Snowflake Data Governance: Features, Tools & Best Practices
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Snowflake data governance enables organizations to secure, manage, and regulate data access through a centralized, role-based platform. This platform provides robust compliance features, such as granular access controls, audit trails, and automated policy enforcement, ensuring adherence to regulations like GDPR and HIPAA. Snowflake’s data governance tools streamline data classification, quality management, and privacy protection, supporting seamless collaboration and scalable data governance across teams.
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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 #
- Snowflake’s native data governance features
- Data governance in Snowflake with Atlan
- How organizations making the most out of their data using Atlan
- Unlocking the complete potential of Snowflake with Atlan
- FAQs about Snowflake Data Governance
- 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.
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.
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.
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.
How organizations making the most out of their data using Atlan #
The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:
- Automatic cataloging of the entire technology, data, and AI ecosystem
- Enabling the data ecosystem AI and automation first
- Prioritizing data democratization and self-service
These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”
For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.
A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.
Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #
- Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
- After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
- Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.
Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.
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.
Snowflake data governance: Related reads #
- Snowflake Cortex: Everything We Know So Far and Answers to FAQs
- Snowflake Copilot: Here’s Everything We Know So Far About This AI-Powered Assistant
- Polaris Catalog from Snowflake: Everything We Know So Far
- Snowflake Cost Optimization: Typical Expenses & Strategies to Handle Them Effectively
- Snowflake Horizon for Data Governance: Here’s Everything We Know So Far
- Snowflake Data Cloud Summit 2024: Get Ready and Fit for AI
- How to Set Up a Data Catalog for Snowflake: A Step-by-Step Guide
- How to Set Up Snowflake Data Lineage: Step-by-Step Guide
- How to Set Up Data Governance for Snowflake: A Step-by-Step Guide
- Snowflake + AWS: A Practical Guide for Using Storage and Compute Services
- Snowflake X Azure: Practical Guide For Deployment
- Snowflake X GCP: Practical Guide For Deployment
- Snowflake + Fivetran: Data movement for the modern data platform
- Snowflake + dbt: Supercharge your transformation workloads
- Snowflake Metadata Management: Importance, Challenges, and Identifying The Right Platform
- Snowflake Data Governance: Native Features, Atlan Integration, and Best Practices
- Snowflake Data Dictionary: Documentation for Your Database
- Snowflake Data Access Control Made Easy and Scalable
- Glossary for Snowflake: Shared Understanding Across Teams
- Snowflake Data Catalog: Importance, Benefits, Native Capabilities & Evaluation Guide
- Snowflake Data Mesh: Step-by-Step Setup Guide
- Managing Metadata in Snowflake: A Comprehensive Guide
- How to Query Information Schema on Snowflake? Examples, Best Practices, and Tools
- Snowflake Summit 2023: Why Attend and What to Expect
- Snowflake Summit Sessions: 10 Must-Attend Sessions to Up Your Data Strategy
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