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

Spark/Big Datahard0.4 min readPremium
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
Tiger Analytics
Key Concepts Tested
etlspark
Expert AnswerPremium
76 wordsInterview-ready
**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....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Tiger Analytics. The answer also includes follow-up discussion points that interviewers commonly explore.

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