Snowflake is the data plane, storing and processing enterprise data at scale. Atlan is the context layer for AI, capturing the business meaning, cross-system lineage, governance policies, and certified semantics that make that data AI-ready. As Snowflake’s 2025 Data Governance Partner of the Year and Snowflake Intelligence launch partner, Atlan layers on top of Snowflake Horizon rather than replacing it, feeding certified enterprise context to every Snowflake AI agent via MCP.
| What Atlan is | The context layer for AI, governed infrastructure that delivers enterprise knowledge to every model, agent, and team from a single source of truth |
| What Snowflake is | The data plane, the warehouse that stores, processes, and governs data at enterprise scale |
| Integration method | Native connector (key-pair auth, OAuth/Okta, AWS/Azure PrivateLink) + bidirectional metadata sync + MCP Server context delivery |
| Partnership | Snowflake’s 2025 Data Governance Partner of the Year; OSI launch partner (Sept 2025); Snowflake Intelligence launch partner |
| Key products connected | Enterprise Data Graph, Context Engineering Studio, Context Lakehouse, Atlan MCP Server connecting to Snowflake Cortex Analyst, CoWork, CoCo, Genie |
| Accuracy impact | 5x improvement in agent accuracy when grounded in Atlan’s context layer (Atlan AI Labs) |
Snowflake is the data plane. Atlan is the context plane.
Permalink to “Snowflake is the data plane. Atlan is the context plane.”Every enterprise has two distinct architectural needs. One layer processes and stores data: that is the data plane. Snowflake is purpose-built for that role. The other layer holds the meaning of that data: what each metric means, where each table came from, who owns it, which definition of “revenue” is correct, and which governance policy applies. That is the agent context layer.
Without the context plane, AI agents can query data but cannot reason about it reliably. A Snowflake agent asked “what is our revenue for Q2?” can pull a number from a table. It cannot tell you whether that number uses the sales team’s definition or the finance team’s definition, whether the table is certified or deprecated, or whether the pipeline that feeds it ran successfully this morning.
Atlan’s context plane addresses exactly this gap:
- Snowflake (data plane): storage, compute, query execution, access control via Snowflake Horizon, native governance within the warehouse boundary
- Atlan (context plane): Enterprise Data Graph across 100+ systems, Context Engineering Studio, Context Lakehouse, MCP server delivery, spanning every tool in the data stack
Atlan does not replace Snowflake Horizon. It enriches it. Horizon enforces policies at query time inside Snowflake. Atlan defines those policies in a collaborative workspace and pushes them into Horizon automatically via bidirectional tag sync. The frame to use: Atlan layers on top of Snowflake Horizon. Not an alternative. An amplifier.
Is your data estate ready for AI agents?
Assess Your ReadinessHow does Atlan connect to Snowflake technically?
Permalink to “How does Atlan connect to Snowflake technically?”Atlan connects to Snowflake via a native connector that ingests schemas, query history, tags, and quality metrics. That raw metadata flows into the Enterprise Data Graph, gets enriched in the Context Engineering Studio, and is stored in the Context Lakehouse, where Atlan’s MCP Server delivers it directly to Snowflake agents at query time.
Connector and metadata ingestion
Permalink to “Connector and metadata ingestion”Authentication options include key-pair, OAuth/Okta, and password, with AWS PrivateLink and Azure Private Link for secure enterprise connectivity. Two extraction methods are available: Account Usage views (SNOWFLAKE database) and Information Schema.
What flows from Snowflake into Atlan: schemas, tables, views, columns, SQL query history, native Snowflake tags, usage and popularity signals, and Data Metric Function (DMF) quality results. Column-level lineage is reverse-engineered from SQL query history; Atlan parses historical queries to reconstruct lineage automatically, without instrumentation. Technical setup documentation: docs.atlan.com/apps/connectors/data-warehouses/snowflake.
Enterprise Data Graph construction
Permalink to “Enterprise Data Graph construction”Ingested Snowflake metadata joins with metadata from every other connected system: dbt, Looker, Tableau, BigQuery, Databricks, Salesforce, and 95+ more. The enterprise data graph produces cross-system column-level lineage, tracing a dashboard metric back through a BI layer, through a transformation pipeline, to the source warehouse table. This cross-system view is what Snowflake-native lineage cannot provide; Snowflake’s lineage boundary stops at the warehouse edge.
Context Engineering Studio enrichment
Permalink to “Context Engineering Studio enrichment”Data owners and stewards add certified descriptions, business glossary terms, KPI definitions, ontology relationships, and governance policies via a collaborative interface. Context agents auto-generate descriptions at scale: 690K+ descriptions generated across 50+ enterprise customers, with 87% rated on par or better than human writing. Context is versioned and tested; CI-integrated eval suites validate context before it is published to agents.
What flows from Atlan back into Snowflake: enriched descriptions, business glossary definitions, governance policies (via tag-based masking), Semantic Views definitions, and quality rules (DMFs).
Context Lakehouse and MCP Server delivery to Snowflake agents
Permalink to “Context Lakehouse and MCP Server delivery to Snowflake agents”The Context Lakehouse (Iceberg-native, open formats, vector-native) stores the certified context that Atlan’s MCP Server reads from. Atlan’s MCP server for Snowflake delivers lineage graphs, ownership records, glossary definitions, quality metrics, and semantic relationships directly to Snowflake agents at query time.
Snowflake’s own MCP Server and Atlan’s MCP Server work in parallel: Snowflake’s exposes warehouse context; Atlan’s exposes enterprise context spanning the full stack. Agents query both through a standard protocol with no custom integration required.
Architecture flow:
| Step | What Atlan Does | What Snowflake Receives |
|---|---|---|
| 1. Connect | Native connector authenticates to Snowflake (key-pair / OAuth / PrivateLink) | No change, standard auth flow |
| 2. Ingest | Pulls schemas, query history, tags, DMF quality results, usage signals into the Enterprise Data Graph | Metadata mirrored; nothing removed from Snowflake |
| 3. Cross-system lineage | Joins Snowflake metadata with dbt, Looker, Tableau, BigQuery, Databricks, and 95+ other connectors; constructs column-level cross-system lineage | |
| 4. Enrich | Context Agents generate certified descriptions, glossary terms, KPI definitions; Context Engineering Studio validates before publication | |
| 5. Sync back | Enriched descriptions, governance policies, Semantic Views definitions, quality rules (DMFs) write back into Snowflake | Enriched Snowflake assets; governance policies active at query time |
| 6. Deliver to agents | Atlan MCP Server reads from Context Lakehouse and delivers structured enterprise context to CoWork, CoCo, Cortex Analyst, and Genie on demand | Snowflake agents receive cross-system lineage, certified definitions, ownership, quality status per query |
Atlan vs Snowflake Cortex: what is the difference?
Permalink to “Atlan vs Snowflake Cortex: what is the difference?”Cortex (including CoWork, CoCo, and Cortex Sense) operates within the Snowflake boundary, building intelligence from data and activity inside the warehouse. Atlan operates across the enterprise: 100+ systems, cross-system lineage, certified semantics from every data source. Cortex queries data. Atlan tells Cortex what that data means.
The framing is not competitive. Snowflake Cortex is correctly focused on the warehouse layer, which is its job. Cortex Sense (announced Snowflake Summit 2026) derives runtime signals from Snowflake’s own activity. Atlan’s Enterprise Data Graph spans 100+ systems and delivers certified context via the Context Engineering Studio. These are complementary, not competing architectures.
| Capability | Snowflake Cortex stack (CoWork / CoCo / Cortex Sense) | Atlan Context Layer | How they work together |
|---|---|---|---|
| Scope | Snowflake warehouse boundary | 100+ enterprise systems (warehouses, BI, pipelines, CRM, and more) | Atlan extends Cortex context beyond the warehouse |
| Metadata connectors | 5 connectors (Horizon Context, private preview) | 100+ connectors (GA) | Atlan’s EDG feeds richer metadata into Cortex agents |
| Business glossary | Roadmap: H2 2026 | Shipped (GA) | Atlan’s certified glossary terms serve as the semantic layer Cortex queries today |
| Context enrichment | Cortex Sense: runtime signals from Snowflake activity | Context Agents: 690K+ descriptions, cross-system, 87% quality parity with human writing | Cortex Sense + Atlan Context Agents = runtime + governed enterprise context |
| Semantic studio | Private preview | Context Engineering Studio (GA), CI-integrated, versioned, tested | Atlan validates context before Cortex agents use it |
| Cross-system lineage | Snowflake-internal only | Column-level lineage across dbt, Looker, Tableau, BigQuery, Databricks, Salesforce, and more | Cortex agents can trace data provenance end-to-end across the stack |
| Governance enforcement | Tag-based masking, row-level security within Snowflake | Policies defined in Atlan, pushed to Snowflake Horizon via bidirectional tag sync | Atlan defines; Horizon enforces, one policy, two enforcement points |
| MCP delivery | Snowflake MCP Server: warehouse context | Atlan MCP Server: enterprise context (lineage, ownership, semantics, quality) | Agents query both servers via standard MCP protocol, no custom integration |
| Agent accuracy | Warehouse-scoped answer quality | 5x accuracy improvement when grounded in Atlan’s context layer (Atlan AI Labs) | Grounding Cortex agents in Atlan context delivers enterprise-grade accuracy |
| Deployment status | CoWork/CoCo GA; Cortex Sense GA (Summit 2026) | All four products GA | Both available today; integration is active |
For a deeper look at how the context layer for Snowflake Cortex works, see the dedicated guide. For the difference between what is context engineering and how it applies to the Context Engineering Studio, see the context engineering explainer. The MCP connected data catalog pattern is how Atlan serves this context across every agent.
What the Atlan + Snowflake partnership means in practice
Permalink to “What the Atlan + Snowflake partnership means in practice”Atlan holds three formal partnership designations with Snowflake: 2025 Data Governance Partner of the Year, OSI launch partner, and Snowflake Intelligence launch partner. Together they represent co-built standards, co-validated architecture, and a shared commitment to making enterprise data AI-ready.
Open Semantic Interchange (OSI): Launched September 23, 2025, co-led by Snowflake and a coalition of founding partners. OSI creates a universal, vendor-neutral semantic model specification, a standard for how semantic metadata is defined and shared across AI and BI applications. Atlan is a launch partner alongside Salesforce, BlackRock, dbt Labs, Alation, RelationalAI, Cube, Hex, ThoughtSpot, Mistral AI, Sigma, Select Star, and others. For Snowflake users, OSI-compliant semantic definitions from Atlan’s Context Engineering Studio can flow into any OSI-compatible tool in the stack.
Snowflake 2025 Data Governance Partner of the Year: Externally announced via BusinessWire on 2025-06-03. Only one vendor holds this title per year. Atlan holds it for 2025.
Snowflake Intelligence launch partner: Atlan’s MCP Server serves as the Context API for Snowflake Intelligence’s Talk to Data feature. Atlan’s Metadata Lakehouse auto-generates semantic views, so data stewards define business rules in Atlan and those definitions flow into Snowflake Horizon Context automatically.
In practice, this partnership plays out in production deployments. At one data education platform, Talk to Data runs on Claude connected to Atlan MCP, Looker MCP, and BigQuery MCP. The CEO uses it directly and mandates it company-wide: “He’s one of the biggest users of it right now. And if he’s happy, he forces adoption on the rest of the company.” (attributed: director of analytics at a data education platform)
How Atlan’s context layer improves Snowflake AI agents
Permalink to “How Atlan’s context layer improves Snowflake AI agents”Without context, Snowflake AI agents answer questions about data they can query but cannot interpret. With Atlan’s Enterprise Data Graph and Context Lakehouse feeding them via MCP, agents know what each metric means, which table version is authoritative, and what governance policy applies before they write a single query.
The core failure mode without context: Snowflake agents (Cortex Analyst, Snowflake CoWork, Snowflake CoCo) fail on questions that require cross-system understanding: “Which version of revenue does the sales team use vs. finance?”, “Where did this table come from?”, “Is this metric governed?” Without a context layer, agents either hallucinate an answer or return an ambiguous result, and users lose trust.
The accuracy gap: Atlan AI Labs measurement shows a 5x accuracy improvement in agents grounded in Atlan’s context layer. At Workday, deploying Atlan MCP to expose shared business language to AI agents solved definitional ambiguity that was blocking a revenue analysis agent from reaching production. The mechanism: the Enterprise Data Graph constructs the full picture (assets, lineage, definitions, quality status, ownership), the Context Engineering Studio validates and certifies the semantic layer before it ships to agents, and Atlan’s MCP Server delivers that certified context at query time.
Result: Cortex Analyst and CoWork answer using certified, cross-system-grounded context instead of raw schema inference.
For a deeper look at how to build this layer: enterprise context layer and unified context layer implementation.
See how enterprises are deploying Atlan + Snowflake in production
Watch Context Layer LiveReal stories: Atlan + Snowflake in production
Permalink to “Real stories: Atlan + Snowflake in production”“I was thinking in years, maybe I can start thinking in weeks. Atlan’s automated lineage, that was an expectation of mine, the technology and the employees will be really much more faster than our company can follow.” (Senior data leader at a European financial institution)
“We technically enriched 6,700 assets within a few minutes. What would take normally months literally, of course, within 20 minutes, that was actually really cool.” (Data governance lead at a Fortune 500 retailer)
“Our COO, for the first time, requested Snowflake access because he was like, with Atlan, and my rusty SQL skills from university, I think I should be able to self-serve some more stuff.” (Data lead at a European SaaS company, 1,000+ employees)
Why Snowflake and Atlan are stronger together
Permalink to “Why Snowflake and Atlan are stronger together”- Snowflake handles data storage, compute, and warehouse-level governance. Atlan handles the business meaning, cross-system lineage, and certified semantics that sit above it.
- Atlan connects to Snowflake via a native connector and syncs metadata bidirectionally, context flows in both directions.
- The Enterprise Data Graph spans 100+ systems; Snowflake is one node in a broader cross-enterprise context map.
- Cortex (CoWork, CoCo, Cortex Sense) operates within Snowflake’s boundary. Atlan’s context layer for AI agents spans the full enterprise. Together they give AI agents warehouse compute plus enterprise intelligence.
- Atlan’s MCP Server and Snowflake’s MCP Server work in parallel. Agents query both through a standard protocol.
- Atlan holds three formal partnership designations with Snowflake: 2025 Data Governance Partner of the Year, OSI launch partner, Snowflake Intelligence launch partner.
- Agents grounded in Atlan’s context layer for Snowflake are 5x more accurate (Atlan AI Labs measurement).
- “Atlan layers on top of Snowflake Horizon.” This is the one-sentence architectural answer to every comparison question.
FAQs about Atlan and Snowflake
Permalink to “FAQs about Atlan and Snowflake”1. How does Atlan integrate with Snowflake?
Permalink to “1. How does Atlan integrate with Snowflake?”Atlan connects to Snowflake via a native connector that authenticates using key-pair, OAuth/Okta, or password auth, with AWS PrivateLink and Azure Private Link for secure enterprise connectivity. The connector ingests schemas, query history, tags, and Data Metric Function results into Atlan’s Enterprise Data Graph. Enriched context, certified descriptions, governance policies, business glossary terms, and Semantic Views, then syncs back into Snowflake bidirectionally. Snowflake agents receive enterprise context via Atlan’s MCP Server at query time.
2. What is the difference between Atlan and Snowflake Cortex?
Permalink to “2. What is the difference between Atlan and Snowflake Cortex?”They are not alternatives. Snowflake Cortex (CoWork, CoCo, Cortex Sense) processes and enriches data within the Snowflake warehouse boundary. Atlan operates across the full enterprise stack with 100+ connected systems and delivers certified, cross-system context to Cortex agents via MCP. Cortex queries data. Atlan tells Cortex what that data means. Together they give agents warehouse compute plus enterprise-wide contextual intelligence.
3. Does Atlan replace Snowflake Horizon?
Permalink to “3. Does Atlan replace Snowflake Horizon?”No. Atlan layers on top of Snowflake Horizon rather than replacing it. Horizon enforces tag-based masking, row-level security, and access policies at query time inside Snowflake. Atlan defines those policies in a collaborative workspace and pushes them into Horizon automatically via bidirectional tag synchronization. The result is compliance-in-flow rather than compliance-by-audit.
4. How does Atlan’s MCP Server work with Snowflake agents?
Permalink to “4. How does Atlan’s MCP Server work with Snowflake agents?”Atlan’s MCP Server reads from the Context Lakehouse and delivers structured enterprise context to any MCP-compatible Snowflake agent, including Cortex Analyst, CoWork, CoCo, and Genie. The context includes lineage graphs, ownership records, glossary definitions, quality metrics, and semantic relationships. Snowflake’s MCP Server and Atlan’s MCP Server work in parallel, each delivering their respective context layer via standard protocol, with no custom integration required.
5. What metadata does Atlan ingest from Snowflake?
Permalink to “5. What metadata does Atlan ingest from Snowflake?”Atlan ingests schemas, tables, views, column definitions, SQL query history, native Snowflake tags, usage and popularity signals, and Data Metric Function quality results. Column-level lineage is reverse-engineered from SQL query history; Atlan parses historical queries to reconstruct lineage automatically, without instrumentation. This raw Snowflake metadata joins with metadata from every other connected system in the Enterprise Data Graph, producing cross-system lineage that Snowflake-native tools cannot provide.
6. How does Atlan improve Snowflake Intelligence and Talk to Data?
Permalink to “6. How does Atlan improve Snowflake Intelligence and Talk to Data?”Atlan is a launch partner for Snowflake Intelligence. Atlan’s MCP Server serves as the Context API for Talk to Data: when a user asks a natural-language question, Snowflake Intelligence retrieves certified context from Atlan’s Context Lakehouse to ground its answer. Atlan’s Metadata Lakehouse also auto-generates Snowflake Semantic Views: data stewards define business rules and metric definitions in Atlan’s Context Engineering Studio, and those definitions flow into Snowflake Intelligence automatically.
7. What is the Open Semantic Interchange and what does Atlan’s role mean for Snowflake users?
Permalink to “7. What is the Open Semantic Interchange and what does Atlan’s role mean for Snowflake users?”The Open Semantic Interchange (OSI) is a universal, vendor-neutral semantic model specification co-launched by Snowflake, Salesforce, dbt Labs, Atlan, and ecosystem partners in September 2025. It defines how semantic metadata is shared across AI and BI applications without proprietary lock-in. For Snowflake users, OSI means semantic definitions curated in Atlan’s Context Engineering Studio can flow into any OSI-compatible tool in the stack.
8. Is Atlan competitive with Snowflake or complementary?
Permalink to “8. Is Atlan competitive with Snowflake or complementary?”Complementary. Snowflake is the data plane: storing, processing, and governing data at the warehouse layer. Atlan is the context plane: capturing business meaning, cross-system lineage, and governed semantics that live above the warehouse and make data AI-ready. Atlan enriches Snowflake Horizon rather than replacing it and holds three formal partnership designations: 2025 Data Governance Partner of the Year, OSI launch partner, and Snowflake Intelligence launch partner.
Sources
Permalink to “Sources”- Atlan Wins Snowflake Data Governance Data Cloud Product Partner of the Year. BusinessWire. 2025.
- Snowflake Open Semantic Interchange Launch Partner. Atlan. 2025.
- Making Talk to Data 3x More Accurate with Atlan. Atlan. 2026.
- MCP Servers on Snowflake: Unify and Extend Data Agents. Snowflake. 2026.
- Snowflake CoWork: The Personal Work Agent for Every Knowledge Worker. Snowflake. 2026.
- Atlan Connector for Snowflake. Atlan Docs. 2026.
- Snowflake Intelligence: Getting Started with MCP Connectors. Snowflake. 2026.
