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Design semantic views around the recurring business questions and decisions people need to address—not around individual agents. A business-first model gives different consumers a shared set of definitions for entities, relationships, metrics, and dimensions. Snowflake captures the principle plainly: “Design from the perspective of your end users, not the database.”
Start with the decisions people need to make
Before choosing tables, joins, or an AI consumer, collect representative questions from stakeholders, recurring reports, and analysis workflows. For each question, clarify the decision it supports and the words the person asking it uses. That helps establish what the model should mean, rather than letting the shape of the source database dictate the business interface.
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Snowflake’s modeling guidance suggests four useful workshop prompts:
- What business entities exist in your data?
- How do these entities relate to each other?
- What metrics are important to your business?
- What dimensions do you use to analyze these metrics?
These are prompts for defining a model, not evidence about how often people search for those exact phrases.
#1 Best Overall
Set scope by business domain and use case
Organize concepts around recognizable business topics such as sales, marketing, or customer support. Split views when distinct use cases need different interfaces; do not split them simply because data happens to be stored in separate schemas or tables. An agent may use a view, but its implementation should not determine the business domain or own a separate copy of metric logic.
Snowflake’s guidance recommends designing from the end user’s perspective, using business language, and organizing by domain and use case rather than physical data structure. A practical scoping test is whether a stakeholder can understand what questions a view is meant to answer and which concepts it contains.
Define the business model before mapping it to tables
Identify the entities, their relationships, the metrics that matter, and the dimensions used to analyze those metrics. Only then map those concepts to source data. This order makes it easier to spot when a physical join or column does not faithfully represent the business meaning people expect.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- List the entities. Name the business objects users ask about, using terms they recognize.
- Describe relationships. Specify how entities connect and which analysis paths are valid.
- Define metrics and dimensions. State how important measures are calculated and how users can break them down.
- Map to physical data. Connect concepts to source tables or views, define joins, and implement the calculations. Snowflake recommends a simple star schema as a starting point, not as a universal requirement.
Make the model understandable and discoverable
Descriptions and synonyms help bridge stakeholder language to modeled concepts. Use terms people actually use, and explain what a metric means, how relationships work, and which filters or dimensions are appropriate. A name that is technically precise but unfamiliar to users can make a sound model hard to find or interpret.
Rank #3
Snowflake’s Best practices for modeling semantic views covers business-first design, descriptions, synonyms, relationships, metrics, filters, testing, and iteration. Its overview of semantic views describes moving from a business model to physical data and use cases.
Test the view with representative questions
Try the model against questions stakeholders actually ask. Check whether the intended entities, relationships, metric definitions, and dimensions produce an interpretation that matches the business definition. When results diverge, revise the definitions or structure rather than adding a separate agent-specific version of the logic.
Rank #4
Snowflake’s guidance treats testing and iteration as part of modeling. It also notes a roughly 100,000-token context-window consideration for large semantic views used with agent instructions and conversation history. That is a product-specific modeling note, not evidence of a general business outcome or a reason to design the model around an agent.
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Semantic definitions can serve more than one kind of consumer, but reuse is a platform capability to verify—not a guarantee that one model works unchanged everywhere. Snowflake says semantic views can be queried directly and used with Cortex Agents. dbt describes its Semantic Layer as defining metrics on existing models and handling joins.
Best Value
When evaluating an implementation, check whether it can represent your required entities, relationships, metrics, dimensions, and filters; whether descriptions and synonyms support your users’ language; which BI tools, APIs, SQL workflows, and AI systems can consume the definitions; how joins and calculations are exposed; and how permissions, ownership, reviews, versioning, and changes are managed.
Snowflake’s semantic views overview, Horizon Context documentation, dbt’s dbt Semantic Layer documentation, and its Semantic Layer quickstart describe platform-specific capabilities. They do not establish a neutral benchmark proving one implementation universally superior.
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