# Optimizing Distributed Data Processing for ML at Scale

Page: https://stenobird.com/podcast/data-science-tech-brief-by-hackernoon-6367564/optimizing-distributed-data-processing-for-ml-at-scale
Text version: https://stenobird.com/podcast/data-science-tech-brief-by-hackernoon-6367564/optimizing-distributed-data-processing-for-ml-at-scale.md
Podcast: [Data Science Tech Brief By HackerNoon](https://stenobird.com/podcast/data-science-tech-brief-by-hackernoon-6367564)
Published: 2026-05-21T16:00:43+00:00
Episode link: https://share.transistor.fm/s/993507ed
Audio file: https://media.transistor.fm/993507ed/2a432a6d.mp3
Processing state: not_requested
JSON: https://stenobird.com/v1/public/podcasts/data-science-tech-brief-by-hackernoon-6367564/episodes/optimizing-distributed-data-processing-for-ml-at-scale
Duration seconds: 423

## Resource

This story was originally published on HackerNoon at: https://hackernoon.com/optimizing-distributed-data-processing-for-ml-at-scale . A practitioner's guide to ML data pipeline performance: read the query plan first, eliminate shuffle, fix file layout, handle skew, prune columns Check more stories related to data-science at: https://hackernoon.com/c/data-science . You can also check exclusive content about #spark , #pyspark , #machine-learning , #data-engineering , #performance-optimization , #distributed-systems , #distributed-data-processing , #optimizing-distributed-data , and more. This story was written by: @seshendranath . Learn more about this writer by checking @seshendranath's about page, and for more stories, please visit hackernoon.com . Stop tuning knobs on a broken foundation shuffle, file layout, skew, and column pruning do more for ML pipeline performance than any clever algorithm.

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## Transcript

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