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Describe building custom JARs for Spark jobs

Spark/Big Dataeasy0.3 min readPremium

**Why custom JARs**: Encapsulate dependencies; ensure version consistency. **Process**: Use sbt or Maven; package Spark + app code. Use `--packages` for Maven deps to avoid fat JAR. Submit via `spark-submit`. **Scalability trade-offs**: Shade conflicting deps; large JAR = slower...

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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
LTIMindtree
Key Concepts Tested
spark

Why This Question Matters

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

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Expert Answer
60 words

Why custom JARs: Encapsulate dependencies; ensure version consistency. Process: Use sbt or Maven; package Spark + app code. Use --packages for Maven deps to avoid fat JAR. Submit via spark-submit. Scalability trade-offs: Shade conflicting deps; large JAR = slower distribute. Cost implications: Build time; deployment size. Best practice: Shade conflicts; use --packages for common libs; version JARs; test in staging.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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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.

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