With evolving business requirements, the data schema is bound to change over time. However, unmanaged schema changes can cause significant disruptions for downstream data users. In this article, we’ll explore strategies, including version control, data testing, and proper communication, to manage schema changes and minimize their impact.
Schema Changes: The Implications
Take the hypothetical table freshers_in_university as an example. It contains details about new university students. If a new column program_duration is added, or if an existing column program changes its datatype or is removed, the queries and reports relying on the previous schema will malfunction, creating a ripple effect of disruptions. By using version control, implementing thorough data testing, and fostering clear communication, you can manage schema changes effectively, minimizing disruptions and maintaining the trust of your data users.
To avoid this, we must adopt strategies that not only accommodate the schema changes but also minimize the downstream impact.
Version Control: The First Line of Defense
Version control plays an indispensable role in managing schema changes. It allows you to track changes to your code, providing a history of modifications and making it possible to revert changes if something goes wrong.
In the context of our freshers_in_university table, suppose you’re considering adding the program_duration column. The addition is first done in a separate branch. If the change is successful and has no negative impacts, the branch is merged with the main codebase. If there are issues, the changes can be rolled back without affecting the main codebase.
# Create a new branch for the schema change
git checkout -b add-program-duration
# Make the schema change and commit it
git commit -am "Add program_duration to freshers_in_university"
# If everything is fine, merge the branch with the main codebase
git checkout main
git merge add-program-duration
# If there are issues, discard the changes
git branch -d add-program-duration
Data Testing: Ensuring Accuracy and Consistency
In dbt, data testing is another effective way to handle schema changes. Tests in dbt are SQL queries that return True or False. If the test returns False, dbt will throw an error.
You can create tests for schema changes to ensure that they don’t break anything. For instance, when you add the program_duration column to the freshers_in_university table, you could write a test to ensure that all program_duration values are positive integers.
version: 2
models:
- name: freshers_in_university
columns:
- name: program_duration
tests:
- not_null
- greater_than: 0
In the above YAML file, we’ve defined two tests for the program_duration column: not_null ensures that there are no null values, and greater_than: 0 ensures that all values are positive.
Effective Communication: Bridging the Gap
Finally, none of the above strategies would be effective without proper communication. The data team should clearly communicate schema changes to downstream data users, including what changes are being made, why they are necessary, and how they might affect data usage.
One way to communicate is by documenting your changes in the dbt documentation, including a description of the change and its impact. You can also directly inform downstream users via email or a dedicated communication platform.
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