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What is one disadvantage of using Scala for data engineering tasks?

Spark/Big Dataeasy0.4 min read

**Disadvantages**: (1) Smaller talent pool than Python. (2) Steeper learning curve. (3) Longer dev cycles for ad-hoc analysis. (4) PySpark dominates data science; ML integration easier in Python. (5) JVM startup for small scripts. **When Scala Wins**: Performance-critical UDFs,...

πŸ€– 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
Coforge
Key Concepts Tested
pythonspark

Why This Question Matters

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

How to Approach This

Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
76 words

Disadvantages: (1) Smaller talent pool than Python. (2) Steeper learning curve. (3) Longer dev cycles for ad-hoc analysis. (4) PySpark dominates data science; ML integration easier in Python. (5) JVM startup for small scripts.

When Scala Wins: Performance-critical UDFs, core engine code. Scala UDF avoids Python serialization.

Trade-offs: Python for agility and collaboration; Scala for performance. Hybrid teams common.

Cost Implications: Scala expertise costs more; but Scala UDFs can cut job cost 2–5x vs. Python UDFs.

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Companies that ask this Spark/Big Data question

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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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