DataEngPrep.tech

JavaScript is required to use this application. Please enable JavaScript in your browser settings or disable any extensions that may be blocking scripts.

What are Spark optimizations, and can you explain them?

Spark/Big Datahard0.6 min readPremium

**Partitioning**: repartition by key before shuffle; coalesce before write. Reduces skew and small files. **Broadcast Join**: Small table < 100MB to all executors; no shuffle. Critical for star schema. **Predicate Pushdown**: Filters pushed to Parquet/ORC; skip row groups....

🤖 Analyze Your Answer

Frequency

Low

Asked at 1 company

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Cognizant. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, optimization, partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer

110 words

Partitioning: repartition by key before shuffle; coalesce before write. Reduces skew and small files.

Broadcast Join: Small table < 100MB to all executors; no shuffle. Critical for star schema.

Predicate Pushdown: Filters pushed to Parquet/ORC; skip row groups. Partition pruning on directory structure.

Caching: MEMORY_AND_DISK for reused DFs; unpersist when done.

AQE: Runtime coalesce of shuffle partitions; skew join split; broadcast join switch. Enable spark.sql.adaptive.enabled=true.

Salting: Add random suffix to skewed keys; distribute load; merge after.

Tungsten/Catalyst: Codegen, off-heap, logical optimization.

Scalability Trade-offs: Over-caching evicts other data. Salting adds extra shuffle. AQE adds slight overhead; benefit usually outweighs.

Cost Implications: 2–5x speedup typical with tuning. Profile first—optimize bottleneck, not everything.

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?Free hardWhat is the difference between SparkSession and SparkContext in Spark?Free mediumWhat is the difference between cache() and persist() in Spark? When would you use each?Free mediumWhat is the difference between groupByKey and reduceByKey in Spark?Free mediumWhat 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 questions More Spark/Big Data questions →