The next step is to transform all these data into a single format of storage. These data are then cleaned up, to avoid repeating or junk data from its current storage units. As a preliminary process, before the data is loaded into the repository, all the data relevant and required are identified from several sources of the system. ETL stands for Extract, Transform and Load.
Data Repository is the storage space for the data extracted from various data sources, which undergoes a series of activities as a part of the ETL process. The Bottom Tier in the three-tier architecture of a data warehouse consists of the Data Repository. Here is a pictorial representation for the Three-Tier Data Warehouse Architecture 1.
Hadoop, Data Science, Statistics & othersĮach Tier can have different components based on the prerequisites presented by the decision-makers of the project but are subject to the novelty of their respective tier. The three different tiers here are termed as: Three-tier Data Warehouse Architecture is the commonly used choice, due to its detailing in the structure.
The type of Architecture is chosen based on the requirement provided by the project team. The Three-Tier Data Warehouse Architecture is the commonly used Data Warehouse design in order to build a Data Warehouse by including the required Data Warehouse Schema Model, the required OLAP server type, and the required front-end tools for Reporting or Analysis purposes, which as the name suggests contains three tiers such as Top tier, Bottom Tier and the Middle Tier that are procedurally linked with one another from Bottom tier(data sources) through Middle tier(OLAP servers) to the Top tier(Front-end tools).ĭata Warehouse Architecture is the design based on which a Data Warehouse is built, to accommodate the desired type of Data Warehouse Schema, user interface application and database management system, for data organization and repository structure.