DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/Spark/Big Data/Provide specific examples of challenges faced with PySpark and SQL and solutions implemented.

Provide specific examples of challenges faced with PySpark and SQL and solutions implemented.

Spark/Big Datamedium0.5 min readPremium

**Situation**: Production join job on 10B-row fact table with 100M-row dimension timed out; one task ran 2h vs 5min for others. **Task**: Identify root cause and fix without schema changes. **Action**: (1) Profiled via Spark UI—identified skew on region key (80% data in 3...

🤖 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
S&P Global
Key Concepts Tested
joinpartitionsparksql

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like S&P Global. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, 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.

Expert Answer
101 words

Situation: Production join job on 10B-row fact table with 100M-row dimension timed out; one task ran 2h vs 5min for others. Task: Identify root cause and fix without schema changes. Action: (1) Profiled via Spark UI—identified skew on region key (80% data in 3 partitions); (2) Implemented salting: replicated dimension with salt range, joined on (key, salt), aggregated; (3) Enabled AQE skew join as fallback. Result: P99 dropped 70%; job met SLA; pattern documented for team. Detail: OOM—increased partitions, broadcast small; slow query—Z-order on filter columns, partition pruning.

Scalability trade-offs: Salting adds shuffle overhead; AQE adaptive. Cost implications: Right-size after fix.

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 →