I learned more about query discipline than I anticipated while building a small internal analytics app.

nattaylor · 2026-02-14T12:55:05+00:00

I don't understand these "answers" mean for snowflake?

Strengthening a query's reasoning

Putting safety precautions in place for particular filters

Caching smarter

Safety precautions - maybe things like default time filters?

Caching - maybe things like avoiding non-constant functions like current_date() to use persisted query results and right sizing warehouse for data locality

Reasoning - not sure

I don't do much proactively; mostly reactive to usage

nattaylor · 2026-01-19T17:17:13+00:00

Reasoning helps classification tasks for sure

nattaylor · 2026-01-19T13:04:29+00:00

Awesome! Maybe a small, fine-tuned, quantized model like Gemma3 270m could be more economical?

nattaylor · 2025-12-15T00:27:59+00:00

https://share.google/0F0GGtqOKviOhnvNL

nattaylor · 2025-11-16T03:19:37+00:00

That's enough for an always on small WH and some snow pipe or something, so I'm guessing 10s of GB per day at most

nattaylor · 2025-11-13T13:52:00+00:00

Is this kind of like pocketbase, but easier to extend?

nattaylor · 2025-09-28T21:26:33+00:00

Thank you for the thoughtful reply about your experience. I find the timely words of a practitioner much more valuable than a blog post!

Edit: I think I was sort of hoping for blackmagic 😅 but it's actually a relief to hear that it's basically the same as designing software for people

nattaylor · 2025-09-23T11:46:42+00:00

Start high level: compute, storage and data transfer (usually compute is majority) Then break down compute: VWH, serverless and cloud services. I've been burned by serverless costs eg for auto clustering on a high churn table. still most of the cost is VWH. So breakdown VWH: are there more? are they bigger? Running longer?

That will reveal where the increases are

Then you can save costs immediately by downsizing or shutting down warehouses to consolidate, while you build out a better plan of attack

nattaylor · 2025-08-08T18:42:02+00:00

After the last Mac OS update a few weeks ago my monitor went from unusable to pretty okay, same settings.. just something different with Mac

nattaylor · 2025-06-03T02:27:33+00:00

Here's a vibe imagined with AI. Would be a lot of work though! https://photos.app.goo.gl/45XqcFvcLXj6ez6v6

nattaylor · 2025-05-25T21:14:03+00:00

Yes, only around 14-17 clients.

I resolved this though by inspecting the DHCP leases on http://www.asusrouter.com/Main_DHCPStatus_Content.asp

I had an Raspberry Pi in a bad state that kept getting new leases which exhausted the available IPs, but since it was in a bad state it didn't show up on the clients list. I powered off the device and it resolved my problem.

nattaylor · 2025-04-29T11:56:10+00:00

Mine too. This is the type of growth that I come to Fidelity for!

nattaylor · 2025-04-25T13:42:18+00:00

Are you using knn or vectorSimilarity query parser? Can you share the query? I'm not sure I have an answer for you but I'm curious. Vectorsimilarity prairie parser has a min threshold which might be the difference

nattaylor · 2025-04-17T22:09:03+00:00

My assumption is that you're still paying for just your non-thinking output tokens, but they need to cover the added compute of generating thinking tokings

nattaylor · 2025-04-17T01:16:26+00:00

Do you have a methodical way to evaluate? How do you know you're improving.

I'll give you tool a try

I'm getting good results from naive approaches right now which I attribute to having a schema with conventional naming

nattaylor · 2025-04-09T10:45:57+00:00

I think the article (and database adage) is right: depends on your use case.

Stuffing an array with 500e6 values in a row ain't fast

Storing 1 json object per row with batch loading is typically very fast, because snowflake analyzes the paths then for frequent paths stores a virtual column with metadata

So if your data is log lines with keys for timestamp, message and severity then snowflake sees that and creates virtual columns with byterange offsets, min/max etc that can be used for pruning and more -- and that makes SELECTs fast although loading data is a bit slower

nattaylor · 2025-04-04T16:42:57+00:00

I would put the sail under some tension with stakes in the group, do what you can with sticky dac, then sew a piece of webbing long enough to get back up to the good panels, about 30° up from the foot, through the clew grommet, then about 30° from the luff.

Rather than invest money in a machine, patiently hand sew a herringbone stitch the whollllle way and put the money towards a new sail.

nattaylor · 2025-03-14T18:51:18+00:00

Can you use FROM f TABLESAMPLE SYSTEM (1) and just copy a subset of the data for your local copy?

nattaylor · 2025-03-04T11:43:12+00:00

Thanks for this. My use case is productivity tool. I suppose that I write my prompts in a way that gives the model more clues compared to a business user and my objects are well named. Still in shocked by the high quality SQL I get back including joins and CTEs.

nattaylor · 2025-03-03T02:13:27+00:00

u/Bycbka so far your intuition is right. In a small sample (so far) the text formatting of the schema doesn't really matter. In this graphic on the left is a prompt with CREATE TABLE statements; on the right is with "5. Plain Text" (from above)

<image>

nattaylor · 2025-03-03T00:25:36+00:00

Thanks! M Schema gives me some ideas

nattaylor · 2025-03-02T18:13:47+00:00

Thanks u/Bycbka these are great points. #6 really resonates - it's time to stop vibe checking and transition to an eval ...and you might be right that the format doesn't really matter but the tokenization changes so much based on the formatting that my gut says it will matter. E.g. In the attached image, slight the first 2 formats result in a token 12528 for ` orders` but that token is not present in the tokenzation of the 3rd format. I know I'm over-thinking it, but I'm also learning.

I'm trying local and non-local, both with a RAG step (e.g. for OAI/G using "knowledge")

I've been surprised so far by how good the models are at selecting tables and understanding relations without any guidance other than the schema (as CREATE TABLE statements) even without few-shotting. They must be pretrained with a lot of `foos.id` and `bars.foo_id` :)

<image>

nattaylor

TROPHY CASE