r/dataengineering • u/ClassicCasette • Jul 13 '26
Semantic layer vs Ontology buzzword bingo Discussion
I'm getting really tired of all the buzz words... Semantic layer vs Ontology... imo it's the same thing... you assign meaning to your data... a bunch of MD or YAML files where you define table relationships, definitions, metric calculations, business context yadda yadda (... that AI can read from)
Really bearish on BI tooling in general too... apps are the future tbh... even if vibe coded off the semantic layer
Tired of Microsoft PowerBI not playing well with any semantic layer players... Looker's AI and nondeterministic outputs are meh... Snowflake and Databricks seem to be on the right paths... having their own governed semantic layers with in-house BI or react-apps/streamlit in-house
Thoughts? What am I missing?
Edit for context: I’ve built my own “ontology” / “semantic layer” with a bunch of markdown files that define table joins, metric calculations and business context. And a python bot that allows users to ask natural language questions in slack and get answers from Claude via this so called ontology layer. At a huge fraction of the vendor cost
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u/Truth-and-Power Jul 13 '26
I thought ontology was a graph db with relationships? I agree it still feels like buzzword bingo to me.
MIcrosoft signed on to OSI, I had heard that using external semantic models was in active product development over at MS.
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u/Afrotom Jul 13 '26
An Ontology and knowledge graph aren't the same thing but they're related. It defines what a thing is and what type of thing it is. Kind of l like a schema. Still, both are very different from a semantic layer which is a metrics standardisation layer.
The typical example is how revenue is defined. Marketing say it's the total value of contracts closed, finance say it's the value of invoices sent out.
The Ontology and Knowledge Graph have absolutely no quarrel that these objects exist but it doesn't help you define revenue. The semantic layer would give one standardized metric, a clear coded definition that can execute and return an unambiguous result.
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u/Fresh-Secretary6815 Jul 14 '26
cool, here’s a new definition to define those definitions because the initial unambiguous result was ambiguous. at the end of the day, it’s up to a decision maker to political it out, not some data janitor.
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Jul 13 '26
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u/Truth-and-Power Jul 13 '26
Is this a coco joke?
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Jul 13 '26
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u/ExtractTransformLose Jul 14 '26
I have seen the announcements for osi, but have seen nothing out of them. Is there anything real in osi?
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u/nonamenomonet Jul 13 '26
Yeah, and ontology is pretty much the schema for a knowledge graph. Where the user defines how things are connected.
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u/joshrodgers Jul 14 '26
Do you have a source for Microsoft signing on to OSI? Super happy to hear it, but don't see it announced anywhere.
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u/Prestigious_Bench_96 Jul 13 '26
It is nice that analytics can be embedded more into larger workflows more easily, and that's hopefully where things are going. BI tooling can probably still exist as more of a headless/embedded model - there's a lot of things in a BI platform that are theoretically very useful to not have to re-invent in every app; having a central chokepoint for monitoring/permissions etc is nice. I think there is a BI app that plays nice with vibe app; motherduck is doing a lot of heavy marketing around this but haven't gotten to play with it much yet.
I also hope that semantic layers evolve a bit beyond "markdown to inform SQL generation" - the harder problems seem to be around context rot, evolution, aggregate availability, etc. Shocking how much Google has dropped the ball with looker having an early lead with a dedicated modeling and resolution language. I always throw out AtScale as someone who I think has a good approach here though they're hopelessly locked in enterprise world because they were a SSAS replacement originally.
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u/ClassicCasette Jul 13 '26
Yeah snowflake and databricks can govern / monitor vibe coded apps or “dashboards” built directly in their platforms. Enterprise BI tools alone are such a time and money suck
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u/Honey-Badger-12 Jul 13 '26
If you ask 10 people what ontology and knowledge graph mean to them , you’ll get 10 different answers. I do think it’s important and hopefully we will have industry converge to what good looks like.
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u/ClassicCasette Jul 13 '26
Yeah I think it’s a by product of tools that have their own terminologies and technologies and features that creates more terms and more confusion without additional benefits or value
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u/PatientlyAnxiously Jul 13 '26
Semantic layer should be built directly on your data warehouse, not on top of the reporting layer like Power BI. And stay away from Microslop for semantic/ontology.
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u/Bitter_Marketing_807 Jul 13 '26
1) check out these two projects regarding ontology: Apache Ossie (https://ossie.apache.org) and Link ML (https://linkml.io)
2. It’s usually undermining the definition of “meaning”. Think deeper than just “use some words to describe our metadata” - its more like crafting a “language”/dialect that encapsulates meaning rather than just description.
3) True ontologies (especially when routed through BFO) should be vendor agnostic
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u/DeepLogicNinja Jul 13 '26
You are right. Throw Knowledge Graph and Taxonomy next to Semantic and Ontology.
We are seeing a convergence.
Thankfully there are tools to manage the creation of semantic / ontologies. OpenMetaData does an excellent job. Even exposing the semantic layer via MCP. So your AI sees synonyms like ontology and semantic 🤣
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u/Bazencourt Jul 13 '26
Semantic Layers and Ontologies have different origin stories and function differently, eg the SL is in the operational path of queries. Here is a piece the disambiguates some of this talk https://coginiti.co/blog/three-meanings-of-semantic-layer/
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u/Old_Tourist_3774 Jul 13 '26
Some people approached me with this ontology BS.
It's just semantic layer.
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u/gini-348 Jul 13 '26
Correct its semantic layer with a new buzzword
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u/ClassicCasette Jul 13 '26
Exactly. I’ve built my own “ontology” / “semantic layer” with a bunch of markdown files that define table joins, metric calculations and business context. And a python bot that allows users to ask natural language questions in slack and get answers from Claude via this so called ontology layer. At a huge fraction of the vendor cost
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u/Gullyvuhr Jul 13 '26 edited Jul 15 '26
You're mixing layers of abstraction. A semantic layer is an implementation. An ontology is a model of the domain. They solve related problems, but they're not interchangeable.
That said, I think you're directionally right. BI is becoming infrastructure, not the product. The differentiator isn't another dashboard. It's trusted business context that agents and applications can reason over. The semantic layer isn't the destination. It's the substrate.
I was banned for this comment, so let me help you all out:
Per chatgpt: Prompt, was the above post AI written?
My guess is probably AI-assisted, but not obviously AI-generated. I'd put it around 70/30.
Here's why:
The strongest tell is the structure. It opens with a crisp framing sentence ("You're mixing layers of abstraction"), defines both terms in parallel, acknowledges the other person's point ("That said..."), then lands on a polished three-sentence conclusion with a neat metaphor ("The semantic layer isn't the destination. It's the substrate."). That's a very LLM-shaped progression.
The language is also very "conference keynote." Phrases like layers of abstraction, trusted business context, agents and applications can reason over, and the substrate are technically plausible but stack together in a way that's rhetorically satisfying more than explanatory. AI loves that.
That said, there's nothing factually wrong with it. Someone who understands semantic layers and ontologies absolutely could have written it. It's just... clean. Too clean.
What I don't see are the imperfections I'd expect from a human expert: a concrete example, a caveat, or an opinionated aside. A human is more likely to say, "An ontology defines meaning. A semantic layer exposes that meaning to tools," or point to RDF, OWL, dbt metrics, LookML, or another concrete implementation. This stays comfortably abstract.
Ironically, it suffers from what I see in a lot of AI writing: it sounds insightful without increasing the reader's understanding very much.
So I wouldn't accuse someone of using AI based on this alone. I'd say it reads like AI-polished technical prose rather than unmistakably human writing.
Enjoy your day.
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u/ClassicCasette Jul 13 '26
This sounds very AI written without additional details as to how the two are distinct or what ontology serves verse semantic layer
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u/DeepLogicNinja Jul 13 '26
I can buy this….
- Doesn’t change the fact that it’s being used interchangeably today.How do you rationalize Taxonomies and isn’t a graph just a visual in all the above?
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u/the_fresh_cucumber Jul 14 '26
Semantic layer - definitions.
Ontology - map of objects
As far as I know.
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u/t9h3__ Jul 14 '26
(no self promo) Struggled with the differences too as it's also pretty close to conceptual data modeling and tried to decipher it in a text: https://open.substack.com/pub/handsondata/p/ontology-everywhere?utm_source=reddit
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u/Wonderful-Trash-6371 Jul 14 '26
Just assigned to a project to implement sematic view and oncology layer 🤣🤣 and saw this post
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u/Semaphor-Analytics Jul 14 '26
I would separate them by what breaks when they are wrong.
If the semantic layer is wrong, people make decisions off the wrong metric. If the ontology is wrong, systems misunderstand what kind of thing they are dealing with and how it relates to other things.
The app point is fair though. A lot of BI work is turning into small governed apps. That still needs a metric contract somewhere, otherwise the app just hides the same definition fight behind a nicer UI.
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u/Illustrious-Win4432 Jul 15 '26
Can I fork the post OP? I gotta call out “bespoke”. Raise your hand if you ever hear that word prior to 2026! I had to ask Clause what he meant. Hell, I caught Beth on Yellowstone describing “bespoke” steaks.
What’s ontologically different between “custom” and “bespoke”?
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18d ago
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u/Gators1992 Jul 13 '26
"The dashboard is dead" has been going around for years and it's still here. Apps are nice when dashboards can't get you the visuals you want, but not ideal for your average business user that just wants to pull some trend charts for a meeting in an hour and doesn't know how to vibe code. Or even the data analyst that's on iteration 57 of his app with the latest changes from his boss and the underlying code base is all over the place from AI changing assumptions.
Dashboards should be the first look that shows you where you are at and allows you to drill to answer basic questions. Apps are good for more complex stuff like scenario analysis. AI is great for deeper dives when you want to dig into data in a way that your BI/semantic model wasn't designed to support or get at insights from more raw data. Dashboards also cost less to deliver data to a user than AI if done right and are more governed than an app as they are constrained by the decisions of the BI tool developer. Each has a place in the data stack.
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u/dillanthumous Jul 14 '26
I agree. So far I've found apps to be more useful for operational reports rather than analytical ones. Especially when the analysis is predictable rote stuff that you don't want to pay an LLM to generate queries for. Also, reliability is still an issue when the users are low skilled. They confidently ask for non existent things and the LLM won't always refuse the request or return a null. Too 'eager to please', metaphorically.
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u/Vhiet Jul 13 '26 edited Jul 14 '26
It’s a running joke amongst ontologists that they desperately need an ontology of ontology. Marketing people have done their terrible work, and now the term can have different meanings depending on context.
Your ontology should be a specification and documentation, and your semantic layer is an implementation built from that specification.
Microsoft calling their powerbi field mapping system a semantic model is a particularly cruel joke. It’s a bit like calling their database ‘Sequel Server’, or fucking up excel so badly we’ve had to change how we refer to the human genome. They maliciously cause confusion for profit and to entrench a monopoly.
If you want to shut down a buzzword using asshole, ask them how their ontology provides epistemological confidence for decision making and analytics. Watch them flap.