ETLELTprotsesse

ETLELTprotsesse is a term used to describe the set of data integration practices that combine ETL (extract‑transform‑load) and ELT (extract‑load‑transform) workflows. It covers the methods by which data is moved from source systems to target storage and transformed to support analytics, reporting, and decision making.

In ETL, data is extracted from sources, transformed in an intermediate processing layer to enforce quality and schema, and loaded into the destination data warehouse or data store.

In ELT, data is first loaded into the target platform and transformed there, often using the computing power of the destination system. This approach suits modern data lakes and cloud warehouses where scalable processing is available and minimizes data movement.

Common components include data sources, a staging area, a transformation layer or scripts, the target repository, and governance mechanisms. Orchestration and scheduling tools coordinate extraction, transformation, and loading, while metadata and lineage tracking support data quality and compliance.

Key considerations for choosing between ETL and ELT include data volume and variety, latency requirements, the capabilities of the target platform, and governance needs. ETL may be preferred when data quality must be ensured before loading; ELT is often favored for large scale and flexible schema.

Benefits of ETLELTprotsesse include improved data consistency, faster data delivery, and better reuse of transformation logic across analytics scenarios. Challenges include managing schema evolution, monitoring data quality, and controlling costs in cloud environments.