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I can’t blame them really. Regular pandas-in-notebook can deal with quite a bit of data and is reasonably convenient. Even if the org and datasets are large, it
by pletnes 3y ago
I can’t blame them really. Regular pandas-in-notebook can deal with quite a bit of data and is reasonably convenient. Even if the org and datasets are large, it’s rare to have huge single tables. Then you can go full spark when you have to.
- disgruntledphd2 3y agoSure, but why bother paying databricks for that? By default you'll be running 3+ machines while doing all your work in memory on the master node. It's just so very, very silly.
- pletnes 3y agoTrue. I’ve been wondering why they don’t facilitate non-spark use better. But then you can’t reasonably invoice 3 VMs extra, obviously.