Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Code**: ```python from pyspark.sql.functions import when, col spark = SparkSession.builder.getOrCreate() df = spark.read.parquet("/path") result = df.filter(col("region") == "US").withColumn("category", when(col("amount") > 100, "high").otherwise("low")) ``` **Why**: Filter...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Bristol Myers Squibb. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (python, spark, sql) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example. The expert answer includes a code example that demonstrates the implementation pattern.
Code:
from pyspark.sql.functions import when, col
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/path")
result = df.filter(col("region") == "US").withColumn("category", when(col("amount") > 100, "high").otherwise("low"))
Why: Filter before calculated column reduces data. when/otherwise = built-in, optimized.
Scalability Trade-offs: Filter first. Built-in over UDF.
Cost Implications: Filter early = less data. when/otherwise = codegen.
Pro-Move: 'Filter then withColumn; we process 50% less rows.' Red Flag: withColumn before filter—processes full table.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.