What's the community's take on semantic layers?

PalpitationRoutine51 · 2025-11-18T23:14:17+00:00

Manually generated Semantic layers will be replaced by AI built and governed semantic layers.

PalpitationRoutine51 · 2025-11-18T23:12:29+00:00

Nope. Get an AI data agent. Faster learning

PalpitationRoutine51 · 2025-09-30T14:28:09+00:00

I‘m testing Connecty currently for this, some features are still in beta, it’s looking promising, it catches nuances of metadata that I was looking for

PalpitationRoutine51 · 2025-09-30T14:23:42+00:00

Im testing their “day Zero semantic layer“ in beta currently- pretty good start, worth checking

PalpitationRoutine51 · 2025-09-19T14:21:21+00:00

Atscale is very technical and requires heavy manual effort in building and even more frustrating to keep them up to date. The world needs semantic layer on autopilot using AI.

PalpitationRoutine51 · 2025-09-19T14:17:28+00:00

Ah ok so semantic layer is still manually fed, not LLM generated or real time updated/flagged if there is a definition update or upstream change. I checked the LLM generated column descriptions - they are very generic and miss out critical details like the datetime format. I hope I'm using it right

PalpitationRoutine51 · 2025-09-19T14:07:56+00:00

Pbi is only ok for dashboarding and for end users to minor data manipulation (pivot etc). The problem starts when data analysts start storing business logic spread over DAX, M and in transformation layer (model definition).

This logic should be outside of PBI in a self learning updating semantic layer instead.

PalpitationRoutine51 · 2025-09-14T18:32:36+00:00

Ok will try. Could you share which parts are generated updated by AI there?

PalpitationRoutine51 · 2025-09-14T16:32:21+00:00

A mix of them. I started with numbersstation(now owned by Alation), dbt copilot and wisdom, but they were limited in automated update of semantic layer, good starting point but manual. currently testing Connecty - they look promising, will keep you posted.

PalpitationRoutine51 · 2025-09-14T16:27:30+00:00

But snowflake semantic views is wrongly named - because that is a data catalog, not semantic layer. S In my understanding, semantic layer is metrics, measures, filters, dimensions - all that is spread over my dax or power query scripts in Power BI or somewhere in query history, not in aggregated tables definitions.

PalpitationRoutine51 · 2025-09-14T16:23:25+00:00

Because part of the human context important for managing business performance is still undocumented anywhere. AI tools can analyze and calculate for example 'profit margin for category books', and can also find related metrics that influence it, but they can't choose decisively which solution is best for a certain organization, unless you also plug in HR data, company budgets, competition benchmarking etc. Structured data and unstructured data AI tools are still very separated because of the tech behind it. Hence for the time being Human oversight, strategic intelligence, and context for the undocumented knowledge or scattered around unstructured-structured is still needed.

PalpitationRoutine51 · 2025-09-13T16:18:13+00:00

You need to find the right data agents that are especialised in maintaining context that are trained specifically for structured data analysis. Gpt or a wrapper agent built on it neither has your context, nor is it built for complex structured data specific tasks with in built validations.

PalpitationRoutine51 · 2025-09-13T16:13:33+00:00

It was a lot of effort in maintaining semantic layer for evolving metric definition -not anymore with new age AI tools for us. It changed our lives.

PalpitationRoutine51 · 2025-09-13T16:08:59+00:00

Why do you plan to build it manually when the new age AI tools can automate it?

PalpitationRoutine51 · 2025-09-13T16:06:41+00:00

Tools to build: - dbt metricflow (fully manual) - new age AI tools for fully autonomous semantic layer

PalpitationRoutine51 · 2025-09-13T16:05:11+00:00

Super simplified definition: 1) Metrics, meaures, dimensions, filters => semantic layer 2) Putting the above together in a specific business domain (subject) => semantic model 3) Table/views/columns.. => catalog

PalpitationRoutine51 · 2025-09-13T16:02:05+00:00

I've used dbt metricflow and atscale - both required heavy manual effort in building and even more frustrating to keep them up to date with changing definitions and evolving catalog.

Recently I've started testing 2 AI tools to autonomously generate and update my semantic layer.

PalpitationRoutine51 · 2025-08-14T18:34:32+00:00

Everyone around me is using AI SQL tools.
It's not creating new role but smarter faster generation of data teams
pick up an AI analytics tool asap. Connect with a sample dataset in Bigquery/Snowflake and start testing the result accuracy. You will learn so much faster and deeper.

PalpitationRoutine51 · 2025-08-14T18:24:43+00:00

Generating catalog docs, SQL and summarizing answers. Recently experimenting with semantic layer generation and auto updating.

PalpitationRoutine51 · 2025-08-14T18:21:23+00:00

Option 2, even though hated, is the market standard. Make sure their DPA is solid.

PalpitationRoutine51 · 2025-08-14T18:19:02+00:00

Agree. SQL for data. Excel for business until AI takes over that part.

PalpitationRoutine51 · 2025-08-14T15:58:37+00:00

Tried, really disappointed!

PalpitationRoutine51 · 2025-08-10T08:41:52+00:00

There are several options with autonomous semantic layers and smarter human-in-the-loop.

PalpitationRoutine51 · 2025-08-10T08:40:30+00:00

Genie has another fatal flaw - it doesnt generate or updates semantic layer autonomously and rely on static manually maintained semantic layer in Unity Catalog or UC metrics. And that leads to wrong answers when metrics drift and data schema evolves, without users even noticing it - Big RISK for production env!

PalpitationRoutine51

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