Refactoring Analytics Models

Sooner or later, every data professional faces the need to refactor analytical models. For example, you might need to migrate SQL models from Airflow to dbt, or overhaul a dbt project for scalability and best practices. While every refactoring project is unique, there are some common rules that can help guide the process. Here are 6 guidelines to help you succeed.

🔹 Rule 1. Don't break production analytics
Make sure that production reports and systems are not affected. Refactoring is the full responsibility of engineers and analysts.

🔹 Rule 2. Define new rules
Define conventions that satisfy your team, like modeling layers, naming rules, etc.

🔹 Rule 3. Inspect existing models
It helps in creating a refactoring plan:
- Some models may be easy to migrate (loosely coupled)
- Some models will require additional work

🔹 Rule 4. Start from the end
Knowing the end goal will help you decide which data sources and intermediates are required for the final table.

🔹 Rule 5. Proceed in small chunks
Proceed in small increments. Deliver small, yet complete, changes that are easy to review and deploy.

🔹 Rule 6. Beginning is hard, but it gets easier
At the start of the migration, you will face a lot of work: every new model will be implemented from scratch and require significant groundwork.
However, the more you do at the beginning, the easier it will be in the end.

#sql #dbt #dataanalytics