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

Updated September 27th, 2024

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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.
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Let’s get right to it!

Table of contents #

  1. Snowflake’s native data governance features
  2. Data governance in Snowflake with Atlan
  3. Unlocking the complete potential of Snowflake with Atlan
  4. Related reads

Snowflake’s 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 data classification, data quality and monitoring metrics, object tagging, and dynamic masking using SQL statements or Python UDFs.

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.

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.

Data governance in Snowflake 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.

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.

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