Best practices for data transformation as pipeline complexity grows

Best practices for data transformation as pipeline complexity grows are crucial for efficiency, scalability, and data integrity. Starting with ingestion-level transformations is essential to improve data quality now to reduce complexity later. This includes data type validation, null value handling, deduplication, trimming and cleaning, schema validation, and referential integrity checks. As data volume grows and query complexity increases, materialized views can be used to pre-compute result sets stored for faster query performance. The golden rule for materialized views is to understand your query performance, and choosing dbt when quality and complexity becomes the focus is also crucial. dbt brings a more developer-like workflow to data transformation, enabling data analysts and engineers to transform data in their warehouses more effectively. Finally, using Airflow for the most complex workflows allows for managing dependencies between tasks and scheduling complex jobs, providing scalability for large-scale data processing.