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A Spark job scanning 10TB of raw event data to answer a question about yesterday is not a Spark problem. It's a storage design problem. With the right partitioning in place, that same query touches 50GB and finishes in seconds instead of minutes. That's not an optimization at the margins; it's the difference between a pipeline your team trusts and one they dread.
Partitioning and bucketing are two separate ideas ...
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