DataFrames: scaling up and out


DataFrames of all sorts have become very popular for tabular data analytics for their nifty APIs and ease of use. But as they usually operate as in-memory engines, they can be hard to scale. In my talk, I'd like to outline several ways one can scale their compute platform to handle larger datasets without incurring much cost.

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Editors Note:

I am looking for editors/curators to help with branches of the tree. Please send me an email  if you are interested.