The Data Warehousing Methodology is organized into the following phases:- Initiation : Evaluating Readiness and Opportunities.
- Analysis : Analysis and Requirements Determination.
- Design : Data Warehouse and Data Mart Models (Star Schema/Multidimensional Model)
- Design : Technical Architecture.
Data warehousing methodologies share a common set of tasks, including business requirements analysis, data design, architecture design, implementation, and deployment [4, 9]. analysis is used to elicit the business questions from the intended users of the data warehouse.
Data warehousing methodologies share a common set of tasks, including business requirements analysis, data design, architecture design, implementation, and deployment [ 4, 9 ]. For business requirements analysis, techniques such as interviews, brainstorming, and JAD sessions are used to elicit requirements.
Extract, transform, load (ETL) and extract, load, transform (ELT) are the two main approaches used to build a data warehouse system. The typical extract, transform, load (ETL)-based data warehouse [5] uses staging, data integration, and access layers to house its key functions.