No one can underestimate the value of reliability and integrity of data in the present data-driven world. Bad data can lead to poor decisions, flawed insights, and even considerable financial losses. Snowflake, as you know, is a robust cloud data platform. It provides an excellent foundation for data warehousing. However, it is your responsibility to ensure that the data that flows into and resides in Snowflake is of high quality.

You cannot underestimate the importance of data verification before loading it into Snowflake. Thankfully, you can now automate data quality checks. When the data quality check process is automated, you can maintain the integrity of the data. Also, when you do this, you can foster trust in your analytics. You can understand the different techniques available for Snowflake data validation from this blog post.

Why Automate Data Quality Checks?

You cannot underestimate the time it takes to manually check every data that gets into Snowflake. Particularly, when the volume of data grows, the pipeline will become more complex. The sheer scale of operation and human errors can make manual checks ineffective and impractical as well. When you automate data checks in Snowflake, you can get the following benefits:

Early detection of issues

When you automate data quality checks, you can catch issues with the quality of data at the source or as soon as they get into the system. In turn, you can prevent the propagation of issues downstream.

Reduced Manual Efforts

Manually entering data can be a repetitive task for your analysts and data engineers. You can relieve them of this tedious task by automating the process. As they are relieved of this time, they can focus more on strategic initiatives.

Better Data Trust

With automated and consistent validation, you can build confidence in your data assets. This will happen all through your organization, thereby helping with better decision-making.

Compliance and Governance

With automated data checks, you can enforce data governance to meet regulatory compliance requirements and policies.

Pillars of Automated Data Quality in Snowflake

In Snowflake, automation of data quality checks involves a combination of built-in features. Also, it involves the usage of external tools and SQL scripting. You can gain insights into the core components here:

Built-in Features for Data Validation in Snowflake

The good thing about Snowflake is that the platform offers many native capabilities. They serve as foundational components for Snowflake data validation:

Constraints

Snowflake does not enforce all constraints in its traditional transactional database. However, it enforces not-null constraints to ensure the completeness of data. Examples of traditional transactional databases include Unique, Key, and Primary. These are not enforced for performance reasons in a distributed environment.

• Not Null is your first line of defense. Let us consider that you declare a column as not null. When you do this, you can prevent null values from being inserted into the database. In turn, you can ensure that crucial data points are present ever.
• Unique and Primary key when declared offers valuable metadata for your query organizers and data consumers. They signal intent about the uniqueness of rows. If you feel it to be critical, you have the option to enforce uniqueness programmatically.
• Foreign Key is yet another in-built component. When you implement it, you can document relationships between tables. However, it will not prevent insertions of values that do not match. Here, you will have to manage referential integrity through other means. For instance, ETL will help you process or separate validation steps.

Data Types

To ensure the quality of data, it is crucial to use appropriate data types. In fact, this is a basic thing you will have to do. The robust type system of Snowflake ensures that your data conforms to the formats you expect. When you mistakenly choose incorrect data types, you should face unexpected behavior, errors, and implicit conversions. For instance, check that the numbers are stored in the Float or Number data type. Similarly, dates are to be stored in the Timestamp or Date data type. The texts that enter should go to the Varchar Data type.

Clones for Testing

With the cloning feature of Snowflake, you can create zero-copy instant clones of schemas, tables, or even whole databases. However, this element is not valuable for transformation without impacting production data and testing data quality rules. You can carry out validation checks on a cloned atmosphere. Then, you can apply the refined rules or fix it in a cloned environment. Thereafter, you can apply the refined rules or fixes to your main pipeline.

SQL-based Data Validation Rules

SQL, as you might be aware, is the dialect of data professionals. It is known for its power in defining and executing custom data quality checks within Snowflake. This is where you begin to automate data checks in Snowflake:

Fundamental Validation Checks

• Uniqueness checks will let you spot duplicate records based on a unique identifier or primary key.
• Completeness Check beyond not null will let you spot columns with a higher percentage of nulls.
• Conformity Checks will let you use regular expressions for validating data against particular patterns like zip codes, phone numbers, or email addresses.
• Consistency Checks will help ensure that the relationships between tables or columns are not only consistent but also logical.
• Range Checks will help you check whether data values or numeric values fall within an expected range.

Creating Data Quality Tables/Views

In Snowflake, you can create dedicated tables or views that house data quality issues and metrics. With this move, you can monitor the quality of data over time and develop dashboards.

• Error Logging Table: This is a table where data quality failures are logged
• Data Quality Dashboard View: This view will summarize the status of your data quality checks

Stored Procedures and Tasks for automation

This is where the “automation” aspect of Snowflake shines. The Stored Procedures facility of Snowflake can store complex data quality logic. Similarly, Tasks can schedule these procedures to run at particular intervals.

External Tools & Integrations

Now, you know about the built-in tools to automate data checks in Snowflake. However, the integration of external tools can further improve your strategy to automate data checks.

Here are a few things to know about the external tools and integrations:

Data Observatory Platforms

You can rely on tools like Acceldata, Datafold, and Monte Carlo that specialize in data observability. They provide automated monitoring, lineage tracking, and anomaly detection. You can get a comprehensive view of the health of your data by integrating these tools into your Snowflake platform.

ETL/ELT Tools

Modern ETL/ELT tools like Informatica, Talend, DBT, and Fivetran come with built-in data quality features. As part of your transformation pipelines of data ingestion, you can define validation rules on these tools. In turn, you can make sure that your data is clean before it lands in the final Snowflake tables.

For instance, DBT or data build tool is known for its Snowflake Data Validation capabilities. With this tool, you can define tests straight into your data models. These tests run as part of your dbt build process. If the tool detects quality issues with the data, it will stop. Also, it will provide clear insights into what went wrong.

Custom Scripting

For complex data quality checks that are highly customized, you can use orchestration tools like Apache Airflow and scripting languages like Python:

• Apache Airflow will let you orchestrate your Snowflake data quality tasks. The directed acrylic graphs of Airflow can define sequences of operations like alerts, quality checks, and data loading. With these details, you can get sturdy monitoring and scheduling capabilities.
• With Python, you can write Python scripts that connect to Snowflake. You can do this with Snowflake_connector_Python. Also, with this integration, you can execute data quality queries. Also, you can integrate with notification systems like Slack and email.

How to Implement A Robust Data Quality Framework?

To effectively automate data checks in Snowflake, you can consider the following framework:

Define Data Quality Dimensions

Start by clearly identifying what quality means for your data. Here are some common dimensions you can follow:

• Completeness
• Validity
• Accuracy
• Consistency
• Uniqueness
• Timeliness

Establish Data Quality Rules

Once you define the dimensions, translate them into particular testable rules. Make sure to thoroughly document these rules.

Select the Right Tools

Select the right combination of native features of Snowflake, dbt, SQL, and other external tools that suits your needs best. Also, ensure that you choose the tools that suit your existing ecosystem.

Implement and Automate

Write the required external scripts, stored procedures, dbt tests, and SQL scripts. Schedule them with the help of Airflow, Snowflake tasks, or the orchestrator you selected.

Monitor and Alert

With the help of external BI tools, custom DQ Tables, or the Information Schema of Snowflake, set up monitoring dashboards. This will help you track data quality metrics. Also, do not forget to configure alerts to notify appropriate teams immediately upon the detection of data quality issues.

Remediate and Iterate

Have a clear process not only for investigating but also for resolving data quality issues. To improve automation and to refine your rules, you should learn from every incident.

Conclusion

Automating data checks in Snowflake is not just a nice-to-have facility. It is turning out to be a crucial element to achieve a sturdy data strategy. With the help of third-party tools and also built-in tools of Snowflake, you can establish an efficient and proactive framework for Snowflake Data Validation. This investment in data quality will pay dividends to your organization by building reliability in your data. So, begin your automation journey today!