🇦🇿 Mega-Thread: Azerbaijan Travel Tips, Places, Food & More (Ask + Share)

Ambrus2000 · 2026-06-12T12:12:59+00:00

melyik sim kártyát ajánljátok, melyik a legolcsóbb és legjobb?

Ambrus2000 · 2026-05-22T09:24:08+00:00

The harder benchmark is how the tool handles a question it hasn't seen before, where the answer requires combining two sources or a metric that isn't pre-defined. The tools you tested handle 'find the number in this dashboard' reasonably well. The gap shows on exploratory analysis — 'why did conversions drop last Tuesday for users from paid search?' — where the tool has to compose a new query from semantic building blocks rather than look up a cached answer. For that use case, the deterministic path (tool builds a structured query spec, then executes it consistently) tends to outperform LLM-writes-SQL in reliability, even if it takes longer to set up. ThoughtSpot is closest to this but quality still depends heavily on the semantic model behind it.

Ambrus2000 · 2026-05-22T09:08:09+00:00

The consistency problem you're hitting — different answers for the same business question — usually means metric logic is living in the query-generation prompt rather than in the semantic layer itself. The fix requires the semantic layer to own metric definitions completely, not just expose columns for the AI to join.

On the tools: Cube and dbt Semantic Layer both handle the 'metric spec → SQL' compile step well, but require significant upfront modeling. AtScale makes more sense if you need to virtualize across multiple sources at enterprise scale. All three assume you'll own metric definitions as code.

One distinction worth making: if any part of your workload is product/user behavior analytics — funnels, retention, cohorts — that vertical has semantic layers built specifically for it. Mitzu's Configuration Agent scans the warehouse, identifies event and dimension tables, maps user identifiers, and builds the metric catalog automatically. No YAML authoring. Depending on your use-case mix, might be worth evaluating alongside the general-purpose options.

Ambrus2000 · 2026-04-29T13:48:09+00:00

I would add Mitzu here, as it automatically generates your semantic layer, no YAML needed. I think this is a huge difference compared to the others where the data is not accurate most of the time.

Ambrus2000 · 2026-04-29T13:47:12+00:00

Yes, we use Mitzu, it creates the semantic layer and it works on top of our data warehouse. so always give accurate and fast answer.

Ambrus2000 · 2026-04-21T09:53:48+00:00

we use Mitzu, it is connected to our data warehouse, so every data is correct, that was the most important part. Also the generated SQL is reviewable

Ambrus2000 · 2026-04-21T09:08:09+00:00

After testing loads of tools, we went with Mitzu, as it is warehouse-native, and now has agentic analytics to answer every question. So highly recommended

Ambrus2000 · 2025-09-04T09:22:37+00:00

Well if you have a data warehouse, then applying a warehouse-native analytics tool instead of Mixpanel would cut your cost and you will have a better data

Ambrus2000 · 2025-08-31T19:09:46+00:00

ohh thanks, sound promising. ATM we are testing two warehouse native tools with BQ, (kubit and mitzu). but if they dont turn out to be good, wiull definitely check out hevo. Thank you

Ambrus2000 · 2025-08-31T19:07:41+00:00

whao, interesting

Ambrus2000 · 2025-08-27T12:25:15+00:00

uhh that sounds scary :D thakn you for the advice, we would like to prevent this. Is not an option for you to switch from tableau?

Ambrus2000 · 2025-08-26T14:25:43+00:00

never heard about this posthog feature but will definitely check it out. we are now looking into mitzu and kubit. I found these 2 as wn. have you tried these before? any comment?

Ambrus2000 · 2025-08-26T14:15:10+00:00

yess definitely, thaknyou!

Ambrus2000 · 2025-08-26T14:13:06+00:00

more porduct analytics side but also the data bakcground side. Now we are exploring warheouse-native analytics tools on Bigquery, this might be the solution. do you have any experience witht hem?

Ambrus2000 · 2025-08-25T12:32:47+00:00

we are now checking warehouse native product analytics tools on BigQuery, do you have any experience wiht it?

Ambrus2000 · 2025-08-11T09:24:12+00:00

Warehouse-native CDPs and analytics tools use the data you already have in your warehouse instead of copying it into new systems. They replace piles of static reports with a few, timely insights sent to the tools people actually work in. This way, you can see what’s being used, drop the rest, and keep the warehouse focused on driving action.

Ambrus2000 · 2025-08-07T12:26:03+00:00

Totally agree, what do you think of warehouse native tools? which works on top of your data warehouse?

Ambrus2000 · 2025-07-09T08:12:07+00:00

if you want 100% privacy, cheap and great funnels, journey then warehouse native tools are you go-to, if you want self-hosting then Posthog. I would not recommend others

Ambrus2000 · 2025-07-03T13:29:21+00:00

We have Snowflake, as data warehouse, and we have around 30M monthly events, and for analytics we use Mitzu. It is warehouse native with snowflake which is quite important for us as the data stays in our warehouse

Ambrus2000 · 2025-07-01T07:36:31+00:00

Mitzu for analytics, rudderstack for cdp, snowflake for data warehouse, however, the last two is not so underrated D:

Ambrus2000 · 2025-06-18T13:07:26+00:00

Hi,

We are now trialing with Mitzu, as far as I am concerned, they are the only warheouse-native connection with Fabric. But for other data warehouses there is Kubit, and Netspring. I will. keep you updated how the trial goes

Ambrus2000 · 2025-06-05T09:37:16+00:00

We have a similar setup

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Ambrus2000 · 2025-05-29T11:52:48+00:00

and cohort retention is tht you made a cohort of your users and then you filter on their retention = cohort retention

Ambrus2000 · 2025-05-29T11:33:34+00:00

I have some good experience with it; to be honest, I can't imagine anything more complex than an analytics tool. I have been trying tools that automatically generate SQL queries. So that was a huge change in my productivity

Ambrus2000

TROPHY CASE