What is the data warehouse architecture?

Data Warehouse architecture is a design that encapsulates all aspects of data storage for business environment. Data storage is to create a central domain for storing comprehensive decentralized business data in a logical unit that allows data mining, business intelligence and overall access to all relevant data within the organization. Data warehouse architecture includes all reporting requirements, data management, security requirements, bandwidth requirements and storage requirements. This proposal should be considered a blue print for business data architecture. In particular, several primary areas should develop when considering data warehouse architecture. These areas are access to the source system, the production process, the data enrichment process, data architecture, business intelligence for repository and storage.

Data storage requires transactionswith source data from a transaction or database of recording to a data warehouse. This process is simplified into the term transformation and load on the term (ETL), which essentially encapsulates the access area to the source system, enrich data and data architecture. For clarity, it is better to design these architectural areas in detail, which outlines how the ETL process will be reached. While some data are required from source systems, all data are not desirable because it would overload the corporate warehouse. The primary areas of concern when solving the source system layer are data access methodologies, data required from the source system and renewal requirements.

Another architectural layer of data stores is to consider the process of the production. Since most of the data from the source systems will be Quire investigating and the cleaning of data is important to create a landing zone for source data that will move before loading into a layer of data warehouse rules. Staging area maintains raw data feedFrom source systems that are usually stamped in time to ensure recent data.

Data enrichment process or business rules is where the data is cleaned to meet the required data warehouse outcome. A good example of this cleaning approach is the use of address cleaning tools; In the event that the source system has incorrect data, the data enrichment process launches the address from the raw data set to the trade rules system that would correct invalid addresses. This is also a time when inaccurate data is removed or modified to ensure completeness in the data warehouse.

Another layer to be considered is a layer of data architecture. This area is a place where a real design or scheme of a business data warehouse is completed. Data warehouse in non -bombination of all data files within the company, but instead it is a newly defined database created to allow an overview of all business entities within the company.

it requires data architeEktura answered questions containing business intelligence and data mining. By creating data architecture in this way, RAW data sets will be transformed into tables of facts that allow users to make ad-hoc reports of the entire Enterprise view rather than a specific database. This is also an area that will maintain metadata about data from the raw system that could include the name of the source system or the primary keys.

Another area to be considered is the requirements of business intelligence and reporting. This layer can be considered as a user -focusing requirement for data storage. This area usually contains canned messages, the ability to report ad-hoc and board, or alert the corporate dash. Business Intelligence Layers usually gains the most consideration because it is the only component heading out in the data warehouse.

The last layer for assessment is the overall data storage and maintenance requirements. Giventhat the data warehouse is constantly growing and expanding, the storage of the user base must be strictly managed and maintained. In addition, when creating a data warehouse architecture, the design should make realistic estimates of what will be required, from the storage capacity, as well as a belt of data access capacity. These requirements will be critical because the data warehouse is widely used throughout the company.