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Spark jobs that shuffle 40 TB just to count something
Databases
Big data
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Updated at 2026-10-10 06:14 UTC
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Related Topics
The cost of a bad partition key in a petabyte table
Sharding a table before it has a million rows is premature pain
Columnar formats cut our storage bill in half
Data skew makes one reducer do all the work
Denormalizing for read speed and paying for it on every write
Comparing databases by throughput alone hides how complex the queries were
How do you keep a streaming pipeline exactly-once without doubling the cost?
Real-time analytics sounds great until the batch job and the stream disagree
Soft deletes keep the row and break every unique constraint
Can synthetic data replace real records for training a model?
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