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Snowflake Data Governance: Best Practices, Features, and Integration with Atlan

Updated March 06th, 2025

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Snowflake data governance enables organizations to secure, manage, and regulate data access through a centralized, role-based platform. How does Snowflake enhance data governance to ensure secure, compliant, and efficient data management? Snowflake offers robust features like role-based access controls, data masking, and comprehensive audit trails, ensuring adherence to regulations like GDPR and HIPAA, enabling organizations to protect sensitive data while maintaining accessibility for analytics and AI-driven tasks.

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Key Features of Snowflake Data Governance:

  • Role-Based Access Control (RBAC): Define user roles and permissions to ensure appropriate data access levels.
  • Data Masking: Protect sensitive information by masking data based on user roles.
  • Audit Trails: Monitor data access and modifications with detailed logs for compliance tracking.
  • Data Quality Monitoring: Utilize system and user-defined data metric functions to maintain data integrity.
  • Object Tagging: Categorize and track data assets for better management and compliance.

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This article covers Snowflake’s native data governance features, what they enable, and how you can enhance them.

It will also explain how to build a metadata-led control plane for Snowflake to manage and govern data across internal, external, source, target, and intermediary systems, providing a consistent, end-to-end view of all your assets.

Let’s get right to it!

Table of contents #

  1. How does Snowflake enhance data governance?
  2. Enhancing Snowflake’s data governance with Atlan
  3. Snowflake and Atlan integration: Customer success across industries
  4. Unlocking the complete potential of Snowflake with Atlan
  5. FAQs about Snowflake Data Governance
  6. Snowflake data governance: Related reads

How does Snowflake enhance data governance? #

Snowflake comes with native data governance features. Snowflake, as the primary data storage and processing layer of the data stack, offers features for data asset discovery, collaboration, and governance—collectively known as Snowflake Horizon. These tools help organizations unlock value from their data by enabling discovery and governance within the Snowflake AI Data Cloud.

You can leverage features like

Some of the more advanced features, such as automated metadata enrichment and lineage generation, involve using custom solutions built on top of Snowflake Cortex’s generative AI capabilities, where you can use the best LLMs from Mistral, Reka, Meta, Google, including Snowflake’s open-source model called Arctic.

Snowflake’s Copilot also uses generative AI to help data engineers and users write better workflows and queries. It operates within Cortex, which securely leverages your enterprise data and metadata to build and run queries, reports, dashboards, and workflows.

While there are many native tools to make life easy within Snowflake, many organizations use other peripheral tools for data movement, security, observability, quality, etc.

In their Snowflake 2024 Trends Report, Snowflake reported a 70% growth in the application of data governance features across its platform between 2023 and 2024. This includes advancements in data access controls, tagging, and masking policies, which have enabled organizations to protect sensitive data effectively while still making it accessible for analytical and AI-driven tasks. Notably, the number of queries run against protected data increased by nearly 150%, indicating higher utilization of these governed data sets for analysis and machine learning (ML) applications .

Snowflake tries to solve this by enabling interoperability between different platforms and tools. One such example is the support for open formats like Apache Iceberg, and another one is the open-sourcing of the Polaris data catalog.

This is where a single control plane can make life easier. One control plane for your entire data stack enables data democratization and self-service by giving you a single interface for interacting with and accessing all your data assets, irrespective of their source. It democratizes data by enabling everyone in your organization to search and discover any data asset within the ecosystem with various search filters, categories, tags, and other options and then request access to the data asset.

That’s exactly what the next section will be about. Let’s look at how Snowflake and Atlan work together to enhance an organization’s data governance experience.

Enhancing Snowflake’s data governance with Atlan #

With Atlan to unify your entire data ecosystem (Snowflake and non-Snowflake assets), you get the following, among other things:

  • An intuitive, multi-layered data asset search and discovery
  • A business glossary and a semantic layer of metrics
  • A governance layer for access control, data privacy, and protection
  • Advanced collaboration and data sharing capabilities

Atlan serves as a metadata-powered control plane, managing search, discovery, business glossaries, lineage, governance, quality, and more. It’s built on a metadata lakehouse that integrates with all the tools in your data stack.

Atlan as a Metadata-powered control plane

Atlan as a Metadata-powered control plane - Image by Atlan.

With such a control plane, you can leverage all the metadata that is already captured by Snowflake in the various schemas, such as ACCOUNT_USAGE, ORGANIZATION_USAGE, MONITORING, etc., in the SNOWFLAKE database.

Atlan also performs two-way sync on object tags, allows you to preview data from Snowflake assets, and mines query history to extract and build a reliable lineage graph, among other things.

With all your metadata in Atlan, you can manage access, discovery, visibility, and governance from a single control plane.

This is exactly how Indica Worldwide leveraged Atlan to become one tool that they used to “understand what’s going on within [its] data estate, what [the] data hold(s), and what its characteristics are, and how it’s being used.”

This was quite challenging for an operation of Indica’s size, as “[it has] some clients who have 40 million customers and build tens upon tens of billions of rows of data. [It’s] Atlan instance right now has something like 500,000 assets in it.”

While Atlan leverages all the native Snowflake data cataloging and governance features, it also offers novel and value-adding features, such as embedded collaboration and active data governance.

These capabilities enable you to collaborate and govern your data better by building upon and activating the inherent value in all the metadata flowing from Snowflake and other tools in your organization’s data stack.

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, and enable self-service access to data. 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!

Unlocking the complete potential of Snowflake with Atlan #

To get you started and fully onboarded, Atlan provides you with several resources to help you out with Snowflake connectivity. These resources range from step-by-step onboarding for connecting Atlan with Snowflake, crawling metadata from Snowflake, setting up PrivateLink in Azure or AWS, enabling OAuth, and managing Snowflake tags, among other things.

Community examples and use cases #


There’s an active community of Atlan champions that finds innovative ways to incorporate Atlan into their organization’s culture and business processes. These innovations have various themes, including metadata ingestion, documentation culture, project management, and glossary.

Let’s look at some prime examples:

  • Creating a metrics glossary and a business glossary to better understand data assets across the organization and implement a uniform organizational language for metrics and KPIs.
  • Defining personas for data users to better collaborate and govern data across the organization. This goes to the heart of implementing role-based access, data masking, and data sharing controls, among other things.

There are several more of these examples, which you can explore on Atlan’s exclusive community portal.

Best practices and recommendations #


In addition to the community examples, Atlan also has its own recommendations and best practices around metadata ingestion, data security, and privacy.

Following these will enable a more secure and functional connection with Snowflake.

  • Use Snowflake’s RSA keypair authentication method as Atlan only supports a secure connection with Snowflake.
  • Choose the ACCOUNT_USAGE method over the INFORMATION_SCHEMA method of fetching metadata from Snowflake.
  • Configure the Snowflake miner to use Atlan’s advanced discovery features like usage and popularity metrics.
  • When you’re using Snowflake’s Business Critical Edition (or above) to meet any security and compliance requirements, you must use PrivateLink (AWS, Azure) to enable a connection between Snowflake and Atlan.

Using all these best practices and recommendations from Snowflake, Atlan, and the community of users who bring these tools together, you can derive the most value from the Snowflake + Atlan integration securely and efficiently. To learn more about how to set up Atlan for Snowflake, please head over to our official documentation.

FAQs about Snowflake Data Governance #

What is Snowflake Data Governance? #


Snowflake data governance encompasses tools and practices that Snowflake provides to manage, secure, and control data. This includes managing access, ensuring compliance, tracking data usage, and implementing data quality measures.

How does Snowflake ensure data compliance and security? #


Snowflake ensures data compliance through advanced security features like encryption, data masking, and access control. These features help meet regulatory requirements and protect sensitive information.

What tools are available in Snowflake for managing data governance? #


Snowflake offers various native tools such as role-based access control, data masking, and data lineage tracking to support data governance. Integrations with platforms like Atlan provide additional metadata management and enhanced governance capabilities.

How can I set up role-based access controls in Snowflake? #


Role-based access controls (RBAC) in Snowflake allow you to define roles and permissions for users, ensuring that data access aligns with each user’s responsibilities and compliance requirements. This structure helps protect data integrity and privacy.

How does Snowflake’s approach to data governance improve data quality? #


Snowflake improves data quality through tools that monitor data usage, enforce data consistency, and allow integration with external data quality platforms. These practices ensure that data is accurate, consistent, and reliable.

How can Snowflake help with regulatory compliance? #


Snowflake’s compliance tools include data masking, encryption, and robust access controls that aid in meeting industry regulations like GDPR and HIPAA, protecting sensitive information and ensuring data security.

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