Best practices for data modeling with SQL and dbt

Analytics engineers often face challenges when rewriting SQL data models, such as slow updates, lack of comments, incorrect joins, and duplicated data. Data modeling organizes SQL code to make it usable, and dbt (Data Build Tool) aids in writing efficient, modular code that improves model performance and debuggability. Key practices include creating base models to reference raw data, using correct joins to minimize duplicates, and favoring common table expressions (CTEs) over subqueries for clarity. dbt also supports macros for reusable SQL logic, enhancing code efficiency and readability. By adhering to these best practices, analytics engineers can build fast, dependable, and scalable data models that avoid technical debt and support business growth.

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