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Write PySpark code to filter records based on specific conditions and add a calculated column.

Spark/Big Dataeasy0.3 min read

**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...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Bristol Myers Squibb
Key Concepts Tested
pythonsparksql

Why This Question Matters

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.

How to Approach This

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.

Expert Answer
51 wordsIncludes code

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.

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