Snowflake has changed data warehousing by offering a scalable, secure, and cloud-based platform for organizations to store and analyze massive datasets. Its ease of use and performance capabilities have made it a go-to solution for data-driven businesses across industries. However, just like any powerful tool, the success of projects built on Snowflake relies heavily on the quality of the data itself.

Data quality refers to the accuracy, completeness, consistency, relevancy, and timeliness of data. In simpler terms, it means having the right data, in the right format, at the right time. Even in a modern data warehouse like Snowflake, data quality issues can creep in due to various reasons. These issues, if left unchecked, can lead to inaccurate insights, flawed decision-making, and ultimately, lost revenue and diminished business value.

Data quality issues arise from various causes, including schema changes, failed API calls, and manual data retrievals, leading to errors and duplicate data. Migrations to the cloud and a lack of unified data lifecycle views create further challenges. The sheer scale of data collection, growing at 63% ¹ per month, and the vast number of data sources complicate data management. Numerous data tools across the modern data stack, often unintegrated, contribute to fragmented and unreliable data environments. Effective data governance is crucial to prevent an overabundance of expensive data silos and maintain data quality.

This blog post dives into the critical role of data quality within the Snowflake ecosystem. We’ll explore the common challenges organizations face in maintaining clean data, and how DQLabs can power up your Snowflake environment. By integrating DQLabs with Snowflake, you can gain a comprehensive view of your data health, automate data quality processes, and ensure your analytics are built upon a solid foundation of trustworthy information.

Understanding Data Quality in Snowflake

Before diving into how DQLabs tackles data quality issues in Snowflake, let’s establish a clear understanding of what data quality entails. In essence, data quality refers to the overall health of your data, encompassing six key dimensions.

• Accuracy: Does your data accurately reflect the real world it represents? Are there errors in data entry, transcription, or calculations?
• Completeness: Is all the necessary data present? Are there missing values or gaps that could skew analysis?
• Consistency: Does your data follow consistent formatting and definitions across different data sources and tables? Inconsistencies can lead to confusion and inaccurate comparisons.
• Validity: Is the data relevant to the specific tasks and decisions you need to make? Does it meet the specifications of your business questions?
• Uniqueness: Are there any duplicate entries or data records? This ensures each data record appears only once in your dataset. It prevents duplicates that skew analysis and compromise data integrity.
• Timeliness: Is the data up-to-date and reflects the current state of affairs? Outdated data can lead to missed opportunities and ineffective decision-making.

These principles are important within the Snowflake ecosystem as well. While Snowflake gives you a robust data platform, it can’t guarantee the inherent quality of the data you ingest. Here’s why data quality is even more critical in Snowflake:

• Insight Accuracy: Snowflake’s processing power gets you powerful analytics, but these insights are only as reliable as the data feeding them. Inaccurate data will lead to misleading results, hindering your ability to make sound decisions.
• The Domino Effect: Data errors in the early stages of the data pipeline can ripple through subsequent analysis, leading to cascading issues, downtime and potentially significant financial losses.
• Data Governance Gap: Without proper data quality measures, there’s a risk of inconsistencies creeping in, making it difficult to manage and govern your data effectively within Snowflake.

Let’s now delve deeper into the common data quality challenges that can plague your Snowflake environment:

• Data Ingestion Errors: During data loading processes, duplicates, missing values, or formatting inconsistencies can occur.
• Schema Changes: Schema modifications can disrupt established data pipelines, causing breaks and inconsistencies in data flow.
• Missing or Incomplete Data Points: Incomplete data sets can hinder analysis and limit your ability to draw comprehensive conclusions.
• Data Lineage Issues: The lack of clear data lineage can make it difficult to pinpoint the source of data quality problems or understand the impact of changes in the data pipeline.
• Lack of Data Governance: Without clear data ownership, access controls, and standardization practices, data quality issues can easily proliferate within Snowflake. Also, storing PII data with no governance puts you at security risk. Data breaches are becoming more expensive. IBM’s 2023 Cost of Data Breach Report reveals a staggering increase, with the average cost reaching $4.45 million.
• Storage Inefficiencies: Without adequate data quality, your storage can hold excess single-use data that users are experimenting with to build models and visualizations.

Limitations of Snowflake

For Data Quality 

While Snowflake provides a range of built-in features designed to classify, secure, and validate data, these features often require manual intervention and a significant amount of SQL-like coding. Snowflake’s functionalities like object tagging, data masking, access management, and column/row-level security, primarily target data governance within the predefined warehouse structure. This approach excels at managing data designed for specific warehousing purposes. 

However, it may not fully address the needs of agile data exploration and rapid prototyping often employed by data science teams. The unknown variables introduced by agile teams for rapid data analysis and prototyping, or mistakes in data integration processes, might slip through these manual checks.

Snowflake allows creating data quality (DQ) rules as SQL procedures. However, this approach can be a lot of effort for collaboration, maintenance, and presenting results in a user-friendly format. Additionally, it requires significant technical expertise, essentially replicating the challenges of manual data quality management. This highlights the need for a more comprehensive, automated approach to data quality that complements Snowflake’s strengths and caters to the dynamic nature of modern data exploration.

For Data Observability

Ensuring data quality in Snowflake is challenging despite the platform’s popularity. Snowflake’s primary management interface, SnowSight, offers limited visibility and control, lacking real-time alerts or detailed status reports. This is frustrating for data engineers accustomed to continuous oversight. 

Most users rely on ETL scripts for data validation and cleansing, along with SQL-based reporting tools like Tableau, Looker, and Microsoft Power BI. However, these tools, designed for executive reporting, are not suited for real-time observability or day-to-day management. They lack pre-built templates, scorecards, and precise controls essential for monitoring and optimizing data quality.

Snowflake’s automated data partitioning and indexing simplify operations but can inadvertently cause data quality issues. Migrating data from traditional databases to Snowflake requires transformation, risking schema errors. Even minor issues, like SQL case sensitivity, can break applications and pipelines without being flagged by Snowflake or noticed by engineers.

Conventional MDM and data governance tools that depend on Snowflake’s metrics aren’t capable of detecting nuanced data quality issues such as SQL syntax errors. Snowflake Data Profiler provides only high-level overviews without performing detailed checks, while reporting tools like Looker can only test for data quality at single points in time, such as during data ingestion. This lack of continuous validation means data errors that occur later often go unnoticed.

In summary, Snowflake’s design and existing tools do not provide the comprehensive real-time monitoring and control required for maintaining data quality, posing significant challenges for users aiming to ensure their data remains accurate and reliable.

How DQLabs Enables High-Quality Data in Your Snowflake Ecosystem

Data quality is essential, but achieving and maintaining it can be a complex task. Here’s where DQLabs steps in. DQLabs seamlessly integrates with Snowflake, offering a comprehensive suite of tools and functionalities specifically designed to enhance data quality within your cloud data warehouse.

Key Features of DQLabs for Improved Data Quality & Observability

Data Profiling and Assessment: DQLabs does more than just basic data volume checks.

• • Deep Profiling: DQLabs profiles all your data, capturing their structure, metadata, relationships, dependencies, and lineage. This comprehensive context is essential for creating effective data quality rules. 
  • Out-of-the-Box Data Quality Checks: DQLabs offers over 50+ out-of-the box data quality checks and it automates in-depth data profiling in Snowflake, identifying issues for all five key dimensions as mentioned earlier.

Data Cleansing & Standardization: Dirty data doesn’t stand a chance with DQLabs. DQLabs empowers you to:

• Identify Errors & Duplicates: Identify errors like typos, invalid formats, or duplicate entries. DQLabs also identifies redundant records to ensure accurate representation.

• Standardize Data: Ensure consistent data formats throughout your Snowflake environment with the help of semantics.

Discover how DQLabs can help you maximize your Snowflake investment by providing insights into performance, quality, cost, and more.

Data Governance Framework: DQLabs helps establish a robust data governance framework within Snowflake by enabling you to:

• Define Data Ownership: Clearly assign ownership and accountability for different data sets.
• Implement Access Controls: Define user permissions and restrict access to sensitive data using data masking.
• Enforce Data Quality Standards: Set clear rules and thresholds to prevent data quality deviations.

Continuous Data Monitoring: DQLabs keeps a watchful eye on your data. It provides:

• Automated Dashboards: Visualize key data quality metrics in real-time.
• Alerts and Notifications: Receive instant notifications when data quality issues arise. With the AI & ML enabled anomaly detection process, DQLabs automates the process of data quality issues tracking. Based on the deviation from the standard data, DQLabs set the priority of the alerts (high, medium, and low).
• Easy Issue Remediation: Receive instant notifications about data issues directly in your preferred collaboration tools like Slack, Teams, and email, complete with relevant details for root cause analysis to minimize downtime.

Data Lineage Tracking: DQLabs tracks your data’s journey. By tracking data lineage, you can:

• Data Drift Identification: DQLabs tracks schema changes (adding/removing columns, renaming) across all data sources and alerts you. It also offers versioning to audit changes and recommends actions to address schema drift (transformations, reprofiling, data checks, workflow adjustments).
• Maintain Data Reliability: Setting baselines and initial data quality rules isn’t enough. DQLabs continuously monitors your Snowflake data pipeline throughout its lifecycle, offering multiple levels of breakdown from nodes to jobs to pipelines to tasks to runs.
• Unified Data View: DQLabs offers a single, unified view of your entire data pipeline, ensuring data quality from source to target. Comprehensive metadata coverage ensures that data lineage from upstream to downstream includes detailed information about all transformations applied to the data.

Machine Learning-Powered Data Quality: DQLabs leverages the power of Machine Learning (ML) to take data quality to the next level:

• Anomaly Detection: ML algorithms automatically identify unusual data patterns, potentially indicating errors.
• Semantic Understanding: DQLabs analyzes your data attributes, identifying or linking semantic types based on existing information. This provides valuable context for rule creation.
• Rule Recommendation Engine: Leveraging the semantic understanding and metadata, DQLabs suggests relevant data quality rules for each attribute.
• Effortless Approval: DQLabs employs AI/ML, NLP, and LLM techniques to assist with rule evaluation. You’ll receive trust levels and recommendations, allowing for faster and more informed decision-making when approving rules.
• Automated Rule Application: Once approved, DQLabs automatically applies the data quality rules to the corresponding attributes.
• Flexible Execution: Choose between batch or real-time execution of these rules, based on your organization’s specific needs.

DQLabs and Snowflake Works Better Together

By integrating DQLabs with Snowflake, you get a powerful combination for data-driven success:

• Improved Data Quality: Experience cleaner, more reliable data for trustworthy analytics.
• Reduced Errors and Biases: Make informed decisions based on accurate insights.
• Enhanced Data Governance: Ensure data compliance and maintain data integrity.
• Increased Operational Efficiency: Automate data quality processes for faster results.
• Faster Time-to-Insight: Spend less time cleaning data and more time extracting valuable insights.

Companies recognize that even with Snowflake, it’s crucial to invest time and effort into establishing a continuous data quality cycle to test, validate, and rectify data errors as they arise.

The ideal technical partner for automating this continuous data quality cycle is a modern data quality platform like DQLabs. It offers a unified view of your data throughout its journey within your systems.

Experience Augmented Data Quality and Data Observability firsthand. Schedule a personalized demo today.

Source:¹ https://www.datanami.com/2020/02/17/how-is-data-growth-affecting-real-world-enterprises-4-key-findings/