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Code a simple PySpark job to read a JSON file, filter records, and write output in Parquet format.

Spark/Big Datamedium0.5 min readPremium
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
American Express
Key Concepts Tested
partitionpythonsparksql
Expert AnswerPremium
97 wordsIncludes code examplesInterview-ready
**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 = 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("...
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like American Express. The answer also includes follow-up discussion points that interviewers commonly explore.

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