DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/Spark/Big Data/Write PySpark code to extract data from a CSV and create a table.

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

Spark/Big Datamedium0.3 min readPremium

**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.

This answer is partially locked

Unlock the full expert answer with code examples and trade-offs

Recommended

Start AI Mock Interview

Practice real interviews with AI feedback, track progress, and get interview-ready faster.

  • Unlimited AI mock interviews
  • Instant feedback & scoring
  • Full answers to 1,800+ questions
  • Resume analyzer & SQL playground
Create Free Account

Pro starts at $24/mo - cancel anytime

Just need answers for quick revision?

Download curated PDF interview packs

Interview Packs
1,800+ real interview questions sourced from 5 top companies
AmazonGoogleDatabricksSnowflakeMeta
This answer is in the DE Mastery Vault 2026
1,863 questions with expert answers across 7 categories →

Free: Top 20 SQL Interview Questions (PDF)

Get the most asked SQL questions with expert answers. Instant download.

No spam. Unsubscribe anytime.

Related Spark/Big Data Questions

mediumWhat is the difference between repartition and coalesce in Apache Spark?FreehardWhat is the difference between SparkSession and SparkContext in Spark?FreemediumWhat is the difference between cache() and persist() in Spark? When would you use each?FreemediumWhat is the difference between groupByKey and reduceByKey in Spark?FreemediumWhat is the difference between narrow and wide transformations in Apache Spark? Explain with examples.Free

Want to know if YOUR answer is good enough?

Paste your answer and get instant AI feedback with a FAANG-level improved version.

Analyze My Answer — Free

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.

← Back to all questionsMore Spark/Big Data questions →