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Comment by aynyc | Hacker News Reader

Their moat was to be able to spin up spark clusters for you. This was very useful when companies just starting in that space and don’t have the skills. But with K8s and clusters becoming more common for normal workload, it’s not hard to manage your own spark clusters anymore. There goes their moat. Their other offerings just aren’t that amazing. People now don’t just want to buy hand tools anymore, they want power tools. AI/ML and its cousins are in the power tool space.

Databrick cost is stupid high, like snowflake. Any companies looking to chop budget would put those contracts up first.

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Their moat is their phenomenal sales team.

Like, I'm at company 3 that pays for databricks.

Amount of those companies that use Spark = 0.

I've given up complaining now.

So what are they using, the notebooks?

But in the notebooks you run pyspark / scala spark / spark sql code?

Yes, you can, if you want.

Yes, but for most of these companies they don't actually run (much or any) spark stuff, they just use the notebooks.

It's really depressing, tbh but I've made my peace with it now.

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.

Sure, 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.

True. I’ve been wondering why they don’t facilitate non-spark use better. But then you can’t reasonably invoice 3 VMs extra, obviously.

So Databricks is being replaced by open source, self-managed spark-on-k8s? Broadly speaking? Any concrete recommendations on how to do that in a project?

We tried this at a previous company I was at, it was incredibly tough to maintain and tune. We ended up using databricks which made it significantly more productive.

No, it's not about the technology. It's about your stack and skill set. If you are still on the school of one box for all. Then just pay for Databrick or EMR. If you are already running K8s because you invested into that world, then adding Spark isn't a huge jump.

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