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....
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 Fragma Data Systems. The answer also includes follow-up discussion points that interviewers commonly explore.
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