data-engineering-guide

Designing a data pipeline for a sales data warehouse requires a structured approach from initial brainstorming to final implementation, covering pipeline design, schema creation, and data quality checks to ensure robust data flow.

Structuring a star schema for data models involves creating central fact tables connected to surrounding dimension tables, which simplifies queries and improves performance for analytical workloads within your data warehouse.

Adding data quality checks to an ETL pipeline involves implementing validation rules and reviews at various stages of data ingestion and transformation to ensure accuracy, completeness, and consistency before loading.

Yes, applying this guidance requires existing knowledge of various data engineering concepts and tools, as it provides expert direction for pipeline design, schema creation, and data quality implementation rather than foundational teaching.

The difference between ETL and ELT pipeline design lies in where data transformation occurs: ETL transforms data before loading into the target, while ELT loads raw data first and transforms it within the target system using its compute resources.