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parquet vs csv
(lemmy.ml)
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this post was submitted on 03 Sep 2024
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In the deep learning community, I know of someone using parquet for the dataset and annotations. It allows you to select which data you want to retrieve from the dataset and stream only those, and nothing else. It is a rather effective method for that if you have many different annotations for different use cases and want to be able to select only the ones you need for your application.
How does this differ from graphQL?
Parquet is a storage format; graphQL is a query language/transmission strategy.