The constant increase in the data produced by modern technologies has given rise to significant challenges, such as data complexity, inconsistencies, and breaching issues. You need a structured approach to address these challenges and mitigate the risk of comprising sensitive data. 

  • Data governance is a robust function that assists in managing critical data. It includes procedures, policies, and standards that enhance data quality and integrity, ensuring you comply with important standards.
  • Although traditional data governance practices provide a foundation for managing data, you must keep up with the dynamic needs of the data environment. 
  • Snowflake’s data governance offers a comprehensive approach that surpasses traditional governance by providing scalability, automation, integrated security, and unified data management. It helps you maintain high standards and compliance with relevant regulations.
  • In this article, you will explore how Snowflake data governance will help you to accommodate growing volumes of data without compromising performance. 

What is Data Governance in Snowflake? 

While Snowflake enables you to handle increasing volumes of data, you must implement a robust data governance procedure to ensure data compliance. An adequate data governance approach helps ensure business data quality, integrity, security, and availability. 

Data governance structure provides industry-leading features that enable the highest level of governance for your organizational data stored in the Snowflake warehouse.

With data governance in Snowflake, you can do the following: 

  1. Monitor your data
  2. Ensure data consistency
  3. Reduce the risk of a data breach
  4. Comply with regulatory requirements like GDPR or HIPAA
  5. Create policies for data retention and deletion
  6. Improve trust and confidence in your data for users, and much more for better data management.

Data governance in Snowflake not only addresses the key areas that need attention but also drives business growth by maximizing the value of data and making it secure.

Key Features of Snowflake Data Governance

There is a comprehensive set of data governance Snowflake features that help you effectively manage your data. The features include the following:

Data Metrics Function 

  • Snowflake data governance allows you to monitor the integrity and state of your data using data metrics functions (DMFs)
  • By applying DMFs, you can study your data by measuring key metrics such as freshness, duplicate or unique values, and NULLs. Although Snowflake provides built-in DMF functions, you can customize them to measure data quality more precisely. 

Column-Level Security 

The column-level security feature is supported in Snowflake Enterprise Edition or higher. This feature allows you to apply a masking policy to the column’s data in the Snowflake table. Currently, Snowflake’s column-level security has two features:

  • Dynamic Data Masking: You can use dynamic data masking to selectively mask data in tables and view columns when querying. It helps you ensure that only authorized users can view the actual data.
  • External Tokenization: Data tokenization is replacing sensitive data with an undecipherable token. By implementing external tokenization, you can tokenize data before loading it into a Snowflake table and detokenize it at query runtime.

Row-Level Security 

  • You can use Snowflake’s row-level security feature through row access policies, which are schema-level objects, to limit the access of particular rows in a table.
  • The policies help you determine if you can view a row using statements such as SELECT, UPDATE, DELETE, or MERGE. Row-level security enables data governance by limiting access to sensitive data.

Object Tagging 

  • Snowflake supports this feature in the Enterprise edition or higher. Snowflake’s object tagging feature allows you to track sensitive information for compliance, protection, discovery, and resource usage.
  • It supports a centralized and decentralized data management approach, facilitating compliance with external and internal regulatory requirements.
  • Object tags are labels that let you assign metadata to objects in Snowflake, such as tables, views, or schema.
  • You can create a tag using the CREATE TAG statement and assign a single tag to multiple columns. The Snowflake tag consists of key-value pairs. The value of it should be unique and string value.

Data Classification

  • Data classification is a multiple-step process associated with defined system tags for columns. It analyzes fields and metadata.
  • You can classify columns in a Snowflake table to determine if they contain data that needs to be protected or tracked. With classification, you can improve your data governance structure as it facilitates compliance with data privacy regulations.

Object Dependencies

  • Object dependency allows you to operate on an object. The object should have a reference to metadata for itself or some other object while working on it.
  • You can create a view for an account when data sharing is performed. This feature is supported on your local Snowflake account and helps you enhance data integrity and compliance.

How to Implement Data Governance in Snowflake

Follow the steps below to implement data governance in Snowflake. Please note that you need account_admin permissions to access the data governance interface.

  • Step 1: Open the data governance interface to the Snowflake account.  
  • Step 2: You can view the number of objects with tags and policies illustrated on the governance interface.
Snowflake Data Governance Interface
Snowflake Data Governance Interface
  • Step 3: You can analyze data governance for any object by accessing and implementing customized filters. 
Snowflake Data Governance: Object Customization
Object Customization
  • Step 4: Select a particular object from the tagged objects page to apply tags or data governance policies to data. Click on +Tag or +Policy to assign tags or policies. 
Snowflake Data Governance: Applying Tags and Policies to Objects
Applying Tags and Policies to Objects

Tools Used to Implement Effective Data Governance in Snowflake

Various tools help you implement data governance in Snowflake. Let’s look at some of the prominent governance tools: 


  • Alation is a data governance tool that helps you identify critical datasets, understand and manage them, and catalog them for validation and repurpose.
  • It is an ideal solution to implement Snowflake data governance, as it helps you centralize data, adhere to data access and usage policies, and foster collaboration.


  • Collibra is an enterprise-oriented data governance tool that helps businesses understand and manage data assets.
  • It aims to protect data and ensure regulatory compliance by creating an inventory of data and capturing related metadata. You can integrate Collibra with Snowflake to handle and scale your data assets while ensuring governance.


  • Talend is a data governance tool that provides data management using intelligent technologies like data cataloging, data lineage, pattern recognition, and machine learning.
  • Combining Talend with Snowflake allows you to implement suitable security measures for data ownership, remediation, curation, and reuse, ensuring data governance is met.


  • Altan is a governance tool that helps you transform complex data governance processes into a simple, more understandable approach.
  • You can scale your data management using Atlan to implement data governance in Snowflake. It lets you implement crawling and cataloging your data asset in Snowflake, driving self-service data discovery.
  • Atlan also protects sensitive data by auto-classifying PII data such as email, phone number, card information, and more.

Challenges In Implementing Snowflake Data Governance

Even though Snowflake offers a solid foundation for data governance, it faces some challenges when implementing governance policies.

  • Data Lineage: Snowflake stores metadata information at the object level. This storage method makes it difficult to understand data flow and implement consistent data governance policies on dependent objects.
  • Data Discovery: The two essential components of data governance are availability and usability. While Snowflake is widely used for data management, it does natively offer data cataloging and business glossary, which makes it challenging to find and use data.
  • Multiple Data Sources: As Snowflake accommodates data from various sources, it becomes difficult to ensure proper access controls. You need to filter data to manage and adhere to data governance regulations.

The above challenges can make implementing a robust data governance framework in Snowflake difficult. However, you can address these limitations using third-party tools, such as Hevo, which will help you build a foundation for data governance in Snowflake.

How Hevo Helps to Overcome Challenges Associated with Implementing Snowflake Data Governance Framework

Hevo is a real-time ELT data integration platform that syncs data from multiple sources. It helps streamline data workflows and implement a robust data governance framework in Snowflake. 

Hevo also has various built-in features that help you improve data governance practices for your Snowflake warehouse. Let’s see how:

  • Data Transformation: It is essential to filter data for inconsistencies before implementing data governance policies to ensure consistency. Hevo offers two ways to transform your data: Python-based and drag-drop. These transformations assist you in handling data from various sources and modifying the properties and metadata of events. Once loaded in Snowflake, they help you with easy data discovery so you can implement consistent governance policies by identifying dependent data.
  • Incremental Data Loading: With Hevo’s incremental data loading feature, you can keep track of the changes made to the data. By default, Hevo retains the primary key constraints from the source data in the destination while loading data. It helps identify the relationship between data and apply data governance procedures accordingly once transferred to Snowflake.
  • Automated Schema Mapping: Understanding your data format and structure is essential for applying governance policies and maintaining compliance standards. Hevo’s automated schema mapping automatically reads and replicates source data’s schema into the Snowflake warehouse. You can select the fields you want to map. For instance, you can leave sensitive data unmapped or limit the character limit to create shorter, meaningful field names for better understanding.

Hevo makes it easy to set up the data pipeline and handle your data, and its well-integrated built-in features help you enhance analytics tasks. 

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What are Some of the Best Practices for Implementing Data Governance in Snowflake?

Implementing a sturdy data governance framework in Snowflake requires several best practices. Here are some of them: 

Develop a Governance Framework 

You should develop a robust data governance framework to govern data in Snowlfkae in a structured manner. The framework must include: 

  • Essential guidelines to design scalable data models in Snowflake.
  • Strategies for proper data processing and storage to optimize resource management.
  • Alignment with industry standards specific to cloud computing for data security and to perform sound integration.

Set up a Data Governance Team

Setting up a data governance team or committee is essential to help you develop and implement rules and policies related to your data. Each member’s responsibility should be clearly defined to ensure the governance program aligns with your organization’s data needs. For instance: 

  • Your organization’s chief data officer should oversee the Snowflake data governance framework and ensure its alignment with business needs. 
  • Your team’s data governance lead should develop and implement policies and procedures and ensure coordination among the organization’s teams. 
  • The data security officer in the organization is responsible for implementing security and monitoring policies to ensure compliance with regulatory standards.

Define Security Measures and Data Quality Standards

Establishing security measures to protect your data from unauthorized access is essential. These measures must include access controls, data masking, encryption, and more. By implementing security policies, you can decrease incident response time. You must also implement data quality standards for validation, cleaning, and enrichment. These standards enhance your business data quality and make data more reliable and consistent.


Q. Which data governance features are available in the standard version of Snowflake? 

The data governance features that are available in the standard version include: 

  • Access control
  • Data masking 
  • Object Tagging 
  • Data Sharing 

To practice a higher level of data computation, you need to buy the enterprise edition version, which is costlier than the standard version.


Various Snowflake governance features allow you to manage data and implement policies effectively. Judiciously using these built-in data governance features, you can classify your datasets and ensure their quality and integrity. 

Integrating your data with Hevo improves data consistency. The platform’s flexible, no-code data pipeline allows you to transform and migrate data. Implementing these strategies within Snowflake can help you comply with essential regulatory compliances to derive business value.

Want to take Hevo for a spin? Sign Up or a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also checkout our unbeatable pricing to choose the best plan for your organization.

Chirag Agarwal
Principal CX Engineer, Hevo Data

A seasoned pioneer support engineer with more than 7 years of experience, Chirag has crafted core CX components in Hevo. Proficient in lean solutions, mentoring, and tech exploration.

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