Bring Snowflake semantics directly into your Sisense data models

Data teams put significant work into defining what their data means: documenting tables and columns, applying tags, and establishing business logic in platforms like Snowflake. But when that data is brought into an analytics platform, teams often end up having to recreate some of that context manually. This leads to more time spent on setup, duplicated work, and can cause important definitions to drift.

With the rollout of Sisense 2026.3.2, customers connecting Sisense to Snowflake can import their existing tags and descriptions directly into their Sisense data models. That means less manual modeling work and more consistent context across platforms. And if the semantics change in Snowflake, a simple refresh brings the updated context into Sisense.

Why semantic context matters

A data model contains more than tables, columns, and relationships. The context around that data helps people, and increasingly AI, understand what it actually means.

Consider a field called ARR. The team that created it may know exactly what it represents and how it should be used. Someone encountering the model for the first time needs more information. So does an AI system trying to answer a question about it.

Descriptions, tags, business definitions, and other semantic metadata provide that context.

This becomes especially important as AI plays a bigger role in analytics. Sisense grounds AI experiences in governed data and business context so that answers reflect the definitions and metrics organizations actually use.

The more useful context your data model contains, the stronger that foundation becomes.

Import Snowflake semantics without rebuilding them

Snowflake semantic import makes it easier to preserve context your team has already created.

When connecting Snowflake to Sisense, the Import Semantics option automatically brings supported semantic metadata into the Sisense modeling layer. The option is enabled by default, so teams can incorporate that context as part of their existing modeling workflow.

Sisense can import:

• Table and column descriptions, populating the corresponding description fields in the Sisense model.
• Semantic categories, which are mapped to tags in Sisense.
• Privacy categories, which are ingested and stored for future AI workflow integration.

Instead of copying descriptions and tags between platforms, teams can carry that existing context into Sisense automatically.

Keep your semantics up to date

Data models don’t stand still. Definitions change, descriptions improve, and teams add new context as the business evolves.

With Refresh Semantics, Snowflake users can re-import semantic metadata on demand. Sisense asks for confirmation before refreshing because the operation overrides existing descriptions in the model.

So when your Snowflake semantics change, keeping Sisense aligned is just a click away.

Put your existing Snowflake context to work

The immediate benefit is time saved. If your organization already maintains descriptions and tags in Snowflake, there’s little value in manually recreating that work somewhere else.

But reducing setup is only part of the story.

Importing semantics can help teams maintain consistency between Snowflake and Sisense, preserve business context as data moves into the analytics layer, and give both people and AI richer information for understanding the data they’re working with.

That complements Sisense’s broader investment in semantic capabilities. Semantic enrichment, for example, can generate rich descriptions for tables and columns to improve comprehension for people and provide more relevant context for Sisense Intelligence.

Now there’s another path: when that context already exists in Snowflake, bring it with you.

For your team, that means no unnecessary recreation or manually copying descriptions from one system to another. Users get more of the context they’ve already defined, available where their analytics and AI experiences are built.

Ready to put your Snowflake semantics to work in Sisense? Learn more about connecting Snowflake to Sisense and start bringing existing context directly into your data models. Book a demo today.