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Share strategies for query and ETL optimization.

SQLhard0.4 min readPremium
Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Fractal
Key Concepts Tested
etljoinoptimizationpartitionspark
Expert AnswerPremium
73 wordsInterview-ready
Query optimization: index, partition, avoid SELECT *, push filters, use JOINs not subqueries, materialized views. ETL: incremental load, parallelize, partition writes, coalesce small files, use columnar format, tune Spark (executors, memory, shuffle), validate early, reject bad data to quarantine. Monitor and iterate. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead. **Scalability trade-offs**: Profile before optimizing; validate on sample then full....
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 Fractal. The answer also includes follow-up discussion points that interviewers commonly explore.

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