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Snowflake Data Quality: Management, Integration & Improvement

Updated March 20th, 2025

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Gartner estimates that poor data quality costs enterprise organizations $15 million per year. Data quality controls are a key consideration when deciding on a data storage system. As one of the most popular and quickly growing cloud data warehouses — Snowflake projects 30% growth by 2029 — many data teams are discussing Snowflake as an option.

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This article will break down Snowflake’s data quality features, show you how to manage data quality with Snowflake, and look at how integrating Snowflake with Atlan can enhance your data quality even further.

Table of contents #

  1. Snowflake’s data quality features
  2. How to check data quality in Snowflake
  3. How Atlan supports data quality
  4. Conclusion
  5. FAQs about Snowflake data quality
  6. Snowflake Data Quality: Related reads

Snowflake’s data quality features #

Snowflake is a cloud-based data warehouse founded in 2012. The platform supports analysis and simultaneous data access while handling security, storage, scaling, and query processing.

Snowflake has four main features for managing data quality:

  • Access history
  • Data metric functions
  • Object tagging
  • Snowsight, Snowflake’s web=based UI

Access history #


Snowflake’s access history tool tracks and displays who accesses which tables in your data warehouse. Each SQL statement pulling data from your warehouse is recorded as a row of the access history table.

Tracking this access history lets you verify the security and integrity of your data asset. It also provides insight into data ownership and stewardship processes as a part of data quality development.

Data Metric Functions #


Data metric functions (DMFs) are functions applied to queries that return metrics relevant to data quality. Snowflake comes with built-in DMFs covering standard quality metrics such as NULL_COUNT, DUPLICATE_COUNT, and FRESHNESS. A full list can be found in Snowflake’s data quality documentation.

You can also define custom DMFs based on your own data quality needs. Combining standard and custom DMFs lets you build a robust monitoring system to track the status and progress of your data quality development.

Object Tagging #


Object tags are Snowflake’s way of attaching metadata to data assets. Object tags specify important context for your data – for example, statuses like “PII” or “admin access only” to protect privacy and security in your data system.

A meaningful system of metadata tags improves your data quality by making data more discoverable and understandable. Snowflake’s simple object tags are a firm foundation for developing a descriptive and effective metadata system.

Snowsight #


Snowsight is Snowflake’s monitoring tool. It provides a unified interface for working with your Snowflake data by using SQL or Python. Snowsight tracks costs, security, user accounts, and more.

Tracking this high-level information lets you assess the status of your data system, giving you a starting point for developing data quality plans. As you develop your data quality strategy, you can track your system status to measure a new data quality initiative’s impact.

How to check data quality in Snowflake #

Automatic data quality checks are a key tool in maintaining data quality at scale. Snowflake’s data metric functions (DMFs) let you set up queries that provide valuable insight into your data quality.

The simplest way to set up a DMF is to use a built-in function. For example, you might want to set up a count of duplicates to be sure that you don’t have redundant records clogging up your data. Snowflake’s DUPLICATE_COUNT function lets you easily create that check:

DUPLICATE_COUNT code block -> ref

The FRESHNESS function is another useful built-in data quality tool, used for tracking data age. This function allows you to partition fresh data that is still relevant to active analyses from older, less-referenced data that can be stored in cheaper, slower discs.

FRESHNESS code block -> ref

You can also define a custom DMF to manage data quality according to your project’s specific needs. For instance, you may need an age range check on your user accounts to comply with data privacy regulations.

age range custom dmf code block -> ref

How Atlan supports data quality #

Atlan is a next-generation platform for data and AI governance. It provides a control pane that integrates all your disparate data systems, including native integration with Snowflake.

Business-first data trust #


Designed for data products, Atlan helps your organization build business-first data trust by monitoring quality for business-critical data products, ensuring they’re “fit-for-purpose.”

  • Business users receive instant trust signals through intuitive badges, scores, and lineage overlays that clearly communicate data reliability.
  • Contextualized lineage visualization shows downstream dependencies, enabling faster remediation when quality issues emerge.

Seamless cloud-Native integration #


Because it integrates seamlessly with leading cloud data warehouses, teams can use Atlan to create and execute data quality checks directly within Snowflake without requiring additional infrastructure.

  • Atlan’s native rule creation lets users create and execute data quality checks directly within Snowflake
  • Atlan can aggregate and orchestrate signals from multiple quality tools (Monte Carlo, dbt, Soda) and unify them into a single “trust center”
  • Real-time monitoring gives immediate notifications about emerging quality issues directly where the data resides

Unified control plane for operational efficiency #


Atlan establishes a unified control plane for operational efficiency. Self-service rule creation eliminates frictions that typically delay quality initiatives. By converging metadata, lineage, and quality metrics in one platform, Atlan becomes the single source of truth that reduces silos and accelerates insights.

  • Self-service rule creation ends the friction of multi-step, multi-team approvals to eliminate manual tickets and delays
  • Converging metadata, data lineage, and quality metrics in one platform reduces data silos by creating a single source of truth
  • Clear ownership within streamlined data governance workflows creates cross-org accountability for data quality remediation

How Atlan supports data quality with Snowflake #


Atlan’s Data Quality module is a business-first, cloud-native solution tailored for business-critical data products. When integrated with Snowflake it delivers real-time trust signals, automates quality checks, and consolidates all data quality and governance workflows into one control plane — enabling technical and business stakeholders alike to trust their data and make faster, smarter decisions.

  • Atlan supports and extends Snowflake’s data quality tools by integrating them with your entire data system. You can pull Snowflake object tags into Atlan’s metadata system, scaffolding them with AI-powered tag detection and automated rule enforcement.
  • Atlan can integrate your Snowflake assets, including data metric functions, with its monitoring capabilities, embedding quality information into all aspects of your operations.

By linking Snowflake with Atlan, you create a unified data quality framework across all your tools — building trust in your data no matter where it is in your system. This powerful combination creates a comprehensive ecosystem where data quality isn’t just measured but actively maintained throughout the data lifecycle.

Conclusion #

Snowflake’s data metric functions, object tags, and Snowsight monitoring tools help you develop, track, and enforce data quality standards. Building out data quality with Snowflake keeps your data system stable, secure, and active, accelerating analysis and activating data value. With Atlan support, you can integrate all the benefits of Snowflake’s data quality tooling into your larger data system and embed that data quality into your operations.

See how Atlan can support your Snowflake data quality by booking a demo today.

FAQs about Snowflake data quality #

What are the main tools in Snowflake for managing data quality? #


Snowflake provides several features to help manage data quality, including access history, data metric functions (DMFs), object tagging, and Snowsight. These tools allow teams to monitor data usage, track quality metrics like null or duplicate counts, and manage metadata to ensure data discoverability and security.

How can I set up automated data quality checks in Snowflake? #


You can use Snowflake’s built-in Data Metric Functions (DMFs) to automate quality checks. DMFs like DUPLICATE_COUNT and FRESHNESS can be used to monitor data integrity and relevance. Custom DMFs can also be created to meet specific business requirements for data quality.

How does integrating Atlan with Snowflake enhance data quality management? #


Atlan extends Snowflake’s data quality capabilities by providing a unified control plane for managing data across the entire data ecosystem. It allows you to create and execute quality checks directly within Snowflake while also aggregating signals from multiple data tools, offering real-time monitoring and comprehensive governance.

What is the purpose of Snowflake’s Object Tagging for data quality? #


Object tagging in Snowflake allows you to attach metadata to data assets, such as marking sensitive data like PII or access restrictions. This helps improve data governance and quality by making data more organized, secure, and easier to find, ensuring it meets compliance and business requirements.


Snowflake’s Snowsight interface helps you track key metrics related to data quality, including costs, security, and access. By monitoring these indicators over time, you can assess the impact of your data quality initiatives and quickly identify areas where improvements are needed.

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