Architectural optimizations: (1) Partitioning—query only needed partitions. (2) Columnar (Parquet/ORC)—read only columns. (3) File size 128–512MB—avoid small files; compact. (4) Parallelism—partitions, parallelism in Spark/Glue. (5) S3 Select/Blob query—push predicates. (6)...
This hard-level Cloud/Tools question appears frequently in data engineering interviews at companies like Fragma Data Systems. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition, spark) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Architectural optimizations: (1) Partitioning—query only needed partitions. (2) Columnar (Parquet/ORC)—read only columns. (3) File size 128–512MB—avoid small files; compact. (4) Parallelism—partitions, parallelism in Spark/Glue. (5) S3 Select/Blob query—push predicates. (6) Caching—Spark cache, Athena result cache. Trade-off: Over-partitioning = many small files. Cost: Fewer bytes scanned = lower Athena/Glue cost. Best practice: Profile query patterns; partition accordingly.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools 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.