Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Code**: ```python from pyspark.sql import SparkSession spark = SparkSession.builder.appName("csv_load").getOrCreate() df = spark.read.option("header", True).option("inferSchema", True).csv("/path/file.csv") df.write.saveAsTable("my_table") # or createOrReplaceTempView ```...
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like HCL. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, python, spark) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones. The expert answer includes a code example that demonstrates the implementation pattern.
Code:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("csv_load").getOrCreate()
df = spark.read.option("header", True).option("inferSchema", True).csv("/path/file.csv")
df.write.saveAsTable("my_table") # or createOrReplaceTempView
Production: Specify schema (no inferSchema). Partitioning if large. Explicit mode (overwrite/append).
Why Schema: inferSchema scans data; slow and can be wrong. Explicit schema = predictable, faster.
Scalability Trade-offs: inferSchema on 1GB = slow. Partition for large output.
Cost Implications: Schema inference = extra read. Avoid in prod.
Pro-Move: 'StructType schema in config; no inferSchema in prod.' Red Flag: inferSchema in production—slow, schema drift.
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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.