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Write PySpark code to extract data from a CSV and create a table.

Spark/Big Datamedium0.3 min read

**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 ```...

🤖 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
HCL
Key Concepts Tested
partitionpythonsparksql

Why This Question Matters

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.

How to Approach This

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.

Expert Answer
66 wordsIncludes code

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.

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