Context Layer for Snowflake: Native Capabilities & Limitations

Context layer for Snowflake: At a glance

Aspect	Details
What it is	Governed infrastructure connecting raw data, lineage, governance, semantics, and organizational knowledge into a unified layer for humans and AI.
Native context producers	Snowflake Horizon, Semantic Views, object tagging, Data Metric Functions, External Lineage API, Cortex AI functions.
Native context consumers	Cortex Analyst, Cortex Search, Cortex Agents, Snowflake MCP server.
What Snowflake handles natively	Technical metadata (SNOWFLAKE.CORE and ACCOUNT_USAGE), governance (Horizon Catalog), lineage within perimeter, Semantic View definitions, quality signals (Data Metric Functions).
Where native coverage is partial	Organizational knowledge, column-level external lineage, governance propagation beyond Snowflake.
What you need an external platform for	Cross-system semantics, external governance propagation, multi-tool lineage at column level, business context from outside Snowflake.
What an enterprise context layer adds	Enterprise-wide metadata lakehouse, cross-system governance sync, external lineage stitching, context propagation across the full data stack.
Who addresses the enterprise gap	Context layer partners like Atlan that sit across the full data stack as a sovereign, open, and interoperable context layer.

What are the native context capabilities in Snowflake?

Context is built from many different sources, including technical metadata, governance, lineage, quality, organizational knowledge, and documentation. Snowflake automatically handles some of these, including object dependencies, structural metadata, and lineage within its perimeter. Semantics, cross-system governance, and quality are not natively solved across the full data stack.

Having these different constructs connected to one another unlocks the context layer. To address this, you need to better handle both the production and consumption of context.

Which Snowflake services act as context producers?

Snowflake Horizon is the foundational layer of native Snowflake metadata, which covers governance, lineage, and quality. All of this metadata is built upon and attached to the core structural metadata from the SNOWFLAKE.CORE schema. Snowflake also provides a host of views in the SNOWFLAKE.ACCOUNT_USAGE schema that provide a granular view of what exists in Snowflake.

Several Snowflake features contribute to context production across different dimensions:

Object tagging: Applies governance labels to tables and columns for classification and policy enforcement.
Data Metric Functions: Produce data quality signals that feed into the context layer.
External Lineage API: Ingests lineage events from external tools like dbt and Airflow via OpenLineage.
Cortex AI functions: Cortex AI functions like AI_CLASSIFY and AI_EXTRACT enrich metadata automatically using AI-driven classification and extraction.

The focus of Snowflake Horizon, for the most part, is governance. It does not necessarily solve for data semantics — it does not let you define business metrics in organizational language, define complex but clear relationships, or create facts and dimensions associated with the metrics.

That is where Snowflake Semantic Views came in. They are designed to standardize the meaning of metrics and their underlying objects across Snowflake. Semantic Views therefore become a very important part of the context production within Snowflake.

Snowflake Semantic Views diagram

Snowflake Semantic Views — Source: Snowflake

Which Snowflake services act as context consumers?

Once produced, the context is ready to be consumed by the internal Snowflake services that power Snowflake Intelligence (currently in Public Preview), the key ones being:

Cortex Analyst: Cortex Analyst relies specifically on Snowflake Semantic Views to take questions in natural language and convert them to real SQL queries to get the data.
Cortex Search: Cortex Search works primarily for unstructured data that is indexed by Snowflake. Users can ask questions in natural language, and Snowflake gets the answers using vector and keyword-based semantic retrieval using RAG.
Cortex Agents: Cortex Agents work with Cortex Analyst and Cortex Search to answer complex questions that span across a broad organizational context available within Snowflake.

Snowflake context-rich AI agents architecture

Snowflake context consumers — Source: Snowflake

Both Cortex Analyst and Cortex Search are used as tools by Cortex Agents. There is also a Snowflake MCP server, but that is meant to be used by external agents and context engines.

What are the challenges with using only Snowflake’s native context features?

Snowflake’s context layer foundations work well for data assets that are produced and consumed fully within Snowflake. However, features like Semantic Views, tags, classifications, and governance policies are limited to Snowflake.

When data is being stored, processed, shared, and visualized outside the Snowflake ecosystem — which is often the case with organizations that have a variety of tools in their data stack — the context gets fragmented.

Here is how that fragmentation manifests in practice:

Horizon metadata scope: Horizon metadata is limited to Snowflake; however, you can enrich it using the External Lineage API, which accepts OpenLineage events from tools like dbt and Airflow.
Granular lineage gap: Granular lineage is only available for Snowflake objects. External Lineage API only supports tabular lineage, leaving transformation-heavy cross-system pipelines without full depth.
Governance propagation: Governance rules, policies, and enforcements do not propagate beyond Snowflake (either from or to Snowflake).
Business context fragmentation: Business context is stored in a variety of tools, and there is no easy way to bring that context into Snowflake.
Snowflake Intelligence inconsistencies: Snowflake Intelligence works well within its context boundaries, but fragmented and incomplete context across systems can lead to inconsistent outputs.
Semantic Views scope: Semantic Views provide metric definitions for data within Snowflake, but cannot incorporate definitions from external tools like dbt or Looker.

Given these limitations, organizations running multi-tool data stacks need a more expansive approach to context. This is where the need for an enterprise context layer arises.

Here is what you need to close this context gap:

Enterprise-wide metadata lakehouse: An enterprise-wide metadata lakehouse sources metadata from every tool in the data stack, going beyond Snowflake.
Context assimilation system: Integrates with Snowflake, bringing external context in and pushing Snowflake context out to the rest of your data and AI stack.
Governance and lineage propagation: Syncs governance rules, compliance policies, lineage, and quality signals across systems bidirectionally.
Context synchronization: Keeps context aligned between internal Snowflake services, external agent platforms, and the broader data stack.

This is exactly what Atlan addresses with its enterprise context layer.

How does Atlan bring the enterprise context layer to Snowflake?

Atlan identified fragmented context as a real problem and built an enterprise context layer using four key components:

Enterprise data graph: Maps the technical metadata across every system in the data stack.
Governance (data and AI) graph: Handles policies, classifications, and compliance across Snowflake and external tools.
Knowledge graph: Handles institutional knowledge that typically lives in people’s heads or disconnected documentation systems.
Active ontology: A coherent, queryable layer that evolves as your data stack and business context change.

Atlan connects to all the systems across your data stack using OSI (Open Semantic Interchange) and Atlan’s own MCP server, which can bring external context into Snowflake. Together, these bring external context into Snowflake and surface Snowflake context to external agents and services.

This architecture operates across three layers:

Business systems: The real context of the business lives in various systems of record, of data, of knowledge, and of semantics. Atlan connects to them all via its 100+ connectors. This metadata is then made available to Snowflake via Atlan’s MCP server.
Context repos: Containers of context that feed from the enterprise context layer into various interfaces, tools, and AI agents. Snowflake’s MCP server and Atlan’s MCP server are instrumental in making this context available to both internal and external services.
Interfaces and agents: Atlan supports all three types of agents that operate in a Snowflake environment. You can build your own agents, place domain-specific agents, and use general-purpose agents that work with Snowflake Intelligence. Atlan’s context layer serves all three without requiring separate integration work for each.

The Snowflake + Atlan integration for context results in context activation, leading to highly trusted output via Snowflake Intelligence.

Real stories from real customers building enterprise context layers with Atlan

"As part of Atlan's AI Labs, we're co-building the semantic layers that AI needs with new constructs like context products that can start with an end user's prompt and include them in the development process. All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan's MCP server."

Joe DosSantos, Vice President of Enterprise Data & Analytics

Workday

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"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets. As we're doing this, we're making life easier for data scientists and speeding up innovation."

Andrew Reiskind, Chief Data Officer

Mastercard

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Moving forward with Atlan’s sovereign context layer for Snowflake

Snowflake has a solid foundation for context, especially with Cortex services, Semantic Views, and Horizon. But these services, with some exceptions, primarily work within the Snowflake ecosystem. Organizations typically use a number of tools in their data stack, depending on the skills of different teams and business use cases. That is where Snowflake’s native context capabilities need to be extended.

Atlan extends these capabilities by lending its enterprise context layer to Snowflake and any other tools in an organization’s data stack. Atlan does so by leveraging OSI and Atlan’s MCP Server.

Ultimately, the result is a highly enriched, cross-platform, certified, and governed context that can be consumed by Snowflake Intelligence. To find more about how Atlan extends Snowflake’s context layer, book a personalized demo.

FAQs about context layer for Snowflake

1. What is a context layer for Snowflake?

A context layer for Snowflake is the connective tissue between your data and the AI systems, bringing together lineage, governance, semantic definitions, and organizational knowledge. This allows Snowflake Intelligence and AI agents to produce consistent, trustworthy outputs. Snowflake covers this well within its own perimeter. For multi-tool data stacks, an enterprise context layer extends that coverage across every system in the stack.

2. Why is a context layer important?

Context is an accumulation of various individually useful constructs, such as relationships, semantics, ownership, trust signals, lineage, governance, and organizational language and knowledge. Context is what helps users and AI agents derive value from data. You can create this layer within a data platform but that context layer is often limited by the platform’s boundary, which is why there is a need for an organization-wide context layer.

3. What Snowflake services use the context layer?

Snowflake’s context layer is used by its agentic services, including Cortex Analyst, Cortex Agents, and Cortex Search. Without a context layer, there is not much these services can do accurately. From a Snowflake standpoint, a context layer would include everything from object definitions and governance rules to lineage, business logic, orchestration, and observability.

4. What is the difference between the semantic layer and the context layer?

A semantic layer allows you to define the meaning of objects, business glossary, metric definitions, and dimensions, among other things. Snowflake’s semantic layer, powered by Semantic Views, defines what your metrics mean and how they are calculated within Snowflake. A context layer includes everything from the semantic layer, as well as governance rules, trust signals, data lineage, and other metadata. Semantic Views tell Cortex Analyst what revenue means. The context layer tells it whether that revenue data is fresh, who owns it, and whether it is certified for the query being run.

5. How does Atlan work with Snowflake Intelligence to provide the context layer?

Atlan brings context from across the organization to Snowflake via its MCP server, enabling non-native metadata to be brought into Snowflake. This contributes to a better experience for lineage, governance, search, discovery, and quality in Snowflake. Atlan works with Snowflake Intelligence on various fronts, such as automatically generating Snowflake Semantic Views from metadata using OSI. Atlan was also a launch partner for Snowflake Intelligence.

6. How are Snowflake Semantic Views used for context?

A Semantic View is a schema-level object in Snowflake. It consists of tables that map to specific business entities, such as customers, sales, orders, and payments. You can define relationships between these tables to create facts, metrics, and dimensions. So instead of writing complex CTEs and JOINs, you can create a Semantic View and just get the metric across the dimension you want.

7. What are the limitations of building a context layer only within Snowflake?

Whether we are talking about Semantic Views or Horizon lineage, tags and policies, or business glossary, all of these things only sit within Snowflake. If the organization uses other tools for business intelligence, data pipeline orchestration, transformation, and data quality, there is no straightforward way to bring all that metadata in. Without the metadata, you cannot create a fully-functional context layer. In such a situation, the business logic will be fragmented, and so will the context.

This guide is part of the Enterprise Context Layer Hub, a complete collection of resources on building, governing, and scaling context infrastructure for AI.

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Context Layer for Snowflake: Native Coverage, Current Limitations & How to Unify Context in 2026

Key takeaways

Quick answer: What is a context layer for Snowflake?

A complete context layer for Snowflake is built using: