Improve Snowflake Data Quality With Robust Validation, Observability, and Ingestion

How Can You Improve Snowflake Data Quality With Advanced Solutions?

Do you know how to get optimal use from Snowflake? Snowflake is a data ingestion and warehousing solution used by more than 7,000 companies worldwide. It makes it easy to ingest, retrieve, and analyze data from multiple sources, but it doesn’t guarantee data quality. 

To optimize results from Snowflake, you need to employ a third-party solution for data quality, validation, and observability. Read on to learn why and how to choose the best data monitoring tools. 

Quick Takeaways

• Snowflake is a popular cloud data warehousing solution 
• Snowflake makes it easy to ingest data from multiple sources but doesn’t guarantee high-quality data
• To ensure that Snowflake data is reliable and useful, companies need to employ a robust third-party data quality, validation, and observability solution, such as FirstEigen’s DataBuck

What is Snowflake?

Snowflake is a popular cloud data warehousing solution that lets organizations store and analyze data records from various sources in a single place. Enterprises use Snowflake for data storage, data science, data application development, and data analysis. It even lets users create multiple virtual warehouses, enabling different teams to work on the same data without interfering with each other.

Being cloud-based, Snowflake can easily scale to increase performance and capacity on demand. It includes three primary components:

• Cloud services, including authentication, infrastructure management, and user access control
• Query processing via virtual cloud data warehouses
• Database storage for both structured and unstructured data

Users can extend Snowflake functionality by integrating with numerous third-party tools and applications. The Snowflake Marketplace offers simple access to these third-party services, which can be easily integrated into the Snowflake platform. 

Understanding Data Quality in Snowflake

Snowflake excels at ingesting data and making it easily accessible to users. For that data to be usable, it must be of a reliably high quality, free from errors and inaccuracies. 

Data quality is typically monitored using six key metrics:

• Accuracy, or how many errors are present.
• Completeness, or how many empty fields are present.
• Consistency, or how similar data from different sources compare.
• Timeliness, or how recent the data is.
• Uniqueness, or whether any duplicates exist.
• Validity, or how well data conforms to established standards.

While Snowflake makes data easy to access and analyze, it doesn’t mean that the data is always accurate or in a practical format. Snowflake can ingest poor-quality data, and users can introduce errors into data as it moves through the pipeline. 

While Snowflake offers features to check and secure data, such as object tagging and a rudimentary data quality framework, these features do not guarantee clean data. Many users find Snowflake’s built-in data quality functionality time-intensive, difficult to use, not easily scalable, and inherently unreliable. 

To ensure reliable data quality in Snowflake, users must rely on third-party data quality monitoring tools, such as FirstEigen’s DataBuck. DataBuck uses artificial intelligence and machine learning technologies to generate data quality rules, monitor all incoming and existing data, and calculate an objective Data Trust Score for each data asset. It monitors all six essential data quality metrics automatically, with minimal human intervention.

Challenges in Snowflake Data Ingestion:

During Snowflake data ingestion, organizations often struggle with:

• Handling large volumes of data from various sources.
• Ensuring transformations don’t compromise data quality.
• Preventing delays that impact downstream operations.

Understanding Validation in Snowflake

Snowflake enables robust data sharing across an enterprise. Unfortunately, it’s just as easy to share bad data as it is to share good data. The ease of data sharing with Snowflake can inadvertently increase the risk of introducing low-quality data into a system. Therefore, validating all data in the system is necessary to reduce the risk of inaccurate data affecting operational decisions. 

Snowflake’s primary focus is data ingestion from multiple sources. The more data sources there are, the higher the risk of inaccurate data. Data can also be compromised as it moves through a system. We estimate that Snowflake users spend 20%-30% of their time identifying and fixing data issues. 

For this reason, Snowflake encourages users to employ third-party data validation tools. Most current data validation tools, however, are not easily scalable as they establish data quality rules one table at a time. Organizations should seek out Snowflake data validation tools with the following features:

• Artificial intelligence (AI) and machine learning (ML) to identify data fingerprints and detect data errors.
• In-situ solutions to validate data at the source without moving it to other locations.
• Autonomous functionality to validate data with minimal human interaction.
• Scalability at a level that matches that of the Snowflake platform.
• Serverless data validation, ideally using Snowflake’s built-in capability.
• Integration with the data pipeline.
• Open API integration with other systems.
• Detailed audit trail of validation results.
• Complete control by business stakeholders.

FirstEigen’s DataBuck includes all these features and easily integrates into the underlying Snowflake platform. It’s the ideal data quality validation tool for Snowflake.

Understanding Observability in Snowflake 

To gain maximum use of Snowflake, an organization must embrace data observability. Observability enables data managers to monitor system performance using data from all parts of the system. This requires deep visibility into both the data and system performance. 

Snowflake observability requires the constant monitoring of Snowflake’s health and performance. This enables users to generate insights into the performance of a Snowflake data warehouse, identify any issues, diagnose the root causes of those issues, and implement necessary fixes. Observability in Snowflake also enables organizations to optimize data queries, minimize resource consumption, and improve system performance. This leads to more efficient use of resources and reduced costs. 

There are many ways to achieve Snowflake observability. For small organizations, a simple BI dashboard can do the trick. For larger enterprises, more powerful third-party observability tools, such as FirstEigen’s DataBuck, are required.

How Can Data Observability Tools Help Scale Data Quality Management?

Data observability tools are essential for scaling and maintaining data quality management in today’s rapidly evolving data landscape. These solutions offer a comprehensive, automated approach to monitoring data pipelines, ensuring that any anomalies or issues are quickly identified and resolved.

End-to-End Monitoring

By providing end-to-end coverage, data observability platforms allow teams to continuously track data across its entire lifecycle. This ensures that any data quality issues are detected early, minimizing disruptions and maintaining reliable data flow.

Automation and Efficiency

Automated alerting and diagnostics significantly reduce the time spent on manual data checks, allowing teams to focus on strategic tasks rather than firefighting unforeseen data issues. This automation leads to faster problem resolution, thereby enhancing overall productivity.

Scalability

As organizations grow and data ecosystems become more complex, scalable data observability solutions adapt to expanding needs. These tools efficiently handle increasing data volumes and complexity without compromising on the quality of insights, thus supporting seamless scaling of data operations.

Integration with Existing Systems

Data observability solutions integrate smoothly with various data platforms, including open-source tools and bespoke solutions. This ensures that organizations can maintain consistent data quality across all systems, avoiding silos and inconsistencies.

In conclusion, by leveraging data observability tools, organizations can enhance data quality management, reduce operational burdens, and focus on deriving value from their data, fostering growth and innovation.

FirstEigen’s DataBuck: Enhancing Snowflake Data Quality

An increasing number of organizations are turning to FirstEigen’s DataBuck to ensure data quality in Snowflake and to maximize their Snowflake data usage. DataBuck autonomously detects data quality specific to each dataset’s context and generates a trust score for all data assets. This process saves Snowflake users 95% of the time spent on discovering, exploring, and writing data validation rules.

DataBuck can automatically trigger a data trust score whenever new data lands in a Snowflake table or can be scheduled to run at a specific time. AI and ML are used to automatically map and update data trust scores without any human intervention or complex integration efforts.

DataBuck works by scanning each data asset in the Snowflake platform and re-scanning when assets or refreshed. Scanning is done in-situ, so no data has to be moved. The system then autonomously creates data health metrics specific to each data asset and continually monitors these over time to detect unacceptable data risk and generate a data trust score. Users are automatically alerted if the data trust score falls below acceptable levels. 

If you use Snowflake in your organization, you need DataBuck to ensure your insights are coming from high-quality data. 

Contact FirstEigen today to learn more about data quality, validation, and observability for Snowflake.

Check out these articles on Data Trustability, Observability & Data Quality Management-

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• 12 Things You Can Do to Improve Data Quality
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