The formats are kind of a halfway solution, because trying to build something with MPP semantics on objects stores is difficult. The difference between MPP and

The formats are kind of a halfway solution, because trying to build something with MPP semantics on objects stores is difficult. The difference between MPP and something like Databricks or Trino working with object store is that while MPP can likely get much better performance and especially latency from the same hardware, operating it is much harder. You don't "backup" Databricks - the data is stored in object storage and that is it. You don't have to plan storage sizing quarters upfront, and you never get in trouble because there is unexpected data spike. Compute resizes are trivial, there is no rebalancing. Upgrades are easy, because you're just upgrading the compute and you can't break data that way. You can give each user group (like batch one and interactive one, or each team) their dedicated compute over common data and it works. That compute can spin up and down and autoscale to save some money. You don't have to think about how to replicate my table across a cluster or anything. And so on, and so forth. Running a big data and analytics platform - place where tens of teams, tens of applications and hundreds or thousands of analysts come for data and where they build their solutions - is already enough of a challenge without all this operations work, and that is why Snowflake and Databricks are worth that crazy money. If someone could solve the challenge of having MPP that is as easy to manage as Snowflake or a Lakehouse, that would be quite the differentiator. And maybe you people already did and I just didn't notice, I don't know :)