There are different architectural approaches used to create a data warehouse. Inmon and Kimball approaches are the most used today.In addition, Data Vault is another modeling approach used. Basically, they all aim to store historical data in a data store in a relational manner. None of these approaches is better than the other, and each has its own pros and cons. Which method to proceed with is shaped according to the needs.
In the middle layer scructure, where all data is collected, the data is kept normalized. Then, separate datamart designs are made for each subject area (e.g., sales, order, collection, etc.) fed from this middle layer. In these datamarts, the data is kept denormalized. Here, with the ”single source of truth" principle, it is aimed to calculate the data once in normalized form and distribute them in denormalized tables.
A data warehouse is a place where datamarts are prepared as denormalized according to business needs, and then all of these datamarts are collected in the same place.
In this approach, the data is kept normalized again. It contains tables called Hub, Link, and Satellite, and the data filled into these tables are divided into transactional, dimensional, or relation tables.
Without a data warehouse, organizations cannot consolidate their data in a single environment and cannot associate their data in different environments with each other. In addition, since applications such as Excel allow data to be changed by users, a reliable environment does not occur.
Thanks to the data warehouse, companies have the opportunity to store their data from different source systems in a single center. Moreover, it is also possible to store all the data with the one true truth principle by associating the data from different sources with each other. Moreover, DWH also stores data changes in certain periods, which
At GTech, we have continued to respond to many different needs in many different sectors since 2000 with our expert staff.
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We are starting to work to find out if your organization is ready for the data warehouse. It is important to have an organization in place that includes business drivers, an appropriate project sponsor, and the use of data for decisions, and business and IT collaboration.
We define your goals together with you in order to design the most appropriate solution for your organization and business processes; we collect available data and evaluate formats in order to achieve the determined goals.
We are designing the data warehouse architecture to meet your business needs as a concept. At this stage, we are determining the tables and relations to be created (this process can be done as an ER Diagram, or can also be shown with a Bus Matrix.)
This is the section where we physically define all tables and their relations. In this section, we are preparing the project for the next stage by saving the tables that will be the source for the relevant tables and the filling forms of the data.
After storing the data according to the quantity and types using the tips in the previous steps, we fill the data into the tables with ETL/ELT.
We perform consistency checks between existing tables, create inter-layer test scenarios and perform positioning to the data warehouse related to the controls.
After observing how the data management solution we designed performs in the real environment and completing the final preparations, we are taking the project to live.
Esfa Grup, saves time with centralized and accurate data and can
process its data in near real time and take necessary actions quickly.
Renault, manages the after-sales performance measurements of its
authorized dealers in a centralized and automated structure with
visual reports that it monitors instantly.