A fundamental practice in data warehousing involves establishing a dedicated area for initial data transformation. This process typically entails copying raw data from source systems and applying basic cleaning and standardization steps before further modeling. One common implementation of this area utilizes a tool designed for data build tool (dbt) and is often referred to as the ‘staging’ area. For example, source data from a CRM system might be extracted, loaded into a data warehouse, and then moved to a designated staging schema where column names are standardized and data types are enforced.
The existence of such a preliminary transformation layer offers several advantages. Primarily, it decouples raw data from downstream transformations, ensuring that the raw data remains untouched and available for auditing purposes. This segregation improves data governance and enhances trust in the data pipeline. Furthermore, establishing this initial processing step simplifies debugging and troubleshooting as any data quality issues are readily identifiable at this early stage. Historically, this approach evolved to address complexities associated with integrating data from diverse sources with varying data quality and formatting.
