• Data quality refers to your dataset’s dependability, precision, consistency, and completeness. High-quality data does not contain errors, discrepancies, or inefficiencies, making it appropriate for accurate analysis and decision-making.
  • If your organization stores large volumes of data in a data warehouse, you must ensure that the data quality meets the requirements to support business operations. 
  • Snowflake is a leading cloud data warehouse that allows you to store and process massive volumes of data.
  • High Snowflake data quality is necessary to guarantee the reliability of conclusions and decisions. To preserve and enhance data integrity, you must carry out data profiling, cleansing, validation, and monitoring processes.

This article will explore Snowflake’s features and framework for effective data quality management.

What Is Data Quality in Snowflake? 

Snowflake data quality helps you monitor the condition and reliability of your data through data metric functions (DMFs). Snowflake DMFs can measure key metrics, including data freshness and counts that measure duplicates, NULLs, rows, and unique data.

For measuring common Snowflake data quality metrics without defining them, Snowflake offers built-in system DMFs in the SNOWFLAKE.CORE schema. To further accurately adjust for your Snowflake data quality evaluations, you can also define your custom DMFs. These are saved in the database and schema of your choice.

After you assign a DMF to a table or view, Snowflake stores the result of calling the DMF in the event table associated with your account. It does this regardless of whether you use system DMFs, custom DMFs, or both. You can schedule the frequency of calling the DMF per your requirements.

For instance, the DMFs on a certain table can be scheduled to run three times a day. You can even adjust the DMF schedule to meet your internal data quality standards. Every DMF that is placed on the table follows the same sequence.

Once the DMFs are scheduled to run, you can set up alerts to get notifications whenever the data quality changes. Integrating the DMF and alert functionality allows you to have regular notifications for data quality on the tables you measure.

Data Quality Testing for Snowflake Using Data Metric Functions

Data Metric Functions are used in Snowflake data quality monitoring to regularly track data quality parameters like validity, uniqueness, accuracy, and completeness. You can utilize Snowflake’s system data metric functions for typical metrics like row count, duplicates, and freshness. Alternatively, you can specify metrics unique to your data by building custom data metric functions.

You can use the data metric functions to perform continuous data quality checks in Snowflake tables. You may also utilize them in a query to assess the data quality in your pipeline. Schedule-based continuous monitoring is based on regular measuring, and trigger-based continuous monitoring is based on measuring only when the underlying table is changed.

To preserve the privacy of your data, the data metric function findings are kept organized in a consolidated event table within your Snowflake account. You may set up alerts from the event table, construct dashboards, and query metric outcomes.

Data Quality Snowflake Features

Here are some of the key features of data quality in Snowflake:

Data Quality Queries

You may implement data testing within your processes to determine specific quality issues. Simple data testing methods, such as schema or custom tests, allow you to validate your data hypotheses and ensure your code is operating correctly. It also prevents regressions when your code is modified. You can turn to applications like dbt and Great Expectations to test data in Snowflake.


  • Data quality incorporates generating value for the business at large. You can utilize Snowsight, the platform’s visual, metadata-driven analytics interface, to find data swiftly for Snowflake assets.
  • Snowsight gives your team access to metadata information such as filled/empty meters, frequency distributions, key distributions, and histograms for each query result.
  • You can explore these metadata-based contextual statistics further by limiting them to particular subsets of data or by using contextual filters inside the user interface.

Access History Feature

Data audits are essential for guaranteeing the integrity of your data. These audits also ensure safe storage, authorized access, and proper usage of your data to comply with all applicable laws and regulations.

Through constant evaluation and documentating the state of your data within Snowflake, you can identify data quality issues. These issues can be related to expired data, data that deviates from stated distribution ranges, incomplete tables, and schema modifications. With Snowflake’s Access History feature, you may discover vital information about which tables are used, by whom, and how often.

Data Quality Framework in Snowflake Data Pipeline

A data quality check framework is created for the Snowflake data pipeline to satisfy the basic data quality requirements without needing a third-party solution. This framework is expandable, allowing you to add additional validation criteria as your project progresses.

The framework enables you to effectively manage vast amounts and varieties of data by utilizing Snowflake’s parallel processing capabilities and scalability. It enables seamless integration with the data pipeline, allowing for different phases of data quality assessments.

You can design this framework as an independent component that can be quickly integrated into a data pipeline without interfering with existing data mappings. Here are the steps you can follow:

  1. Ingestion of data to landing from various sources.
  2. Snowflake data quality checks and evaluations for landing data.
  3. Cleansing data load from landing to staging.

Let’s look through some best practices for maintaining the data quality framework in Snowflake.

  • A data quality framework allows you to record the results of your organization’s data quality guidelines. You can later use these results for analysis or reporting.
  • Before you load the data, the data quality framework performs extensive checks, such as table existence and structural checks.
  • Be sure to conduct data quality tests on a column or group of columns from a table, such as uniqueness and completeness checks.
  • Execute a business rule check, which may require information from several tables and columns.
  • Allows the load to be stopped/resumed if the data quality check fails.
  • The framework can offer you large and adjustable metadata for easily adding or altering data quality rules.
  • Keep track of anomalous records in an alternate table that doesn’t follow the data quality rule.
  • The ability to incorporate the framework at any pipeline stage for data loading.

Streamlining Data Quality in Snowflake with Hevo

Ensuring Snowflake data quality is necessary for strategies and decisions and operational efficiency. However, to perform quality checks on the dataset, you must consolidate your data from all sources into the Snowflake data warehouse. This can be easily achieved by using Hevo Data.

Hevo is a real-time, no-code ELT data pipeline platform that automates your data integration process cost-effectively and delivers flexible solutions to adjust to your needs. You can also perform various transformations to your data using Hevo, allowing a streamlined process while further handling the data quality checks.

Here are some of Hevo’s key features:

  • Data Transformation: To prepare your data for analysis, Hevo Data offers intuitive drag-and-drop and Python-based data transformation features. These features can reduce the processing resources Snowflake requires for data transformation.
  • Incremental data Loading: Hevo’s incremental data loading facilitates the process of loading recently updated data into the required destination. You can reduce the amount of storage and computational resource usage by eliminating unnecessary inquiries and data storage.
  • Automated Schema Mapping: Hevo automates the complexity of schema mapping by recognizing the schema of incoming data and replicating it in the destination. Per your business requirements, you can choose between full and incremental mappings.


In this article, you have obtained a complete overview of what data quality is and why it is important in Snowflake. Maintaining high Snowflake data quality is important for leveraging its powerful data warehousing capabilities.

With its robust architecture and features like data metric functions (DMFs), Snowflake provides optimized solutions for managing data quality to optimize your workflows.


Q1. What are the data quality checks that can be performed on Snowflake tables?

  • Multiple data quality checks are performed in Snowflake tables, such as access history, data quality queries, and object tagging.

Q2. How does Snowflake differ from traditional data warehousing solutions? 

  • Snowflake is a cloud-based data warehousing platform that provides you with several features and capabilities for data storage, processing, and analysis. Unlike traditional on-premises data warehousing solutions, Snowflake has a shared data architecture with an emphasis on scalability, data sharing, and performance optimization. Due to its pay-per-use pricing structure and ability to handle semi-structured data, it is a popular choice for current data warehousing and analytics requirements.
Senior Customer Experience Engineer

Veeresh specializes in JDBC, REST API, Linux, and Shell Scripting. He excels in resolving complex issues, conducting brainstorming sessions, and implementing Python transformations, contributing significantly to Hevo's success.

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