ETLModell

ETLModell refers to a data integration approach that moves data from multiple source systems into a centralized target, typically a data warehouse or data mart. The model combines Extract, Transform, and Load steps to prepare data for analytics, reporting, and business intelligence. It is characterized by a clear separation of data extraction, data transformation, and data loading tasks, often supported by a staging area and metadata governance.

Key components of an ETLModell include a source layer, a staging area for temporary storage, transformation logic, and a target data store. Metadata repositories document source definitions, transformation rules, lineage, and performance metrics. Orchestration and scheduling mechanisms coordinate the execution of ETL jobs, while error handling and logging support reliability and traceability.

The typical workflow involves identifying source systems, extracting relevant data, cleansing and standardizing values, applying business rules, enriching data, handling deduplication and slowly changing dimensions, and finally loading into the target schema. Transformations can range from simple field mappings to complex aggregations, data type conversions, and surrogate key generation.

Architectures associated with ETLModell often include a staging area, an enterprise data warehouse, and data marts. While traditional ETL uses batch processing, modern implementations may incorporate real-time or near-real time data integration and, in some variants, the ELT approach where loading occurs first and transformations are done inside the target system.

Advantages of ETLModell include centralized data governance, consistency across analytics, and the ability to enforce data quality. Challenges involve maintaining transformation logic, handling schema evolution, ensuring performance, and managing security and data lineage. Common tools implement ETL processes in commercial and open-source ecosystems.