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Introducci贸n a BI y Data Warehouse
驴Qu茅 es BI y Data Warehousing?
Niveles de anal铆tica y jerarqu铆a del conocimiento
Conceptos de BI: Data Warehouse, Data Mart, Dimensiones y Hechos
Base de datos OLTP vs. OLAP
Metodolog铆as de Data Warehouse
Quiz: Introducci贸n a BI y Data Warehouse
Modelos dimensionales
Data Warehouse, Data Lake y Data Lakehouse: 驴Cu谩l utilizar?
Tipos de esquemas dimensionales
Dimensiones lentamente cambiantes
Dimensi贸n tipo 1
Dimensi贸n tipo 2
Dimensi贸n tipo 3
Tabla de hechos (fact)
Configuraci贸n de herramientas para Data Warehouse y ETL
Modelado dimensional: identificaci贸n de dimensiones y m茅tricas
Modelado dimensional: dise帽o de modelo
Quiz: Modelos dimensionales
ETL para inserci贸n en Data Warehouse
Documento de mapeo
Creaci贸n del modelo f铆sico
Extracci贸n: querys en SQL
Extracci贸n en Pentaho
Transformaci贸n: dimensi贸n de cliente
Carga: dimensi贸n de cliente
Soluciones ETL de las tablas de dimensiones y hechos
Par谩metros en ETL
Orquestar ETL en Pentaho: job
Revisi贸n de todo el ETL
Quiz: ETL para inserci贸n en Data Warehouse
Cierre
Reflexiones y cierre
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In the development of an ETL (Extract, Transform, Load) project, dimensions play a fundamental role. In this solution, advanced techniques are used for the efficient handling of dimensions through parallel execution and the use of specific transformations in order to avoid conflicts in the use of variables.
Parallel execution: A dummy is used to allow all transformations to be executed in parallel. However, to avoid overlaps and to ensure that variables are not transmitted incorrectly, a five-second waiting interval is introduced between executions.
Client transformation: Two approaches are presented: one creates a CSV from the data transformation and then loads it; the other, for small records, loads them directly to the database.
Direct uploads: For smaller data dimensions, such as products, territories and vendors, a direct upload to the database is used.
The handling of the fact table (FAC) is a critical process in the data architecture. Although it resembles the procedure used to load clients, there are important nuances that distinguish it.
Single load: Being a single load, neither a dummy nor a wait is required, since all actions are concentrated in a single table.
ID generation: The maximum ID is calculated to ensure the creation of consecutive IDs in the new records.
CSV export: Similar to client loading, a CSV is exported, which is then loaded using a copy command, thus optimizing performance.
After completing the data load, it is crucial to validate the results in the database to ensure that the records are accurate and reflect the established business logic.
Record lists: You can verify the accuracy of the data by querying the dimension tables, such as vendor or customer, and confirm that the IDs and other fields have been mapped correctly.
Slowly changing dimension behavior: Type 2 dimensions, such as salespeople, show expected behavior at the expiration of one record and the opening of another when updated.
Integration with graphical systems: Once validated, the complete data model can be integrated into visualization systems such as Power BI or Tableau for the generation of reports and dashboards that add value to the business.
Your ability to implement and optimize data loads can make all the difference in delivering accurate and useful information for decision making in your organization. Keep experimenting and sharing your innovative solutions in this area - the world of data awaits you with endless possibilities!
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