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Why I chose specific technologies (e.g., Spark over traditional ETL tools)

Spark/Big Datahard0.4 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-25

**Situation**: Legacy ETL (Informatica, SSIS) couldn't scale to TB; per-row licensing expensive. **Task**: Build scalable, cost-effective data platform. **Action**: Chose Spark for (1) **Scale**—handles TB. (2) **Cost**—open source. (3) **Flexibility**—code-based, custom...

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Asked at 1 company
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Key Concepts Tested
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Expert Answer
76 words

Situation: Legacy ETL (Informatica, SSIS) couldn't scale to TB; per-row licensing expensive.

Task: Build scalable, cost-effective data platform.

Action: Chose Spark for (1) Scale—handles TB. (2) Cost—open source. (3) Flexibility—code-based, custom logic. (4) Unified—batch + streaming. (5) Ecosystem—Delta, MLlib, connectors.

Result: 10x data volume, 60% cost reduction. ETL tools for simple, operational pipelines.

Trade-offs: Spark = more engineering. ETL tools = GUI, less flexible.

Cost Implications: Spark 60% cheaper at scale. Right tool for use case.

⚡
Pro Tip

Pro-Move: 'Spark for data platform; Airflow + dbt for orchestration; best of both.' Red Flag: Spark for 10GB simple pipeline—overkill.

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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 an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.

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