mble_

Why not both?

There are times when pushing the work down to the database layer is appropriate - databases are quite good at a lot of these operations - but if you need more nuanced approaches (e.g. ANOVA, ARIMA, other kinds of forecasting or analysis), leverage the appropriate tools.

> I just think we must have completely different working experiences.

Likely! I've generally worked in smaller orgs (including as part of a much larger org, as with my current employer) and there is less access to dedicated resources.

> Even when no BI team is dedicated, there's usually someone that's wearing that hat.

100%. Unfortunately, this has commonly be me from my personal experience.

> In that way, identifying it and raising concern can be 'my job' but when investigating it, it could be a team effort.

Totally agreed.

For some additional context, I've spent my working career on data systems so I likely feel a much stronger affinity to this type of self-serve analysis than your average bear.

Agreed, if you have the budget for it. There are often times where living off the land is necessary.

Author here.

One of the main things here is that you should know your data well enough to articulate the right request from BI. In my experience, BI often end up as pure order takers - if you ask the wrong question, you get a lovingly formatted but wrong answer.

The other thing is that this assumes you have a BI team at hand - smaller teams/orgs often don't! Perhaps I should make this a little more explicit.

My central thesis, also not made explicit, is that leaders should be appropriately curious _and_ leverage the tools they have to be able to do things like "hey, this looks weird, what's up?" and share the data and their methodology - that way it can be corrected/investigated etc.

Oh, very cool. I'll have to add this to my list to check out next year.

Pinot is something I haven't had any personal experience with, so that's why it wasn't on the list - same with StarRocks, or Druid.

Something for me to look into next year, clearly.

I love Datalog, but its such a niche technology. If I had included it, I would have probably swapped out TigerBeetle for it.

Apache Cassandra would probably be the most notable one (outside of Kafka etc).

It was not intentional. I've corrected this oversight, and attribution is now provided - my apologies.

I lived through the MongoDB hype cycle.

For document databases, I'm more interested in things like PoloDB and SurrealDB.

Yeah, this is the bit for me. We have almost no good OSS layers for folks to "plug and play".

Its a bit of a vicious circle - because there is low exposure, no one is building those layers. Because no one is building the layers, there is no exposure.

Definitely doesn't replace databases, at least LLMs as they currently are. We're going to be stuck with relational algebra for a long time to come, but the interfaces to interact with store might change over time from SQL to more natural language.

> what characteristics make for a best in class ai database

As I said before, I think the space is moving too fast on what is "best in class" - see all the vector indexing work that has happened in the last ~6 months or so. Big, centralised vector stores for some applications will have very different profiles to vector stores on the edge/on device, for example.

As mentioned, I'm a big fan of boring technology, so I'm going to happily wait we have a boring "winner".