Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Code**: ```python from pyspark.sql import SparkSession from pyspark.sql.functions import col spark = SparkSession.builder.getOrCreate() df = spark.read.parquet("/path/to/data") filtered = df.filter((col("status") == "active") & (col("amount") > 100)) count = filtered.count()...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Bitwise. 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 import SparkSession
from pyspark.sql.functions import col
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet("/path/to/data")
filtered = df.filter((col("status") == "active") & (col("amount") > 100))
count = filtered.count()
Why: Filter pushdown to Parquet. Count is action. Use column objects for complex conditions.
Scalability Trade-offs: Push filter to source. Avoid UDF in filter.
Cost Implications: Pushdown = less I/O. Simple filter = efficient.
Pro-Move: 'Filter on partition column first; we prune 80% of data.' Red Flag: count() without filter when filtered count needed—full scan.
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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.