Zovda
Data work is usually prototyped on a sample and then pointed at the full set. The prototype was written for clarity, and clarity is expensive at scale.
The difference is not a constant factor. It is a different shape of job.
Where the time goes
- Per-row calls to a model or a service, which dominate everything else
- Unindexed joins that were fine at ten thousand rows
- Re-computing a fingerprint that never changes
- Writing one row at a time to a database
Each is invisible on a sample and decisive on the full set.
Batch and cache
Group the rows that need the same expensive operation, and cache the results by content. A fingerprint computed once is a fingerprint you never compute again.
For model calls, batch by input length so the slow ones do not hold the queue.
Make it restartable
A job that fails at eighty percent and restarts from zero will fail the same way. Write checkpoints, and make each stage idempotent so a rerun does not duplicate rows.
Measure per stage
Time and count at each stage, not just at the end. The stage that is slow is usually not the one people guess.
Zovda runs these collections through Datuva, where each stage reports its own counts. The data research collection shows how a run is described.
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