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Snowflake Data Catalog: Expanding Native Functionality with Unified Metadata Control Plane

Updated March 06th, 2025

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Data teams often ask, “How do I extend Snowflake’s data cataloging capabilities to manage metadata across our entire ecosystem?” Snowflake’s native features are centered around the ACCOUNT_USAGE schema, its primary technical data catalog which is robust for technical metadata, but they can be limited when it comes to a holistic, cross-platform view. To fully leverage your data, you need an integrated control plane that unifies metadata across various systems like AWS Glue, Iceberg REST, object storage, and Polaris.

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Here’s why you need Atlan as the control plane for Snowflake

  • Snowflake’s native catalog is excellent for technical metadata but lacks cross-platform integration.
  • Atlan enhances metadata governance and discovery across Snowflake and other platforms.
  • Ensure your data is AI-ready and compliant with advanced governance features.
  • Automate metadata management, quality checks, and data classification.
  • Reduce costs by identifying stale data and optimizing storage.

Table of contents #

  1. Snowflake’s native data cataloging features
  2. Atlan as the control plane for Snowflake
  3. Snowflake and Atlan integration: Customer success across industries
  4. Summary
  5. FAQs on Snowflake data catalog
  6. Snowflake data catalog: Related Resources

Snowflake’s native data cataloging features #

Snowflake provides several native data cataloging features, enabling you to leverage collected metadata for searching, discovering, and governing all your data assets. These features focus on the following key areas:

  • Foundational technical data catalog with all the structural and operational data
  • Logging and auditing all the activity within the Snowflake ecosystem to monitor security and cost, among other things
  • Data classification, quality metrics, object tagging, etc., covering the aspects of governance, quality, and lineage

Snowflake stores and manages both technical and operational metadata within the ACCOUNT_USAGE schema under the SNOWFLAKE database.

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Additional schemas, such as DATA_SHARING_USAGE, MONITORING, and TELEMETRY, capture metadata for specific use cases. Technical metadata encompasses the structure, definitions, and properties of Snowflake objects like tables, views, policies, functions, and stored procedures. In contrast, operational metadata focuses on Snowflake’s internal management of infrastructure, data processing, movement, and other operations.

Snowflake also provides fine-grained logging of data access and usage across your organization. However, Polaris, the open-source version of Snowflake’s catalog, lacks full feature parity with the Snowflake catalog. Some features, such as controlling row- and column-level access through an RBAC model, are still in early stages of development within Polaris.

All metadata captured by Snowflake is stored in a comprehensive metadata database, allowing you to derive query analytics, data lineage, and quality metrics, among other insights. Additionally, metadata for Snowflake features such as data classification, data quality and monitoring metrics, object tagging, and masking policies is carefully tracked.

However, Snowflake’s data cataloging features primarily serve as a technical data catalog or, at best, a data dictionary with basic business descriptions, tagging, and categorization.

What it doesn’t provide is a full-fledged metadata control plane that integrates with all systems in your data stack, offering complete control over the search, discovery, and governance of your data assets. This is where Atlan comes into play as the unified metadata control plane for your entire data ecosystem.

Snowflake’s latest advancement, the Polaris Catalog, represents a significant shift in data cataloging capabilities by enabling open-source, cross-platform data management.

Launched in July, 2024, Polaris supports Apache Iceberg, an open table format increasingly popular for its ability to manage data stored across various compute engines, including Apache Flink, Spark, Trino, and others.

Snowflake’s goal with Polaris is to create a unified, vendor-neutral catalog that aligns with its commitment to interoperability, allowing organizations to manage data within Snowflake alongside other platforms like AWS, Microsoft Azure, and Google Cloud without traditional data "lock-in”.

Atlan as the control plane for Snowflake #

The rapid growth of data tools and technologies, along with the explosion in data volume, has triggered an evolution of metadata into big data. This shift makes a Lakehouse approach to metadata essential for gaining full control and maximizing its value.

Such an approach enables the creation of advanced capabilities, including complex automation workflows and end-to-end data ecosystem enablement, all built on top of a lakehouse-driven metadata control plane. Atlan has embraced this approach to handle Snowflake metadata effectively.

Atlan leverages all of Snowflake’s metadata, as discussed in the previous section. While Snowflake captures and manages this core data, Atlan enables you to extract value from it by helping you find relevant, trustworthy data assets, collaborate with peers through shared workspaces, and ensure data sharing is in compliance with your organization’s policies. These capabilities are crucial in today’s data- and AI-driven world.

Bringing cataloging, discovery, and governance together #


Built on top of the crawled metadata, Atlan offers several features that help you seamlessly discover, trust, and govern your AI-ready data:

  • Discovery – Provides an intuitive user interface that allows you to search and discover data assets across all your source and target systems, including Snowflake, by tapping into its internal metadata layer.
  • Governance – Enables data governance based on your organization’s operational model, extending Snowflake’s native governance features. It supports different governance models for various teams, accommodating compliance and regulatory requirements.
  • Classification – Enhances data discovery and trust by applying certification and verification tags to data assets. This feature integrates with Snowflake’s native data classification and tagging, using a two-way syncing mechanism for Snowflake tags.
  • Ownership – Facilitates the design and implementation of a data asset ownership model across your organization, which is particularly useful for data mesh architectures. While Snowflake assigns OWNERSHIP to securable objects, Atlan’s ownership model applies uniformly across your entire data ecosystem.
  • Freshness – Automatically builds trust by signaling the freshness of data. This feature leverages Snowflake’s object metadata and customized queries to infer data freshness in tables or views.
  • Cost Optimization – Uses usage pattern metadata to identify dormant or stale data assets, automating their deprecation to save on storage and compute costs. This feature helps streamline your data ecosystem by reducing unnecessary data complexity.
  • Lineage – Maps the flow of data throughout your platform, providing visibility into how data is transformed from ingestion to consumption. Atlan extracts lineage metadata from Snowflake using both the ACCOUNT_USAGE schema and the INFORMATION_SCHEMA.

Additionally, Atlan supports use cases such as automation, business glossary management, metadata activation, and personalization, which you can explore further here.

Snowflake and Atlan integration: Customer success across industries #

Atlan Named a Leader in The Forrester Wave™, has proven its ability to empower organizations across diverse industries, from banking to healthcare, fintech, and manufacturing, have successfully integrated Snowflake with Atlan to modernize their data stack, streamline data governance, enable self-service access to data, and get the data AI-ready. Here’s how these leading companies have transformed their data operations using this powerful combination.

Austin Capital Bank (Banking) is a fast-growing, product-centric bank that adopted Snowflake and Atlan to modernize their data stack. The integration provided a seamless way to manage data access while ensuring governance. As Ian Bass, Head of Data & Analytics, put it, “Atlan gave us a simple way to see who has access to what."

Scripps Health (Healthcare) leveraged the Snowflake-Atlan integration to manage sensitive healthcare data while adhering to HIPAA requirements. With Atlan tapping into Snowflake’s powerful metadata, they gained end-to-end visibility. “Since Atlan is virtualized on Snowflake, security is no longer a concern,” says Victor Wilson, Data Architect.

Tala (FinTech) uses Snowflake as part of their data stack and integrates it with Atlan, dbt, and Looker. By automating the sync of dbt documentation into Snowflake through Atlan, Tala streamlines its data processes. This allows business users to access a unified data dictionary within Atlan, making data easily understandable.

Aliaxis (Manufacturing), a global leader in water solutions, integrated Atlan with their Snowflake-powered data warehouse to enhance data visibility. Atlan serves as their primary point of reference for data-related queries, acting as a “bridge” to understand data within Snowflake. “If there’s any question you have about data in Snowflake, go to Atlan,” shares Nestor Jarquin, Global Data & Analytics Lead.

These stories highlight the transformative power of Snowflake and Atlan for businesses looking to enhance their data capabilities. Want to see how this integration can work for you? [Book a demo today] and discover the impact of Atlan + Snowflake for your organization!

Summary #

The modern data ecosystem is increasingly diverse, often spanning multiple cloud platforms and on-premises environments. Managing governance, compliance, and context across these systems requires a unified control plane—a single go-to platform that addresses all of your organization’s data needs, regardless of the data consumer’s role. Atlan provides this unified control plane, seamlessly integrating with data ecosystems centered around Snowflake or any other SaaS or PaaS data platform. By doing so, Atlan becomes a critical component in helping your organization unlock the full value of its data.

FAQs on Snowflake data catalog #

What is a “Snowflake data catalog”? #


A Snowflake data catalog is a structured collection of metadata within the Snowflake platform that helps users organize, discover, and manage data assets. Utilizing schemas like ACCOUNT_USAGE, the data catalog provides technical and operational insights, supporting data governance, security, and quality control across Snowflake.

Can I use Snowflake’s catalog with other data platforms? #


While Snowflake’s native catalog is built for its platform, integrations with tools like Atlan enable cross-platform metadata management, allowing you to unify data from AWS, Azure, and Google Cloud.

How does Snowflake catalog data? #


Snowflake catalogs data through its ACCOUNT_USAGE schema and other dedicated schemas like DATA_SHARING_USAGE, MONITORING, and TELEMETRY. These schemas capture technical metadata about data structure and properties and operational metadata on usage, access, and infrastructure, enhancing data tracking and management.

What benefits does a data catalog provide in Snowflake? #


A data catalog in Snowflake centralizes metadata, making it easier to search and manage data assets. This leads to improved data governance, data quality monitoring, streamlined compliance, and efficient resource usage through better data discoverability and transparency.

What are the benefits of using a metadata control plane with Snowflake? #


A metadata control plane, such as Atlan, centralizes data governance, discovery, and compliance across platforms. This is crucial for managing data in modern multi-cloud environments and ensuring AI-readiness.

How secure is Snowflake’s data catalog? #


Snowflake’s data catalog is secure, providing fine-grained access logging, auditing, and role-based access control (RBAC). These features help maintain strict governance over data access and usage, ensuring sensitive data is only accessible to authorized users.

How does a data catalog enhance data governance in Snowflake? #


Snowflake’s data catalog enhances data governance by organizing metadata and providing tools for classification, logging, and tagging. This structured approach helps in tracking data lineage, auditing data access, and managing compliance, thereby supporting robust data governance practices.

Can I automate data cataloging in Snowflake? #


Yes, data cataloging in Snowflake can be automated using integrations with third-party tools like Atlan, which helps unify and streamline metadata collection across platforms, reducing manual tasks and enhancing data catalog accuracy.

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