Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Example**: ```python from pyspark.sql import SparkSession from pyspark.sql.types import StructType, StructField, StringType, IntegerType schema = StructType([ StructField("id", IntegerType()), StructField("name", StringType()), ]) df = spark.createDataFrame([],...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like KPMG. 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.
Example:
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
schema = StructType([
StructField("id", IntegerType()),
StructField("name", StringType()),
])
df = spark.createDataFrame([], schema)
# Or DDL: spark.createDataFrame([], "id int, name string")
Why it matters: Empty DataFrame with schema enables downstream operations without data; used for union, schema validation. Scalability trade-offs: Creating large DataFrames in driver = OOM; use RDD or read from source. Best practice: Use for testing, union with same schema; avoid creating large in-memory DataFrames.
Red Flag: createDataFrame with list of millions of rows. Pro-Move: 'We use createDataFrame([], schema) for empty partitions in union.'
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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.