An Enterprise data warehouse (EDW) is a mass data storage unit used for a business or company (an enterprise). In the same way a regular data warehouse stores data, cleanses it, and transforms it, so does an Enterprise warehouse. The difference between an original data warehouse and an Enterprise warehouse is that the Enterprise storage serves as a central storage unit – whereas an original data warehouse stores data for only a certain portion of a business network.
Enterprise data warehouse architecture refers to the components of an enterprise data warehouse. The architecture of a home refers to the materials used to make it: wood, siding, roofing, glass windows, screen doors, steps, brick underpinning, etc. In the same way, data architecture refers to the elements that constitute the centralized data storage unit for a business or company. There are five necessary elements for an Enterprise architecture:
- Single version of data
- Multiple subject areas
- Normalized Design
- Mission-Critical Environment
- Scalability across several dimension
Businesses should have a single version of data. Most businesses have numerous operations that must run in order for the business to stay alive. To keep the information centralized and well-contained, there should only be one data storage unit for all of the company information. Next, a business should have multiple subject areas. A business cannot run only billing operations but no payment operations or an ordering operation but no shipping operation. Businesses should have clearly-defined operations, each distinct from one another, that all run at the same time. The running of several applications at once is what keeps the business at the top of its game.
Normalized design refers to the architecture of the Enterprise storage unit. Enterprise storage units are centralized and distinct from individual data warehouses. Data warehouses usually come with either a snowflake or star schema, while enterprise storage units do not. According to the Enterprise data warehouse definition, Enterprise storage units are more complex data networks than data warehouses; the last thing an Enterprise storage needs is a complex structure this will only add to the complexity of the system and make it harder for businesses to maintain their data storage. Instead, complex storage units such as Enterprise storage units should have a more simple structure, such as a normalized design.
A mission-critical environment is also a significant component of an EDW. A mission-critical environment has all the preventive security measures in place to keep the data network from being destroyed by a virus, hacking incident, etc. Three major types of preventive measures include (1) high availability features, (2) business continuance features (failover, disaster recovery), and (3) security features.
The last major component of an EDW is its scalability across several dimensions. Scalability refers to the freedom of computer users to have their questions answered. Scalability has often been defined by the amount of data a network has, but scalability also involves queries. Computer users ask questions online every day about life, how things work; some even ask questions about things that most individuals would not even think about. It is these “freedom queries” that computer applications must be ready to handle.
An enterprise data warehouse should be owned and operated by every business that seeks to handle day-to-day operations of various tasks. A multi-component business needs a multi-component data network.
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Snowflake is the only data warehouse built for the cloud for all your data & all your users. Learn more about our purpose-built SQL cloud data warehouse.
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IBM data warehouse offerings provide performance and flexibility to support structured and unstructured data for analytics workloads including machine learning.
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ZAP are creators of ZAP Data Hub, ELT data warehouse automation software optimized for Microsoft Dynamics, Sage and Power BI.
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Sap data warehousing solutions offer a complete foundation for managing all types of data – no matter the shape or size.
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biGENiUS for advanced big data and data warehouse automation accelerates the development, imroves the agility and reduces costs both in the implemetation and maintenance of analytical data management solutions.
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