The conventional flow is Extract, Transform, and Load (ETL). Data is extracted from source systems, placed in a staging environment where required transformations are performed, and then loaded into the target Data Warehouse. Consequently, the transformed staging data is made ready and loaded into the Data Warehouse. DAMA-oriented training material presents this exact progression.

The staging area provides a controlled environment in which source data can be integrated without imposing transformation workloads directly on operational systems. Typical activities include datatype conversion, standardization, code translation, matching, deduplication, validation, derivation, consolidation, and handling rejected records.

From a Data Quality perspective, staging is especially important because many quality controls can be applied before data reaches the analytical repository. Profiling can identify unexpected distributions, invalid values, missing records, duplicate entities, referential-integrity defects, and inconsistent reference values. Failed records can then be quarantined or remediated according to governed rules.

Although metrics and profiling may occur in staging, they are supporting activities rather than the final destination in the ETL sequence. Likewise, metadata repositories document the transformation rather than serving as the main target for the transformed business data.

Reference Topics: DAMA-DMBOK2 — Data Warehousing and Business Intelligence; ETL; Staging Areas; Data Integration; Chapter 13 — Profiling, Cleansing, Standardization and Validation.

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