Comparar métodos

	Almacenamiento de datos	Proceso ETL	Diseño de Cubos OLAP
Campo	Sistemas de información	Sistemas de información	Sistemas de información
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Año de origen	1992	1996	1993
Autor original	William H. Inmon and Ralph Kimball	Ralph Kimball and data warehouse pioneers	E. F. Codd and colleagues (Arbor Software)
Tipo	Data system architecture	Data integration methodology	Analytical data structure design
Fuente seminal	Inmon, W. H. (1992). Building the Data Warehouse. New York: QED Technical Publishing.	Kimball, R. (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. New York: John Wiley & Sons.	Codd, E. F., Codd, S. B., & Salley, C. T. (1993). Providing OLAP to user-analysts: An IT mandate. Arbor Software.
Alias	warehouse, DW design	ETL, data integration	OLAP, multidimensional cubes
Relacionados	2	1	1
Resumen	Data warehousing is an approach to designing integrated repositories of historical business data optimized for analysis and reporting. Pioneered by William Inmon and Ralph Kimball in the early 1990s, data warehouses consolidate data from diverse operational sources into a centralized, time-stamped, non-volatile store supporting complex queries across multiple dimensions.	The Extract-Transform-Load (ETL) process is a systematic approach to moving data from source systems into a target repository. Formalized in the context of data warehousing, ETL pipelines extract data from diverse operational sources, apply business rules and data quality checks, and load the results into data warehouses and analytical systems.	OLAP (Online Analytical Processing) cube design is the practice of structuring multidimensional data for interactive analysis. Formalized by Codd and colleagues in 1993, OLAP cubes organize facts (measurements) along multiple dimensions (attributes) enabling rapid pivoting, drilling, and aggregation for business analysis.