Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Code**: ```python from pyspark.sql.functions import col filtered = df.filter(col("status") == "active") # Or: df.filter("status = 'active'") # Multiple: df.filter((col("a") > 0) & (col("b") < 10)) ``` **Why**: Predicate pushdown to Parquet. Chain filters. Avoid UDF in...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Fragma Data Systems. 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 col
filtered = df.filter(col("status") == "active")
# Or: df.filter("status = 'active'")
# Multiple: df.filter((col("a") > 0) & (col("b") < 10))
Why: Predicate pushdown to Parquet. Chain filters. Avoid UDF in filter.
Scalability Trade-offs: Pushdown = skip row groups. Column objects for complex logic.
Cost Implications: Pushdown = 50–90% I/O reduction. Critical for large tables.
Pro-Move: 'Filter on partition columns first; EXPLAIN shows PushedFilters.' Red Flag: UDF in filter—breaks pushdown, 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.