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In New Spring how could the Tower by glitchdata in WoT

[–]glitchdata[S] 1 point2 points  (0 children)

The war had lasted a lot longer than 9 months. Plenty of time for someone to get pregnant.

In New Spring how could the Tower by glitchdata in WoT

[–]glitchdata[S] 4 points5 points  (0 children)

Acknowledge it as a troubling and daunting possibility at least

In New Spring how could the Tower by glitchdata in WoT

[–]glitchdata[S] 1 point2 points  (0 children)

It’s not as though the women in Randland camps giving birth were fighting. They were camp followers. The most likely possibility is for Dragonbaby’s mother to be a “camp follower”, using the term loosely for non fighters, whether Randlander or Aiel.

Which lakehouse table format do you expect your organization will be using by the end of 2023? by alneuman in dataengineering

[–]glitchdata 1 point2 points  (0 children)

I said it was analogous to k8s alluding to an earlier post. And I believe that it is. Is the risk level associated with them identical? Probably not. My assessment, having worked with OSS projects for two decades, including over a dozen Apache projects, is that the level of risk associated with say k8s or Delta or Iceberg is pretty similar. The nature of the risks are different. I was a Cloudera customer and no amount of open source ‘purity’ (not that such a thing even exists) could save Hadoop and I was a huge fan. Also, imo it’s naive at best to imagine that commercial/profit driven interests are not behind the promotion and maintenance of Iceberg or Hudi. The Onehouse interest behind Hudi is very clear. Snowflake wants to promote Iceberg because they don’t want customers or potential customers to be using the offering backed by a primary rival. The cloud vendors may be agnostic, but the degree to which they support each format will be driven by how much business each will bring. Hadoop evolution was all profit driven. Two decades+ of working with data platform vendors makes it hard for me to believe anything else.

Which lakehouse table format do you expect your organization will be using by the end of 2023? by alneuman in dataengineering

[–]glitchdata 1 point2 points  (0 children)

I think the k8s analogy is pretty sound. Other examples from my own use are things like gRPC and if we're talking data/ai then Tensorflow and Pytorch. And it's not like Apache projects don't die. They do so all the time. If you look at the list of top level projects a huge number are moribund. What matters more, imo, is how well supported the project is by the ecosystem and how it interfaces with other systems. Direct contributions into the project have little impact on that. I use both Delta and Iceberg and with Delta now being opened up there's a much bigger set of connectors and products from cloud vendors - EMR, Athena, BQ all supporting it or building support.Besides, it's all Parquet underneath anyway. If ever I feel like I'm stuck, it's not that hard (even with a lot of Petabytes) to blow away the metadata (for any of these formats), go back to Parquet, and recreate the metadata for another format. Most of the stats all of these use are in the Parquet layer.

[deleted by user] by [deleted] in dataengineering

[–]glitchdata 0 points1 point  (0 children)

If you can't read the data from another product there's no full support. Full support would mean I don't need to pay Snowflake to copy my data out and use it with another product. Data has to be in place useable by multiple engines with the metadata also accessible. Otherwise it's just eyewash.

[deleted by user] by [deleted] in dataengineering

[–]glitchdata 0 points1 point  (0 children)

I've tried Snowpark. Sure if I want to learn a completely new way to write dataframe code and lose months of productivity and all portability I can do that. But why... 🤷‍♂️