Labelling Locally Maintained Tables In Snowflake A Comprehensive Guide For Data Governance

Hey guys! Today, we're diving deep into a crucial aspect of data governance within Snowflake: labelling locally maintained tables. This is super important, especially when you're dealing with a data lake environment where data from various sources, including internally maintained datasets and ISL-produced datasets, all converge. Let's break down why this matters and how we can implement a robust solution.

The Challenge: Distinguishing Data Sources

In a data lake like DATA_LAKE, the sheer volume and variety of data can be overwhelming. When you have datasets originating from different sources, such as local teams and centralized ISL (Information Services Library) processes, it becomes essential to distinguish between these data sources. This distinction is critical for several reasons, most notably data quality (DQ) issue escalation. Think about it: if you encounter a data quality problem, you need to know who to contact and how to escalate the issue effectively. Without clear labelling, you're essentially wandering in the dark, trying to figure out who's responsible for which data. You'll want to ensure clear lines of responsibility which is paramount for maintaining data integrity and trust within your organization. By understanding the origin of the data, you can quickly identify the appropriate team or individual to address any concerns. This reduces resolution time and minimizes the impact of data quality issues on downstream processes and analytics.

Moreover, labelling helps in understanding the data lineage. Knowing the source of the data allows you to trace its journey from creation to consumption. This is invaluable for auditing purposes, compliance requirements, and even for debugging complex data transformations. Labelling also facilitates better data governance. It enables you to apply different policies and controls based on the source of the data. For instance, you might have stricter quality checks for locally maintained tables compared to ISL-produced datasets. Proper labelling lays the foundation for a well-governed data environment.

The Solution: Snowflake Object Tagging

So, how do we tackle this challenge? One effective solution discussed was leveraging Snowflake's object tagging feature. Object tagging allows you to attach metadata to various Snowflake objects, including tables, views, and even columns. This metadata can then be used for filtering, access control, and, in our case, identifying locally maintained tables.

Implementing Object Tagging

One straightforward implementation involves using the ALTER TABLE statement. Imagine you want to mark a table named DATA_LAKE.X.Y as locally maintained. You could execute the following SQL command:

ALTER TABLE DATA_LAKE.X.Y SET TAG NCL_LOCAL = 'True';

This command essentially adds a tag named NCL_LOCAL to the table and sets its value to True. The beauty of this approach is its simplicity. The ALTER TABLE statement is executed ad-hoc, meaning it only needs to be done once per table after it's created. This minimizes the overhead and ensures that your labelling process is efficient.

Going a Step Further: Identifying Responsible Contacts

But why stop there? We can enhance our labelling strategy by including information about the contact person responsible for the table. This takes our data governance to the next level by providing a clear point of contact for any queries or issues related to that table. For example, let's say Jake Kealey is responsible for the DATA_LAKE.CANCER__SCREENING.SCREENING_LOCAL table. We could add another tag like this:

ALTER TABLE DATA_LAKE.CANCER__SCREENING.SCREENING_LOCAL SET TAG CONTACT = 'jake.kealey@nhs.net';

Now, anyone looking at the table's metadata can easily identify Jake as the go-to person for questions or concerns. This direct line of communication streamlines issue resolution and fosters a sense of accountability.

Benefits of Detailed Tagging

The benefits of this detailed tagging approach are manifold. First, it improves communication and collaboration between teams. When everyone knows who's responsible for a particular table, it's easier to ask questions, report issues, and coordinate efforts. Second, it enhances data discoverability. Tags act as keywords that help users find the data they need more quickly. For instance, someone looking for locally maintained screening data can easily filter tables based on the NCL_LOCAL and CONTACT tags. Third, it strengthens data governance. By tagging tables with relevant metadata, you create a more structured and organized data environment. This makes it easier to enforce data quality policies, manage access control, and comply with regulatory requirements.

Exploring Alternative Solutions: Snowflake Schema Properties

While object tagging is a powerful tool, it's worth exploring alternative solutions within Snowflake. One intriguing option is to leverage Snowflake's ability to assign contact information to schemas. Schemas, as you know, are logical groupings of database objects like tables and views. By associating contact information with a schema, you can effectively apply that information to all the objects within that schema. This can be a more efficient approach if you have multiple tables maintained by the same team or individual.

Assigning Contact Information to Schemas

Snowflake's interface provides a straightforward way to assign contact information to schemas. You can typically find this option within the schema's properties or settings. The specific steps may vary slightly depending on your Snowflake environment, but the general idea is the same: you navigate to the schema's configuration and add the contact details, such as an email address or a team alias. As shown on the image above, you can add contact information directly to the schema properties. This is particularly useful if all tables within a schema are managed by the same team or individual.

Benefits of Schema-Level Contact Information

Using schema-level contact information offers several advantages. First, it reduces redundancy. Instead of tagging each table individually, you only need to set the contact information at the schema level. This simplifies the maintenance process and reduces the risk of inconsistencies. Second, it promotes consistency. By associating contact information with the schema, you ensure that all tables within that schema inherit the same contact details. This creates a uniform approach to data governance and makes it easier for users to find the right point of contact. Third, it streamlines administration. Managing contact information at the schema level is generally easier than managing it at the table level, especially when you have a large number of tables.

Choosing the Right Approach: Tags vs. Schema Properties

So, which approach is better: object tagging or schema properties? The answer, as often is the case, depends on your specific needs and circumstances. If you need granular control over contact information and want to assign different contacts to different tables within the same schema, object tagging is the way to go. It provides the flexibility to customize metadata at the individual object level. On the other hand, if you have a consistent team responsible for all tables within a schema, using schema properties is a more efficient and streamlined approach. It reduces redundancy and promotes consistency.

In practice, you might even consider combining both approaches. For instance, you could use schema properties to assign a default contact for all tables within a schema and then use object tagging to override that default for specific tables that have a different responsible party. This hybrid approach allows you to leverage the strengths of both methods and create a data governance strategy that's tailored to your unique requirements.

Best Practices for Labelling

Regardless of the approach you choose, there are some best practices to keep in mind when labelling locally maintained tables in Snowflake. These practices will help you create a robust and sustainable data governance framework.

Consistency is Key

First and foremost, be consistent with your labelling. Use the same tagging conventions and naming standards across all your tables. This will make it easier for users to understand the meaning of the tags and find the data they need. For example, if you decide to use the NCL_LOCAL tag to identify locally maintained tables, stick with that tag consistently. Don't use different variations or synonyms, as this will only create confusion. Similarly, if you're using tags to identify responsible contacts, use a consistent format for email addresses or team aliases.

Document Your Labelling Strategy

Second, document your labelling strategy. Create a clear and concise guide that explains the purpose of each tag, its possible values, and how it should be used. This documentation should be readily accessible to all data users, so they can understand the meaning of the tags and use them effectively. Your documentation should cover the following aspects: Tag names and descriptions: Clearly define each tag and its purpose. Value conventions: Specify the allowed values for each tag (e.g., True or False for boolean tags, email address format for contact tags). Usage guidelines: Provide examples of how to use the tags in queries and data governance processes. Maintenance procedures: Outline how tags are added, updated, and removed.

Automate the Labelling Process

Third, consider automating the labelling process. Manually tagging tables can be time-consuming and error-prone, especially when you have a large number of tables. Automate the tagging process by integrating it into your data ingestion or table creation workflows. For example, you could create a script or a stored procedure that automatically adds the NCL_LOCAL tag to any table created in a specific schema. You can also leverage Snowflake's features like task scheduling to automate the execution of tagging scripts. This will not only save time but also ensure that all tables are consistently labelled.

Regularly Review and Update Tags

Fourth, regularly review and update your tags. Data environments are dynamic, and things change over time. Tables might be transferred to different teams, contact information might change, and new tags might be needed. Make it a habit to periodically review your tags and update them as needed. This will ensure that your labelling strategy remains relevant and effective. This review process should include: Identifying obsolete tags: Remove tags that are no longer relevant or used. Updating tag values: Correct any outdated information, such as contact details. Adding new tags: Introduce new tags to capture additional metadata requirements.

Educate Your Users

Finally, educate your users about the importance of labelling and how to use the tags effectively. Hold training sessions, create user guides, and provide ongoing support to ensure that everyone understands the labelling strategy and its benefits. User education is critical for the success of any data governance initiative. When users understand the purpose of labelling and how it benefits them, they are more likely to adopt the practices and contribute to a well-governed data environment.

Conclusion

Labelling locally maintained tables in Snowflake is a crucial step towards effective data governance. By distinguishing data sources and identifying responsible contacts, you can streamline issue resolution, improve data discoverability, and foster a culture of accountability. Whether you choose object tagging, schema properties, or a combination of both, remember to be consistent, document your strategy, automate the process, and educate your users. By following these best practices, you'll create a data environment that's well-organized, easily understood, and ready to support your business needs. So go ahead, guys, start tagging and take control of your data!