The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Julien Le Dem no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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Answered raw tape D 4 · C 4 · P 4 · Cm 3 3.85

Q Some, some love. Okay. Uh, thank you very much. Um, so, uh, let's see. Question from Rohan. Julien, what are your thoughts on where the Hive Metastore, uh, fits in the metadata and lineage story? Are there community efforts to improve the Hive Metastore catalog service for metadata, lineage, observability? Why or why not?

A Uh, yes. So, I mean, the Hive Metastore, for a long time, it was a, it's a de facto standard for data catalog or for worse on top of Hadoop and, um, in this environment. And So whatever people are using, and Hive is one of, um, the tool they're using, should be inter, inter, interconnected with open lineage, right? And this is, and I know that, for example, there's some hooks that exist, um, between Hive and, um, Atlas, and that give some visibility lineage, and those are usually very, Um, oriented towards Atlas. The goal of OpenLineage is really to focus on modeling the jobs that are there running. And therefore it's kind of, it's a bit easier to model in that way because we model what's running and it's really making observation if there's a Hive query running and it's reading from this table and running to that table. And selecting this partition and writing that partition. You can model that and start making it available, and it should be, it should be part of the picture. It's not on my diagram just because You know, I can't fit all the, you know, I can't steal the landscape from Matt and put it on the slide and say, like, everything, basically everything should be involved in this. I think right now, today, Spark is one of the big projects that people are using. SQL is another big one, and so it would make sense, yes, to have Hive as well.

AI assessment note: “it should be part of the picture. It's not on my diagram just because”

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