib.pyplot as plt

spark = SparkSession.builder.appName('Test').getOrCreate()
data = [(1, 95), (2, 105), (3, -5), (4, 50)]
df = spark.createDataFrame(data, ['id', 'score'])

fail_df = df.filter((col('score') < 0) | (col('score') > 100))
fail_count = fail_df.count()

# Plot code here

A A line chart showing scores over ids

B A bar chart with one bar labeled 'Failing Rows' showing value 2

C A pie chart with two slices: 'Failing' 2 and 'Passing' 2

D A scatter plot of id vs score

💡 Hint

Count of failing rows is 2; a bar chart can show this count clearly.

Two rows fail the assertion (scores 105 and -5). A bar chart with one bar labeled 'Failing Rows' showing 2 is the clearest visualization of failure count.

🧠 Conceptual

expert

Best Practice for Data Quality Assertions in Spark Pipelines

Which option describes the best practice for implementing data quality assertions in Apache Spark ETL pipelines?

A Run assertions after all transformations and before writing data to storage to catch errors early

B Run assertions only once at the very end of the pipeline to minimize performance impact

C Skip assertions and rely on schema validation only to improve speed

D Run assertions before any transformations to validate raw data only

💡 Hint

Consider when errors should be caught to avoid propagating bad data.

Running assertions after transformations and before writing ensures data quality is verified after all changes, catching errors early and preventing bad data storage.

Practice

1. What is the main purpose of data quality assertions in Apache Spark?

easy

A. To create new columns based on calculations

B. To speed up data processing by caching

C. To check if data meets expected rules and find errors

D. To visualize data trends over time

Solution

1. Step 1: Understand data quality assertions

   Data quality assertions are used to verify if data follows expected rules and to detect errors like missing or invalid values.

2. Step 2: Compare with other options

   Options B, C, and D describe other Spark tasks unrelated to data quality assertions.

3. Final Answer:

   To check if data meets expected rules and find errors -> Option C

4. Quick Check:

   Data quality assertions = check rules and errors [OK]

Hint: Assertions check data rules and errors, not processing speed [OK]

Common Mistakes:

• Confusing assertions with performance optimizations
• Thinking assertions create new data columns
• Mixing assertions with visualization tasks

2. Which of the following is the correct way to assert that a Spark DataFrame column age has no null values?

easy

A. df.filter(df.age.isNull()).count() == 0

B. df.filter(df.age.isNotNull()).count() == 0

C. df.filter(df.age == null).count() == 0

D. df.filter(df.age != null).count() == 0

Solution

1. Step 1: Understand null checking in Spark

   To check for nulls, use isNull(). Counting rows where age is null should be zero for no nulls.

2. Step 2: Analyze each option

   df.filter(df.age.isNull()).count() == 0 correctly checks if count of null age is zero. df.filter(df.age.isNotNull()).count() == 0 checks for non-null count equals zero, which is wrong. Options C and D use null with == or != which is invalid in Spark.

3. Final Answer:

   df.filter(df.age.isNull()).count() == 0 -> Option A

4. Quick Check:

   Use isNull() to find nulls, count should be zero [OK]

Hint: Use isNull() to find nulls, count zero means no nulls [OK]

Common Mistakes:

• Using == null instead of isNull()
• Checking isNotNull() count equals zero
• Confusing null checks with non-null checks

3. Given the following Spark code, what will be the output?

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data = [(1, 'Alice', 25), (2, 'Bob', None), (3, 'Charlie', 30)]
df = spark.createDataFrame(data, ['id', 'name', 'age'])
null_count = df.filter(df.age.isNull()).count()
print(null_count)

medium

A. 0

B. 1

C. 2

D. 3

Solution

1. Step 1: Identify null values in the data

   The data has one row where age is None (null): the second row with Bob.

2. Step 2: Count rows with null age

   The filter df.age.isNull() selects rows with null age, so count will be 1.

3. Final Answer:

   1 -> Option B

4. Quick Check:

   One null age value = 1 [OK]

Hint: Count rows with isNull() to find null entries [OK]

Common Mistakes:

• Counting non-null rows instead of null rows
• Misreading None as a valid age
• Assuming no nulls without checking

4. You wrote this assertion to check if all values in column salary are positive:

assert df.filter(df.salary <= 0).count() == 0

But it passes even when there are null values in the salary column. What is the likely cause?

medium

A. The column salary contains null values, which are skipped by the comparison

B. The filter condition uses <= which is invalid in Spark

C. The count() method is not supported on DataFrames

D. The assert statement syntax is incorrect in Python

Solution

1. Step 1: Understand filter behavior with nulls

   In Spark, null <= 0 evaluates to null, which is falsy in filter(), so null rows are skipped and not counted, causing the assertion to pass incorrectly when nulls exist.

2. Step 2: Eliminate other options

   A: <= is valid in Spark filters. C: count() is supported on DataFrames. D: assert syntax is correct. B identifies the null-skipping issue.

3. Final Answer:

   The column salary contains null values, which are skipped by the comparison -> Option A

4. Quick Check:

   Nulls skipped in comparison filters [OK]

Hint: Nulls yield null in comparisons and are skipped by filter [OK]

Common Mistakes:

• Not realizing nulls are skipped in comparison filters
• Thinking comparisons catch nulls as invalid
• Blaming syntax instead of null handling

5. You want to assert that a DataFrame df has no duplicate user_id values. Which Spark code correctly performs this data quality assertion?

hard

A. assert df.dropDuplicates().count() == df.count()

B. assert df.select('user_id').count() == df.count()

C. assert df.filter(df.user_id.isNull()).count() == 0

D. assert df.groupBy('user_id').count().filter('count > 1').count() == 0

Solution

1. Step 1: Understand duplicate detection logic

   Duplicates mean some user_id appear more than once. Grouping by user_id and counting, then filtering counts > 1 finds duplicates.

2. Step 2: Analyze each option

   A: df.dropDuplicates().count() == df.count() checks for completely identical rows across all columns, missing duplicates in user_id with differing other columns. B: df.select('user_id').count() == df.count() always true. C: Checks only nulls. D: Correctly finds groups with count > 1.

3. Final Answer:

   assert df.groupBy('user_id').count().filter('count > 1').count() == 0 -> Option D

4. Quick Check:

   Group and filter counts > 1 to find duplicates [OK]

Hint: Group by column, filter count > 1 to detect duplicates [OK]

Common Mistakes:

• Using dropDuplicates() without column subset
• Using count() without distinct or groupBy
• Confusing null checks with duplicate checks