Data Engineering Best Practices: Designing Scalable ETL/ELT Pipelines

Robust data pipelines are the foundation of all BI dashboards and machine learning models. Implementing coding best practices in your data pipelines is essential to prevent system failures and lower warehouse computing bills.

1. Adopt ELT Over ETL

Load raw data into your cloud warehouse first, then use compute nodes to transform it. This simplifies pipelines, preserves historical data, and leverages your cloud warehouse's massive parallel processing capabilities.

2. Use Version Control and CI/CD

Treat SQL queries and database schemas as application code. Write pipeline definitions in repositories, run automated tests on commits, and deploy changes through structured pipelines.

3. Implement Data Quality Testing

Deploy tools like Great Expectations or dbt tests to actively check input schemas, null values, and unique constraints, preventing dirty data from corrupting downstream reporting models.