ETL / ELT

ETL, standing for Extract, Transform, Load, is a traditional data integration process. In this model, data is first extracted from various source systems (like an ERP or CRM), then transformed into a structured, analysis-ready format on a separate processing server, and finally loaded into the target data warehouse or planning platform. This pre-loading transformation ensures that the data in the destination is already cleaned, standardized, and aggregated for specific reporting or analytical needs.

ELT, or Extract, Load, Transform, is a more modern approach that leverages the power of cloud-based data warehouses. Data is extracted from source systems and immediately loaded in its raw form into a data warehouse or data lake. The transformation logic is applied as needed within the target system, offering greater flexibility and speed for handling large volumes of diverse data types. This method is highly effective for creating a single source of truth that can serve multiple analytical purposes, from ad-hoc queries to complex modeling.

The choice between ETL and ELT depends on the data architecture, volume, and the specific use case. While ETL is well-suited for structured workflows with predefined requirements, ELT provides the agility and scalability needed for modern business intelligence and dynamic planning environments that require access to raw, granular data.