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Create a DataFrame with default column types

Spark/Big Dataeasy0.4 min read

**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([],...

🤖 Analyze Your Answer
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
KPMG
Key Concepts Tested
pythonsparksql

Why This Question Matters

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.

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
80 wordsIncludes code

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.

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Companies that ask this Spark/Big Data question

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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 a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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