Data Quality in Snowflake:
Best Practices for 2025

Ensuring data quality in Snowflake is critical for organizations that rely on data-driven decision-making. As Snowflake continues to dominate the cloud data warehouse landscape, understanding and leveraging its native data quality features is essential for maintaining trustworthy, accurate, and actionable data.

Why Data Quality Matters in Snowflake

• Poor data quality can cost enterprises millions annually and erode trust in analytics.

• With Snowflake’s ease of data sharing and integration, low-quality data can quickly propagate across business units, amplifying risks and inefficiencies.

• High-quality data is foundational for accurate reporting, compliance, and effective business operations.

Key Native Features for Data Quality in Snowflake

Snowflake offers several built-in tools and features to help data teams monitor, manage, and improve data quality checks in Snowflake:

Access History

• Every SQL query is logged, enabling teams to trace data usage, detect unauthorized access, and support compliance efforts.

• Facilitates data stewardship by providing visibility into data ownership and usage patterns.

Data Metric Functions (DMFs)

• Built-in DMFs like NULL_COUNT, DUPLICATE_COUNT, and FRESHNESS allow continuous, automated monitoring of data quality.

• Custom DMFs can be defined to enforce business-specific quality checks, such as age ranges or value constraints.

• Results can be stored in event tables for alerting, dashboarding, and trend analysis.

Object Tagging

• Enables tagging of tables, columns, or schemas with key-value pairs (e.g., “PII: true”), supporting compliance and targeted data protection.

• Facilitates dynamic data masking and row-level security policies for sensitive data.

Snowsight

• Provides visualizations of metadata, including data distributions, completeness, and anomalies.

• Allows filtering and exploration of data quality metrics, making it easier to identify and address issues.

Strategies for Ensuring Data Quality in Snowflake

Achieving and maintaining high data quality in Snowflake requires a multi-faceted approach encompassing various stages of the data lifecycle:

1. Data Profiling and Discovery:

Before loading data into Snowflake, it's essential to understand its characteristics, identify potential anomalies, and assess its quality. Snowflake partners offer powerful data profiling tools that can analyze source data and provide insights into its structure, content, and quality issues. This proactive step helps in designing appropriate data cleansing and transformation rules. For example, profiling a CSV file before loading it into Snowflake might reveal inconsistent date formats or unexpected null values.

2. Data Validation and Transformation During ELT:

Leveraging Snowflake's powerful SQL capabilities and extensibility through Snowpark (allowing the use of Python, Scala, and Java), you can implement robust data validation and transformation logic within your ELT pipelines. This includes:

• Schema Enforcement: Defining strict schemas for your Snowflake tables ensures that incoming data adheres to the expected structure and data types.

• Data Type Conversions: Standardizing data types (e.g., ensuring all date fields are in a consistent format).

• Data Cleansing: Handling missing values (imputation or filtering), correcting inconsistencies (e.g., standardizing abbreviations), and removing erroneous data.

• Business Rule Validation: Implementing checks to ensure data conforms to specific business rules (e.g., ensuring order quantities are always positive).

• Data Deduplication: Identifying and merging or removing duplicate records based on defined criteria.

Example using SQL in Snowflake for data validation:

-- Check for null values in a critical column
SELECT COUNT(*) FROM orders WHERE customer_id IS NULL;
-- Identify inconsistent date formats
SELECT DISTINCT order_date FROM orders WHERE TRY_TO_DATE(order_date) IS NULL;
-- Check for order quantities less than or equal to zero
SELECT COUNT(*) FROM order_items WHERE quantity <= 0;

3. Data Governance and Stewardship:

Establishing clear data governance policies and assigning data ownership and stewardship are crucial for maintaining data quality over time. This involves defining roles and responsibilities for data creation, maintenance, and quality monitoring. Data stewards play a vital role in defining data quality rules, resolving data issues, and ensuring adherence to data governance policies within the Snowflake environment.

4. Data Quality Monitoring and Alerting:

Continuously monitoring data quality metrics within Snowflake is essential for detecting and addressing issues promptly. This can involve:

• Implementing automated data quality checks: Regularly running SQL queries or using data quality tools to validate data against defined rules.

• Creating dashboards and alerts: Visualizing key data quality metrics and setting up alerts for when data quality thresholds are breached.

• Leveraging Snowflake's features like data lineage: Understanding the flow of data and identifying the source of data quality issues.

Snowflake's capabilities for real-time data loading and processing necessitate equally agile data quality monitoring strategies.

5. Leveraging Snowflake Partner Ecosystem:

Snowflake has a rich ecosystem of partners offering specialized data quality tools and solutions that integrate seamlessly with the platform. These tools often provide advanced features for data profiling, cleansing, validation, monitoring, and data governance, complementing Snowflake's native capabilities. Exploring these partner solutions can significantly enhance your data quality efforts.

Integrating Data Quality Tools with Snowflake

Snowflake's open ecosystem allows seamless integration with modern data quality and observability tools:

1. Monte Carlo Data

• Connects directly with Snowflake.
  
  

• Offers anomaly detection, freshness monitoring, and lineage tracing.
  
  

• Detects schema changes, failed pipelines, and null spikes.
  
  

2. Soda

• Rule-based data testing via YAML or SQL.
  
  

• Integrates with Snowflake queries to validate datasets and trigger alerts.
  
  

• Can be deployed in CI/CD workflows.
  
  

3. Great Expectations

• Test data against expectations during ingestion or transformation.
  
  

• Can be orchestrated with Airflow + Snowflake pipelines.
  
  

• Useful for schema validation and profiling.
  
  

These integrations make Snowflake part of a broader data reliability stack, essential for scaling data quality in complex environments.

Best Practices for Data Quality Management in Snowflake

• Automate Data Quality Checks: Use DMFs to schedule regular audits for nulls, duplicates, and data freshness across all critical tables.

• Implement Metadata Tagging: Consistently tag sensitive or regulated data to streamline governance, security, and compliance workflows.

• Monitor Data Access: Regularly review access history to detect unusual patterns and ensure only authorized users interact with sensitive data.

• Leverage Data Catalogs and Observability Tools: Integrate Snowflake with data catalogs and observability platforms for enhanced profiling, lineage tracking, and real-time monitoring.

• Establish Governance Protocols: Define clear data ownership, stewardship, and escalation paths for resolving data quality issues.

Addressing Common Data Quality Challenges

• Schema Drift: Monitor for schema changes that can break downstream pipelines. Use DMFs and access history to detect and respond to unexpected modifications.

• Data Staleness: Track data freshness to ensure that analytics and reports are based on up-to-date information.

• Sensitive Data Exposure: Use object tagging and dynamic masking to protect PII and comply with regulations.

• Scaling Quality Checks: Automate as much as possible, leveraging both built-in Snowflake features and external observability tools for end-to-end coverage.

Conclusion

Snowflake provides a robust set of native features—access history, data metric functions, object tagging, and Snowsight—to help organizations maintain high data quality at scale. By combining these capabilities with strong governance and automated monitoring, data teams can ensure their analytics are built on a foundation of reliable, accurate, and secure data.

Investing in data quality within Snowflake not only safeguards compliance and trust but also empowers your business to make smarter, faster decisions in today’s data-driven world.

FAQs

Q: Does Snowflake have a data quality tool?

Yes, Snowflake provides native data quality tools and features, including Data Metric Functions (DMFs) for automated quality checks, Access History for auditing, Object Tagging for metadata management, and Snowsight for visual monitoring and analytics.

Q: What are the 5 elements of data quality?

The five commonly recognized elements of data quality are:

• Accuracy

• Completeness

• Consistency

• Validity

• Uniqueness

Q: What are the best practices for Snowflake data quality?

Best practices for Snowflake data quality include:

• Automating regular data quality checks using DMFs for metrics like nulls, duplicates, and data freshness.

• Using Object Tagging to classify and secure sensitive data for compliance and governance.

• Leveraging Snowsight for visual exploration and monitoring of data quality metrics.

• Auditing data access with Access History to ensure data integrity and authorized usage.

• Integrating with external observability or data catalog tools for end-to-end quality management.

Q: What are the 7 aspects of data quality?

The seven aspects to keep in mind for building data quality framework in Snowflake are:

• Accuracy

• Completeness

• Consistency

• Validity

• Uniqueness

• Timeliness (Freshness)

• Integrity