Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Production-grade example** (with schema, error handling): ```python from pyspark.sql import SparkSession from pyspark.sql.functions import col spark = SparkSession.builder.appName("json_to_parquet").getOrCreate() # Provide schema to avoid inference cost on large reads df =...
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like American Express. 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.
Production-grade example (with schema, error handling):
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
spark = SparkSession.builder.appName("json_to_parquet").getOrCreate()
# Provide schema to avoid inference cost on large reads
df = spark.read.schema("id INT, status STRING, amount DOUBLE") \
.json("s3://bucket/input/*.json")
filtered = df.filter((col("status") == "active") & (col("amount") > 0))
filtered.write.mode("overwrite") \
.parquet("s3://bucket/output/")
Why schema: Inference scans data; explicit schema avoids that on large files. Scalability trade-offs: *.json = many small files = many partitions; coalesce before write to avoid small-file problem. Cost implications: Filter early reduces bytes processed; partition output by date if downstream queries filter by date.
Red Flag: Schema inference on production data. Pro-Move: 'We use schema from Avro/registry; partition output by date for downstream.'
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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.